diff --git a/404.html b/404.html
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diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 015a88d11d..964c6d4153 100644
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@@ -1,8 +1,5 @@
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 <div id="contributing-to-torch" class="section level1">
@@ -140,10 +122,14 @@ <h3 id="workflow">Workflow<a class="anchor" aria-label="anchor" href="#workflow"
 <p>Finally, it’s important to update the documentation. Please always use the custom <code>tools/document.R</code> script instead of <code>devtools::document()</code>, as we need to patch <code>roxygen2</code> to avoid running the examples on CRAN.</p>
 <p>We’re looking forward to your contributions!</p>
 </div>
+<div class="section level3">
+<h3 id="known-issues">Known Issues<a class="anchor" aria-label="anchor" href="#known-issues"></a></h3>
+<ul><li>When running the tests twice (e.g. via <code>devtools::test()</code>) this will cause a segfault, see <a href="https://github.com/mlverse/torch/issues/1218" class="external-link">#1218</a> for a discussion.</li>
+</ul></div>
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@@ -152,14 +138,14 @@ <h3 id="workflow">Workflow<a class="anchor" aria-label="anchor" href="#workflow"
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+<ul class="navbar-nav">
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-    
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 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Automatic Mixed Precision</h1>
+
+      <h1>Automatic Mixed Precision</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/articles/amp.Rmd" class="external-link"><code>vignettes/articles/amp.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/articles/amp.Rmd" class="external-link"><code>vignettes/articles/amp.Rmd</code></a></small>
       <div class="d-none name"><code>amp.Rmd</code></div>
     </div>
 
@@ -128,7 +113,7 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 <ol style="list-style-type: decimal">
 <li>Allow the model to use different implementations of operations so it
 can use half-precision.</li>
-<li>Using loss scaling to preserver small gradient values that might be
+<li>Using loss scaling to preserve small gradient values that might be
 too small due to using half-precision.</li>
 </ol>
 <p>The first step, can also be used to speedup inference pipelines.</p>
@@ -262,7 +247,7 @@ <h2 id="benchmark">Benchmark<a class="anchor" aria-label="anchor" href="#benchma
 <span><span class="co">#&gt;    user  system elapsed </span></span>
 <span><span class="co">#&gt;   3.434   0.114   3.550</span></span></code></pre></div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -273,15 +258,15 @@ <h2 id="benchmark">Benchmark<a class="anchor" aria-label="anchor" href="#benchma
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/articles/distributions.html b/articles/distributions.html
index fbdac5cf42..17f2e8b7ec 100644
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+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Distributions</h1>
+
+      <h1>Distributions</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/distributions.Rmd" class="external-link"><code>vignettes/distributions.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/distributions.Rmd" class="external-link"><code>vignettes/distributions.Rmd</code></a></small>
       <div class="d-none name"><code>distributions.Rmd</code></div>
     </div>
 
@@ -151,12 +136,11 @@ <h2 id="basic-univariate-distributions">Basic univariate distributions<a class="
 <code class="sourceCode R"><span><span class="va">n</span><span class="op">$</span><span class="fu">log_prob</span><span class="op">(</span><span class="fl">0</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; -0.9189</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">dnorm</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="co"># equivalent R code</span></span>
+<span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">dnorm</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="co"># equivalent R code</span></span>
 <span><span class="co">#&gt; [1] -0.9189385</span></span></code></pre></div>
 <p>or, evaluate multiple log probabilities:</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">n</span><span class="op">$</span><span class="fu">log_prob</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">2</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; -0.9189</span></span>
@@ -168,14 +152,14 @@ <h2 id="basic-univariate-distributions">Basic univariate distributions<a class="
 <h2 id="multiple-distributions">Multiple distributions<a class="anchor" aria-label="anchor" href="#multiple-distributions"></a>
 </h2>
 <p>A distribution can take a tensor as it’s parameters:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/distr_bernoulli.html">distr_bernoulli</a></span><span class="op">(</span>probs <span class="op">=</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.5</span>, <span class="fl">0.75</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">b</span></span>
 <span><span class="co">#&gt; torch_Bernoulli ()</span></span></code></pre></div>
 <p>This object represents 3 independent Bernoulli distributions, one for
 each element of the tensor.</p>
 <p>We can sample a single observation:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">b</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  0</span></span>
@@ -183,7 +167,7 @@ <h2 id="multiple-distributions">Multiple distributions<a class="anchor" aria-lab
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{3} ]</span></span></code></pre></div>
 <p>or, a batch of <code>n</code> observations:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">b</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="fl">6</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  0  0  1</span></span>
@@ -202,15 +186,15 @@ <h2 id="using-distributions-within-models">Using distributions within models<a c
 torch.</p>
 <p>Let’s implement a Gaussian linear model, but first let’s simulate
 some data</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">1</span><span class="op">)</span></span>
 <span><span class="va">y</span> <span class="op">&lt;-</span> <span class="fl">2</span><span class="op">*</span><span class="va">x</span> <span class="op">+</span> <span class="fl">1</span> <span class="op">+</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
 <p>and plot:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="distributions_files/figure-html/unnamed-chunk-11-1.png" width="700"></p>
 <p>We can now define our model:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">GaussianLinear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span><span class="op">(</span></span>
 <span>  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span>
 <span>    <span class="co"># this linear predictor will estimate the mean of the normal distribution</span></span>
@@ -228,7 +212,7 @@ <h2 id="using-distributions-within-models">Using distributions within models<a c
 <span></span>
 <span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">GaussianLinear</span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p>We can now train our model with:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">opt</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
 <span></span>
 <span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span>
@@ -251,7 +235,7 @@ <h2 id="using-distributions-within-models">Using distributions within models<a c
 <span><span class="co">#&gt; iter:  90  loss:  1.463971 </span></span>
 <span><span class="co">#&gt; iter:  100  loss:  1.463966</span></span></code></pre></div>
 <p>We can see the parameter estimates with:</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span></span>
 <span><span class="co">#&gt; $linear.weight</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
@@ -268,7 +252,7 @@ <h2 id="using-distributions-within-models">Using distributions within models<a c
 <span><span class="co">#&gt;  1.0461</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{1} ][ requires_grad = TRUE ]</span></span></code></pre></div>
 <p>and quickly compare with the <code><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm()</a></code> function:</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/glm.html" class="external-link">glm</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Call:</span></span>
@@ -289,7 +273,7 @@ <h2 id="using-distributions-within-models">Using distributions within models<a c
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Number of Fisher Scoring iterations: 2</span></span></code></pre></div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -300,15 +284,15 @@ <h2 id="using-distributions-within-models">Using distributions within models<a c
 </div>
 
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index ee640fa12e..0706e2ade9 100644
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+<script src="../../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
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@@ -27,15 +22,15 @@
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     <a class="navbar-brand me-2" href="../../index.html">torch</a>
 
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/examples/basic-autograd.Rmd" class="external-link"><code>vignettes/examples/basic-autograd.Rmd</code></a></small>
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>basic-nn-module</h1>
+
+      <h1>basic-nn-module</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/examples/basic-nn-module.Rmd" class="external-link"><code>vignettes/examples/basic-nn-module.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/examples/basic-nn-module.Rmd" class="external-link"><code>vignettes/examples/basic-nn-module.Rmd</code></a></small>
       <div class="d-none name"><code>basic-nn-module.Rmd</code></div>
     </div>
 
@@ -147,9 +132,9 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 <span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span></span></code></pre></div>
 <pre><code><span><span class="co">## $w</span></span>
 <span><span class="co">## torch_tensor</span></span>
-<span><span class="co">## -0.0834</span></span>
-<span><span class="co">##  0.4667</span></span>
-<span><span class="co">##  0.8820</span></span>
+<span><span class="co">##  1.1848</span></span>
+<span><span class="co">## -0.3247</span></span>
+<span><span class="co">## -0.8387</span></span>
 <span><span class="co">## [ CPUFloatType{3,1} ][ requires_grad = TRUE ]</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## $b</span></span>
@@ -160,9 +145,9 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 <code class="sourceCode R"><span><span class="co"># or individually</span></span>
 <span><span class="va">model</span><span class="op">$</span><span class="va">w</span></span></code></pre></div>
 <pre><code><span><span class="co">## torch_tensor</span></span>
-<span><span class="co">## -0.0834</span></span>
-<span><span class="co">##  0.4667</span></span>
-<span><span class="co">##  0.8820</span></span>
+<span><span class="co">##  1.1848</span></span>
+<span><span class="co">## -0.3247</span></span>
+<span><span class="co">## -0.8387</span></span>
 <span><span class="co">## [ CPUFloatType{3,1} ][ requires_grad = TRUE ]</span></span></code></pre>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span><span class="op">$</span><span class="va">b</span></span></code></pre></div>
@@ -177,16 +162,16 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">y_pred</span></span></code></pre></div>
 <pre><code><span><span class="co">## torch_tensor</span></span>
-<span><span class="co">## -1.8847</span></span>
-<span><span class="co">## -2.0797</span></span>
-<span><span class="co">## -0.3219</span></span>
-<span><span class="co">##  0.9337</span></span>
-<span><span class="co">## -1.1361</span></span>
-<span><span class="co">## -0.2247</span></span>
-<span><span class="co">##  0.0424</span></span>
-<span><span class="co">## -0.8541</span></span>
-<span><span class="co">## -0.8275</span></span>
-<span><span class="co">##  1.7312</span></span>
+<span><span class="co">## -0.0645</span></span>
+<span><span class="co">## -0.8744</span></span>
+<span><span class="co">## -0.7154</span></span>
+<span><span class="co">## -0.9848</span></span>
+<span><span class="co">##  0.8774</span></span>
+<span><span class="co">## -0.3581</span></span>
+<span><span class="co">## -2.6608</span></span>
+<span><span class="co">## -2.3989</span></span>
+<span><span class="co">## -0.3657</span></span>
+<span><span class="co">## -1.1467</span></span>
 <span><span class="co">## [ CPUFloatType{10,1} ][ grad_fn = &lt;AddBackward0&gt; ]</span></span></code></pre>
   </main>
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@@ -198,15 +183,15 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 </div>
 
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@@ -27,15 +22,15 @@
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>dataset</h1>
+
+      <h1>dataset</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/examples/dataset.Rmd" class="external-link"><code>vignettes/examples/dataset.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/examples/dataset.Rmd" class="external-link"><code>vignettes/examples/dataset.Rmd</code></a></small>
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 <span></span>
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+<span><span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span><span class="op">(</span><span class="va">batch</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span></span>
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@@ -219,15 +204,15 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/articles/examples/index.html b/articles/examples/index.html
index 875054e549..a245d4a4aa 100644
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@@ -27,15 +22,15 @@
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/examples/index.Rmd" class="external-link"><code>vignettes/examples/index.Rmd</code></a></small>
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@@ -141,15 +126,15 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Extending Autograd</h1>
+
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/extending-autograd.Rmd" class="external-link"><code>vignettes/extending-autograd.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/extending-autograd.Rmd" class="external-link"><code>vignettes/extending-autograd.Rmd</code></a></small>
       <div class="d-none name"><code>extending-autograd.Rmd</code></div>
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@@ -218,7 +203,7 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"
 <span><span class="co">#&gt;  2</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -229,15 +214,15 @@ <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Indexing tensors</h1>
+
+      <h1>Indexing tensors</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/indexing.Rmd" class="external-link"><code>vignettes/indexing.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/indexing.Rmd" class="external-link"><code>vignettes/indexing.Rmd</code></a></small>
       <div class="d-none name"><code>indexing.Rmd</code></div>
     </div>
 
@@ -133,28 +118,25 @@ <h2 id="single-element-indexing">Single element indexing<a class="anchor" aria-l
 <span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; 1</span></span>
-<span><span class="co">#&gt; [ CPULongType{} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span></span>
+<span><span class="co">#&gt; [ CPULongType{} ]</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; 10</span></span>
 <span><span class="co">#&gt; [ CPULongType{} ]</span></span></code></pre></div>
 <p>You can also subset matrices and higher dimensions arrays using the
 same syntax:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span>shape <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">x</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;   1   2   3   4   5</span></span>
 <span><span class="co">#&gt;   6   7   8   9  10</span></span>
-<span><span class="co">#&gt; [ CPULongType{2,5} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">]</span></span>
+<span><span class="co">#&gt; [ CPULongType{2,5} ]</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; 3</span></span>
-<span><span class="co">#&gt; [ CPULongType{} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="op">-</span><span class="fl">1</span><span class="op">]</span></span>
+<span><span class="co">#&gt; [ CPULongType{} ]</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="op">-</span><span class="fl">1</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; 5</span></span>
 <span><span class="co">#&gt; [ CPULongType{} ]</span></span></code></pre></div>
@@ -162,7 +144,7 @@ <h2 id="single-element-indexing">Single element indexing<a class="anchor" aria-l
 than dimensions, torch’s behaviour differs from R, which flattens the
 array. In torch, the missing indices are considered complete slices
 <code>:</code>.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
@@ -178,7 +160,7 @@ <h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="
 <p>It is possible to slice and stride arrays to extract sub-arrays of
 the same number of dimensions, but of different sizes than the original.
 This is best illustrated by a few examples:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span>
 <span><span class="va">x</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
@@ -192,17 +174,15 @@ <h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="
 <span><span class="co">#&gt;   8</span></span>
 <span><span class="co">#&gt;   9</span></span>
 <span><span class="co">#&gt;  10</span></span>
-<span><span class="co">#&gt; [ CPULongType{10} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span>
+<span><span class="co">#&gt; [ CPULongType{10} ]</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  2</span></span>
 <span><span class="co">#&gt;  3</span></span>
 <span><span class="co">#&gt;  4</span></span>
 <span><span class="co">#&gt;  5</span></span>
-<span><span class="co">#&gt; [ CPULongType{4} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="op">(</span><span class="op">-</span><span class="fl">7</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="co">#&gt; [ CPULongType{4} ]</span></span>
+<span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="op">(</span><span class="op">-</span><span class="fl">7</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt;  2</span></span>
@@ -211,7 +191,7 @@ <h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="
 <span><span class="co">#&gt; [ CPULongType{4} ]</span></span></code></pre></div>
 <p>You can also use the <code>1:10:2</code> syntax which means: In the
 range from 1 to 10, take every second item. For example:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
@@ -220,7 +200,7 @@ <h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="
 <span><span class="co">#&gt; [ CPULongType{3} ]</span></span></code></pre></div>
 <p>Another special syntax is the <code>N</code>, meaning the size of the
 specified dimension.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">5</span><span class="op">:</span><span class="va">N</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;   5</span></span>
@@ -237,7 +217,7 @@ <h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="
 </blockquote>
 <p>To allow dynamic dynamic indices, you can create a new slice using
 the <code>slc</code> function. For example:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
@@ -245,7 +225,7 @@ <h2 id="slicing-and-striding">Slicing and striding<a class="anchor" aria-label="
 <span><span class="co">#&gt;  5</span></span>
 <span><span class="co">#&gt; [ CPULongType{3} ]</span></span></code></pre></div>
 <p>is equivalent to:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fu"><a href="../reference/slc.html">slc</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">5</span>, step <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
@@ -259,27 +239,27 @@ <h2 id="getting-the-complete-dimension">Getting the complete dimension<a class="
 <p>Like in R, you can take all elements in a dimension by leaving an
 index empty.</p>
 <p>Consider a matrix:</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span></span>
 <span><span class="va">x</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt; -1.1594 -1.9351  1.0987</span></span>
-<span><span class="co">#&gt; -0.2400  0.9302  0.4572</span></span>
+<span><span class="co">#&gt; -1.3069  1.0777 -0.0959</span></span>
+<span><span class="co">#&gt;  0.8759  0.0347 -0.8753</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{2,3} ]</span></span></code></pre></div>
 <p>The following syntax will give you the first row:</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt; -1.1594</span></span>
-<span><span class="co">#&gt; -1.9351</span></span>
-<span><span class="co">#&gt;  1.0987</span></span>
+<span><span class="co">#&gt; -1.3069</span></span>
+<span><span class="co">#&gt;  1.0777</span></span>
+<span><span class="co">#&gt; -0.0959</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{3} ]</span></span></code></pre></div>
 <p>And this would give you the first 2 columns:</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span>,<span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt; -1.1594 -1.9351</span></span>
-<span><span class="co">#&gt; -0.2400  0.9302</span></span>
+<span><span class="co">#&gt; -1.3069  1.0777</span></span>
+<span><span class="co">#&gt;  0.8759  0.0347</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span></code></pre></div>
 </div>
 <div class="section level2">
@@ -287,13 +267,13 @@ <h2 id="dropping-dimensions">Dropping dimensions<a class="anchor" aria-label="an
 </h2>
 <p>By default, when indexing by a single integer, this dimension will be
 dropped to avoid the singleton dimension:</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span></span>
 <span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,<span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
 <span><span class="co">#&gt; [1] 3</span></span></code></pre></div>
 <p>You can optionally use the <code>drop = FALSE</code> argument to
 avoid dropping the dimension.</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fl">1</span>,,drop <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
 <span><span class="co">#&gt; [1] 1 3</span></span></code></pre></div>
 </div>
@@ -302,19 +282,17 @@ <h2 id="adding-a-new-dimension">Adding a new dimension<a class="anchor" aria-lab
 </h2>
 <p>It’s possible to add a new dimension to a tensor using index-like
 syntax:</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">x</span><span class="op">$</span><span class="va">shape</span></span>
-<span><span class="co">#&gt; [1] 1</span></span></code></pre></div>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span>, <span class="va">newaxis</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
-<span><span class="co">#&gt; [1] 1 1</span></span></code></pre></div>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span>, <span class="va">newaxis</span>, <span class="va">newaxis</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
+<span><span class="co">#&gt; [1] 1</span></span>
+<span><span class="va">x</span><span class="op">[</span>, <span class="va">newaxis</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
+<span><span class="co">#&gt; [1] 1 1</span></span>
+<span><span class="va">x</span><span class="op">[</span>, <span class="va">newaxis</span>, <span class="va">newaxis</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
 <span><span class="co">#&gt; [1] 1 1 1</span></span></code></pre></div>
 <p>You can also use <code>NULL</code> instead of
 <code>newaxis</code>:</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span>,<span class="cn">NULL</span><span class="op">]</span><span class="op">$</span><span class="va">shape</span></span>
 <span><span class="co">#&gt; [1] 1 1</span></span></code></pre></div>
 </div>
@@ -324,7 +302,7 @@ <h2 id="dealing-with-variable-number-of-indices">Dealing with variable number of
 <p>Sometimes we don’t know how many dimensions a tensor has, but we do
 know what to do with the last available dimension, or the first one. To
 subsume all others, we can use <code>..</code>:</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">125</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">z</span><span class="op">[</span><span class="fl">1</span>,<span class="va">..</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
@@ -333,9 +311,8 @@ <h2 id="dealing-with-variable-number-of-indices">Dealing with variable number of
 <span><span class="co">#&gt;  11  12  13  14  15</span></span>
 <span><span class="co">#&gt;  16  17  18  19  20</span></span>
 <span><span class="co">#&gt;  21  22  23  24  25</span></span>
-<span><span class="co">#&gt; [ CPULongType{5,5} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">z</span><span class="op">[</span><span class="va">..</span>,<span class="fl">1</span><span class="op">]</span></span>
+<span><span class="co">#&gt; [ CPULongType{5,5} ]</span></span>
+<span><span class="va">z</span><span class="op">[</span><span class="va">..</span>,<span class="fl">1</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;    1    6   11   16   21</span></span>
 <span><span class="co">#&gt;   26   31   36   41   46</span></span>
@@ -352,29 +329,29 @@ <h2 id="indexing-with-vectors">Indexing with vectors<a class="anchor" aria-label
 indexing.</p>
 <blockquote>
 <p>Note: Starting from version 0.5.0, vector indexing in torch follows R
-semantics, prior to that the behavior was similar to <a href="https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing" class="external-link">numpy’s
+semantics, prior to that the behavior was similar to <a href="https://numpy.org/doc/2.2/user/basics.indexing.html#advanced-indexing" class="external-link">numpy’s
 advanced indexing</a>. To use the old behavior, consider using
 <code><a href="../reference/torch_index.html">?torch_index</a></code>, <code><a href="../reference/torch_index_put.html">?torch_index_put</a></code> or
 <code>torch_index_put_</code>.</p>
 </blockquote>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>,<span class="fl">4</span><span class="op">)</span></span>
 <span><span class="va">x</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">3</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0.6781 -0.5159</span></span>
-<span><span class="co">#&gt; -1.0444 -1.0188</span></span>
+<span><span class="co">#&gt; -0.0490  0.8325</span></span>
+<span><span class="co">#&gt;  0.0920 -2.4505</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span></code></pre></div>
 <p>You can also use boolean vectors, for example:</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">TRUE</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span>, <span class="cn">FALSE</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0.6781 -0.5159</span></span>
-<span><span class="co">#&gt; -1.0444 -1.0188</span></span>
+<span><span class="co">#&gt; -0.0490  0.8325</span></span>
+<span><span class="co">#&gt;  0.0920 -2.4505</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span></code></pre></div>
 <p>The above examples also work if the index were long or boolean
 tensors, instead of R vectors. It’s also possible to index with
 multi-dimensional boolean tensors:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>,</span>
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@@ -386,7 +363,7 @@ <h2 id="indexing-with-vectors">Indexing with vectors<a class="anchor" aria-label
 <span><span class="co">#&gt;  6</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{3} ]</span></span></code></pre></div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -397,15 +374,15 @@ <h2 id="indexing-with-vectors">Indexing with vectors<a class="anchor" aria-label
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/articles/installation.html b/articles/installation.html
index e570600c8f..930c69b690 100644
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+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
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@@ -27,76 +22,65 @@
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-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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 </li>
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+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced">
+<li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
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+
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 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Installation</h1>
+
+      <h1>Installation</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/installation.Rmd" class="external-link"><code>vignettes/installation.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/installation.Rmd" class="external-link"><code>vignettes/installation.Rmd</code></a></small>
       <div class="d-none name"><code>installation.Rmd</code></div>
     </div>
 
@@ -258,7 +243,7 @@ <h2 id="pre-built">Installing from pre-built binaries<a class="anchor" aria-labe
 <span>  torch <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"https://torch-cdn.mlverse.org/packages/%s/%s/"</span>, <span class="va">kind</span>, <span class="va">version</span><span class="op">)</span>,</span>
 <span>  CRAN <span class="op">=</span> <span class="st">"https://cloud.r-project.org"</span> <span class="co"># or any other from which you want to install the other R dependencies.</span></span>
 <span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span>, type <span class="op">=</span> <span class="st">"binary"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"torch"</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="troubleshooting">Troubleshooting<a class="anchor" aria-label="anchor" href="#troubleshooting"></a>
@@ -318,7 +303,7 @@ <h3 id="installing-older-versions">Installing older versions<a class="anchor" ar
 to open an issue on GitHub if you need help with this.</p>
 </div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -329,15 +314,15 @@ <h3 id="installing-older-versions">Installing older versions<a class="anchor" ar
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/articles/loading-data.html b/articles/loading-data.html
index 96285c009e..b3b908c0d9 100644
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@@ -27,76 +22,65 @@
 </head>
 <body>
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-    
 
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
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+    <li><a class="dropdown-item" href="../articles/examples/dataset.html">dataset</a></li>
+  </ul>
 </li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
+<li class="nav-item dropdown">
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+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
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-  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
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-     
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-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Loading data</h1>
+
+      <h1>Loading data</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/loading-data.Rmd" class="external-link"><code>vignettes/loading-data.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/loading-data.Rmd" class="external-link"><code>vignettes/loading-data.Rmd</code></a></small>
       <div class="d-none name"><code>loading-data.Rmd</code></div>
     </div>
 
@@ -133,7 +118,7 @@ <h2 id="datasets-and-data-loaders">Datasets and data loaders<a class="anchor" ar
 dataloader. First, the <code>minst_dataset()</code> function is used to
 create <code>ds</code> which is a <code>Dataset</code> object. Then a
 dataloader <code>dl</code> is created to query that data. Finally, that
-dataloader is used in a <code><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">coro::loop()</a></code> to iterate over
+dataloader is used in a <code><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">coro::loop()</a></code> to iterate over
 batches of that data:</p>
 <div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># Create a dataset from included data</span></span>
 <span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>ds <span class="ot">&lt;-</span> <span class="fu">mnist_dataset</span>(</span>
@@ -300,9 +285,8 @@ <h2 id="example-of-using-a-custom-dataset">Example of using a custom Dataset<a c
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">tuxes</span> <span class="op">&lt;-</span> <span class="fu">penguins_dataset</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">tuxes</span><span class="op">$</span><span class="fu">.length</span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="co">#&gt; [1] 333</span></span></code></pre></div>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">tuxes</span><span class="op">$</span><span class="fu">.getitem</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] 333</span></span>
+<span><span class="va">tuxes</span><span class="op">$</span><span class="fu">.getitem</span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;     3.0000</span></span>
@@ -320,15 +304,15 @@ <h2 id="example-of-using-a-custom-dataset">Example of using a custom Dataset<a c
 <span><span class="co">#&gt; [ CPULongType{} ]</span></span></code></pre></div>
 <p>To be able to iterate over <code>tuxes</code>, we need a data loader
 (we override the default batch size of <code>1</code>):</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dl</span> <span class="op">&lt;-</span> <span class="va">tuxes</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span><span class="op">(</span>batch_size <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></span></code></pre></div>
 <p>Calling <code>.length()</code> on a data loader (as opposed to a
 dataset) will return the number of <code>batches</code> we have:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dl</span><span class="op">$</span><span class="fu">.length</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="co">#&gt; [1] 42</span></span></code></pre></div>
 <p>And we can create an iterator to inspect the first batch:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">iter</span> <span class="op">&lt;-</span> <span class="va">dl</span><span class="op">$</span><span class="fu">.iter</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">b</span> <span class="op">&lt;-</span> <span class="va">iter</span><span class="op">$</span><span class="fu">.next</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">b</span></span>
@@ -355,7 +339,7 @@ <h2 id="example-of-using-a-custom-dataset">Example of using a custom Dataset<a c
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt; [ CPULongType{8} ]</span></span></code></pre></div>
-<p>To train a network, we can use <code><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">coro::loop()</a></code> to iterate
+<p>To train a network, we can use <code><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">coro::loop()</a></code> to iterate
 over batches.</p>
 <div class="section level3">
 <h3 id="training-with-data-loaders">Training with data loaders<a class="anchor" aria-label="anchor" href="#training-with-data-loaders"></a>
@@ -363,7 +347,7 @@ <h3 id="training-with-data-loaders">Training with data loaders<a class="anchor"
 <p>Our example network is very simple. (In reality, we would want to
 treat <code>island</code> as the categorical variable it is, and either
 one-hot-encode or embed it.)</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span><span class="op">(</span></span>
 <span>  <span class="st">"PenguinNet"</span>,</span>
 <span>  initialize <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span>
@@ -381,15 +365,15 @@ <h3 id="training-with-data-loaders">Training with data loaders<a class="anchor"
 <span></span>
 <span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">net</span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p>We still need an optimizer:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span></span></code></pre></div>
 <p>And we’re ready to train:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  </span>
 <span>  <span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span></span>
 <span>  </span>
-<span>  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">b</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">b</span> <span class="kw">in</span> <span class="va">dl</span><span class="op">)</span> <span class="op">{</span></span>
 <span>    <span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span>
 <span>    <span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">model</span><span class="op">(</span><span class="va">b</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
 <span>    <span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span><span class="op">(</span><span class="va">output</span>, <span class="va">b</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
@@ -400,7 +384,7 @@ <h3 id="training-with-data-loaders">Training with data loaders<a class="anchor"
 <span>  </span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sprintf.html" class="external-link">sprintf</a></span><span class="op">(</span><span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="va">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html" class="external-link">mean</a></span><span class="op">(</span><span class="va">l</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="op">}</span></span>
-<span><span class="co">#&gt; Loss at epoch 1: 560.068096</span></span>
+<span><span class="co">#&gt; Loss at epoch 1: 1306.323535</span></span>
 <span><span class="co">#&gt; Loss at epoch 2: 2.068251</span></span>
 <span><span class="co">#&gt; Loss at epoch 3: 2.068251</span></span>
 <span><span class="co">#&gt; Loss at epoch 4: 2.068251</span></span>
@@ -432,7 +416,7 @@ <h3 id="use--getbatch-instead-of--getitem">Use <code>.getbatch()</code> instead
 function to pull each single datapoint individually. You can speed this
 up by switching to using <code>.getbatch()</code> which will pull all
 the datapoints in a batch at once:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">penguins_dataset_batching</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/dataset.html">dataset</a></span><span class="op">(</span></span>
 <span>  </span>
 <span>  name <span class="op">=</span> <span class="st">"penguins_dataset_batching"</span>,</span>
@@ -480,7 +464,7 @@ <h3 id="remove-dataset-dataloader-and-manually-define-the-function-calls">Remove
 you were expecting you could also remove the <code>dataset</code>
 entirely and manually pass the data. At this point you are trading
 readability of your code and convenience for speed.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html" class="external-link">na.omit</a></span><span class="op">(</span><span class="va">penguins</span><span class="op">)</span> </span>
 <span><span class="co"># conveniently, the categorical data are already factors</span></span>
 <span><span class="va">input</span><span class="op">$</span><span class="va">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">as.numeric</a></span><span class="op">(</span><span class="va">input</span><span class="op">$</span><span class="va">species</span><span class="op">)</span></span>
@@ -525,7 +509,7 @@ <h3 id="remove-dataset-dataloader-and-manually-define-the-function-calls">Remove
 <span><span class="op">}</span></span></code></pre></div>
 </div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -536,15 +520,15 @@ <h3 id="remove-dataset-dataloader-and-manually-define-the-function-calls">Remove
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/articles/memory-management.html b/articles/memory-management.html
index 92ae55abdb..178569898a 100644
--- a/articles/memory-management.html
+++ b/articles/memory-management.html
@@ -5,19 +5,14 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="torch">
 <title>Memory management • torch</title>
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Memory management</h1>
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@@ -264,7 +249,7 @@ <h2 id="mps">MPS<a class="anchor" aria-label="anchor" href="#mps"></a>
 Tensors. Allocation is then retried and if it fails, a OOM error is
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@@ -275,15 +260,15 @@ <h2 id="mps">MPS<a class="anchor" aria-label="anchor" href="#mps"></a>
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@@ -234,15 +219,15 @@ <h2 id="loading-models-saved-in-python">Loading models saved in python<a class="
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Serialization</h1>
+
+      <h1>Serialization</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/serialization.Rmd" class="external-link"><code>vignettes/serialization.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/serialization.Rmd" class="external-link"><code>vignettes/serialization.Rmd</code></a></small>
       <div class="d-none name"><code>serialization.Rmd</code></div>
     </div>
 
@@ -212,9 +197,8 @@ <h2 id="saving-optimizer-state">Saving optimizer state<a class="anchor" aria-lab
 <span><span class="va">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_mse_loss.html">nnf_mse_loss</a></span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">train_x</span><span class="op">)</span>, <span class="va">train_y</span><span class="op">)</span></span>
 <span><span class="va">loss</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">opt</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="co">#&gt; NULL</span></span></code></pre></div>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span></span>
+<span><span class="co">#&gt; NULL</span></span>
+<span></span>
 <span><span class="co"># Now let's save the optimizer state</span></span>
 <span><span class="va">tmp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="../reference/torch_save.html">torch_save</a></span><span class="op">(</span><span class="va">opt</span><span class="op">$</span><span class="fu">state_dict</span><span class="op">(</span><span class="op">)</span>, <span class="va">tmp</span><span class="op">)</span></span>
@@ -223,7 +207,7 @@ <h2 id="saving-optimizer-state">Saving optimizer state<a class="anchor" aria-lab
 <span><span class="va">opt2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_adam.html">optim_adam</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span><span class="op">)</span></span>
 <span><span class="va">opt2</span><span class="op">$</span><span class="fu">load_state_dict</span><span class="op">(</span><span class="fu"><a href="../reference/torch_load.html">torch_load</a></span><span class="op">(</span><span class="va">tmp</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -234,15 +218,15 @@ <h2 id="saving-optimizer-state">Saving optimizer state<a class="anchor" aria-lab
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/articles/tensor-creation.html b/articles/tensor-creation.html
index 30d9ff0381..fe1fc978d2 100644
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@@ -5,19 +5,14 @@
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@@ -27,76 +22,65 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
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 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creating tensors</h1>
+
+      <h1>Creating tensors</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/tensor-creation.Rmd" class="external-link"><code>vignettes/tensor-creation.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/tensor-creation.Rmd" class="external-link"><code>vignettes/tensor-creation.Rmd</code></a></small>
       <div class="d-none name"><code>tensor-creation.Rmd</code></div>
     </div>
 
@@ -132,17 +117,15 @@ <h2 id="from-r-objects">From R objects<a class="anchor" aria-label="anchor" href
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt;  2</span></span>
 <span><span class="co">#&gt;  3</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{3} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span></span>
+<span><span class="co">#&gt; [ CPUFloatType{3} ]</span></span>
+<span></span>
 <span><span class="co"># conform to row-major indexing used in torch</span></span>
 <span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, ncol <span class="op">=</span> <span class="fl">5</span>, nrow <span class="op">=</span> <span class="fl">2</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;   1   2   3   4   5</span></span>
 <span><span class="co">#&gt;   6   7   8   9  10</span></span>
-<span><span class="co">#&gt; [ CPULongType{2,5} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [ CPULongType{2,5} ]</span></span>
+<span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span>, dim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt; (1,.,.) = </span></span>
 <span><span class="co">#&gt;   0.6008  0.4978  0.8746</span></span>
@@ -161,13 +144,12 @@ <h2 id="from-r-objects">From R objects<a class="anchor" aria-label="anchor" href
 </blockquote>
 <p>You can always modify <code>dtype</code> and <code>device</code> when
 converting an R object to a torch tensor. For example:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, dtype <span class="op">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
-<span><span class="co">#&gt; [ CPULongType{1} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, device <span class="op">=</span> <span class="st">"cpu"</span>, dtype <span class="op">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [ CPULongType{1} ]</span></span>
+<span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span>, device <span class="op">=</span> <span class="st">"cpu"</span>, dtype <span class="op">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_float64</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt; [ CPUDoubleType{1} ]</span></span></code></pre></div>
@@ -195,19 +177,19 @@ <h2 id="using-creation-functions">Using creation functions<a class="anchor" aria
 1. You can use the <code>...</code> argument to pass the size of the
 dimensions. For example, the code below will create a normally
 distributed tensor with shape 5x3.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">3</span><span class="op">)</span></span>
 <span><span class="va">x</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0.8893  0.0678  0.1857</span></span>
-<span><span class="co">#&gt;  2.8248 -1.0283 -2.1778</span></span>
-<span><span class="co">#&gt; -1.9115 -0.3700 -0.5116</span></span>
-<span><span class="co">#&gt;  0.6165  0.7820  0.5383</span></span>
-<span><span class="co">#&gt; -0.4962 -0.7342 -0.2521</span></span>
+<span><span class="co">#&gt; -0.7873 -0.3195 -0.6137</span></span>
+<span><span class="co">#&gt; -0.5345  0.5600 -0.5529</span></span>
+<span><span class="co">#&gt; -0.8468 -2.1746  0.1606</span></span>
+<span><span class="co">#&gt;  0.5567  2.3597  1.1621</span></span>
+<span><span class="co">#&gt;  1.0835 -2.1803 -1.1921</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{5,3} ]</span></span></code></pre></div>
 <p>Another example is <code>torch_ones</code>, which creates a tensor
 filled with ones.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">4</span>, dtype <span class="op">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_int64</a></span><span class="op">(</span><span class="op">)</span>, device <span class="op">=</span> <span class="st">"cpu"</span><span class="op">)</span></span>
 <span><span class="va">x</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
@@ -258,23 +240,22 @@ <h2 id="conversion">Conversion<a class="anchor" aria-label="anchor" href="#conve
 </h2>
 <p>Once a tensor exists you can convert between <code>dtype</code>s and
 move to a different device with <code>to</code> method. For example:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span></span>
 <span><span class="va">y</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">$</span><span class="fu">to</span><span class="op">(</span>dtype <span class="op">=</span> <span class="fu"><a href="../reference/torch_dtype.html">torch_int32</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">x</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">y</span></span>
+<span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span><span class="va">y</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  1</span></span>
 <span><span class="co">#&gt; [ CPUIntType{1} ]</span></span></code></pre></div>
 <p>You can also copy a tensor to the GPU using:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">torch_tensor</span>(<span class="dv">1</span>)</span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a>y <span class="ot">&lt;-</span> x<span class="sc">$</span><span class="fu">cuda</span>()<span class="er">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">torch_tensor</span>(<span class="dv">1</span>)</span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>y <span class="ot">&lt;-</span> x<span class="sc">$</span><span class="fu">cuda</span>()<span class="er">)</span></span></code></pre></div>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -285,15 +266,15 @@ <h2 id="conversion">Conversion<a class="anchor" aria-label="anchor" href="#conve
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
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 </html>
diff --git a/articles/tensor/index.html b/articles/tensor/index.html
index a001a71b8e..6d31b2bd6f 100644
--- a/articles/tensor/index.html
+++ b/articles/tensor/index.html
@@ -5,19 +5,14 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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 <title>Tensor objects • torch</title>
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@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tensor objects</h1>
+
+      <h1>Tensor objects</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/tensor/index.Rmd" class="external-link"><code>vignettes/tensor/index.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/tensor/index.Rmd" class="external-link"><code>vignettes/tensor/index.Rmd</code></a></small>
       <div class="d-none name"><code>index.Rmd</code></div>
     </div>
 
@@ -603,8 +588,10 @@ <h2 id="baddbmm_">baddbmm_<a class="anchor" aria-label="anchor" href="#baddbmm_"
 <h2 id="bernoulli">bernoulli<a class="anchor" aria-label="anchor" href="#bernoulli"></a>
 </h2>
 <p>bernoulli(*, generator=NULL) -&gt; Tensor</p>
-<p>Returns a result tensor where each <span class="math inline">\(\texttt{result[i]}\)</span> is independently
-sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{self[i]})\)</span>.
+<p>Returns a result tensor where each
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="monospace">𝚛𝚎𝚜𝚞𝚕𝚝[𝚒]</mtext><annotation encoding="application/x-tex">\texttt{result[i]}</annotation></semantics></math>
+is independently sampled from
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Bernoulli</mtext><mrow><mo stretchy="true" form="prefix">(</mo><mtext mathvariant="monospace">𝚜𝚎𝚕𝚏[𝚒]</mtext><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{self[i]})</annotation></semantics></math>.
 <code>self</code> must have floating point <code>dtype</code>, and the
 result will have the same <code>dtype</code>.</p>
 <p>See <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
@@ -614,13 +601,16 @@ <h2 id="bernoulli_">bernoulli_<a class="anchor" aria-label="anchor" href="#berno
 </h2>
 <p>bernoulli_(p=0.5, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills each location of <code>self</code> with an independent sample
-from <span class="math inline">\(\text{Bernoulli}(\texttt{p})\)</span>.
+from
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Bernoulli</mtext><mrow><mo stretchy="true" form="prefix">(</mo><mtext mathvariant="monospace">𝚙</mtext><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p})</annotation></semantics></math>.
 <code>self</code> can have integral <code>dtype</code>.</p>
 <p>bernoulli_(p_tensor, *, generator=NULL) -&gt; Tensor</p>
 <p><code>p_tensor</code> should be a tensor containing probabilities to
 be used for drawing the binary random number.</p>
-<p>The <span class="math inline">\(\text{i}^{th}\)</span> element of
-<code>self</code> tensor will be set to a value sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{p\_tensor[i]})\)</span>.</p>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mtext mathvariant="normal">i</mtext><mrow><mi>t</mi><mi>h</mi></mrow></msup><annotation encoding="application/x-tex">\text{i}^{th}</annotation></semantics></math>
+element of <code>self</code> tensor will be set to a value sampled from
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Bernoulli</mtext><mrow><mo stretchy="true" form="prefix">(</mo><mtext mathvariant="monospace">𝚙_𝚝𝚎𝚗𝚜𝚘𝚛[𝚒]</mtext><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p\_tensor[i]})</annotation></semantics></math>.</p>
 <p><code>self</code> can have integral <code>dtype</code>, but
 <code>p_tensor</code> must have floating point <code>dtype</code>.</p>
 <p>See also <code>$bernoulli</code> and
@@ -739,9 +729,9 @@ <h2 id="cauchy_">cauchy_<a class="anchor" aria-label="anchor" href="#cauchy_"></
 </h2>
 <p>cauchy_(median=0, sigma=1, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
-<p><span class="math display">\[
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>x</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mfrac displaystyle="true"><mn>1</mn><mi>π</mi></mfrac><mfrac displaystyle="true"><mi>σ</mi><mrow><msup><mrow><mo stretchy="true" form="prefix">(</mo><mi>x</mi><mo>−</mo><mtext mathvariant="normal">median</mtext><mo stretchy="true" form="postfix">)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mi>σ</mi><mn>2</mn></msup></mrow></mfrac></mrow><annotation encoding="application/x-tex">
 f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
-\]</span></p>
+</annotation></semantics></math></p>
 </div>
 <div class="section level2">
 <h2 id="ceil">ceil<a class="anchor" aria-label="anchor" href="#ceil"></a>
@@ -1292,9 +1282,9 @@ <h2 id="exponential_">exponential_<a class="anchor" aria-label="anchor" href="#e
 <p>exponential_(lambd=1, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with elements drawn from the
 exponential distribution:</p>
-<p><span class="math display">\[
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>x</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mi>λ</mi><msup><mi>e</mi><mrow><mo>−</mo><mi>λ</mi><mi>x</mi></mrow></msup></mrow><annotation encoding="application/x-tex">
 f(x) = \lambda e^{-\lambda x}
-\]</span></p>
+</annotation></semantics></math></p>
 </div>
 <div class="section level2">
 <h2 id="fft">fft<a class="anchor" aria-label="anchor" href="#fft"></a>
@@ -1449,9 +1439,9 @@ <h2 id="geometric_">geometric_<a class="anchor" aria-label="anchor" href="#geome
 <p>geometric_(p, *, generator=NULL) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with elements drawn from the geometric
 distribution:</p>
-<p><span class="math display">\[
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>X</mi><mo>=</mo><mi>k</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><msup><mi>p</mi><mrow><mi>k</mi><mo>−</mo><mn>1</mn></mrow></msup><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>p</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">
 f(X=k) = p^{k - 1} (1 - p)
-\]</span></p>
+</annotation></semantics></math></p>
 </div>
 <div class="section level2">
 <h2 id="geqrf">geqrf<a class="anchor" aria-label="anchor" href="#geqrf"></a>
@@ -2037,10 +2027,9 @@ <h2 id="log_normal_">log_normal_<a class="anchor" aria-label="anchor" href="#log
 and <code>std</code> are the mean and standard deviation of the
 underlying normal distribution, and not of the returned
 distribution:</p>
-<p><span class="math display">\[
-f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x -
-\mu)^2}{2\sigma^2}}
-\]</span></p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>f</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>x</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mfrac displaystyle="true"><mn>1</mn><mrow><mi>x</mi><mi>σ</mi><msqrt><mrow><mn>2</mn><mi>π</mi></mrow></msqrt></mrow></mfrac><mspace width="0.222em"></mspace><msup><mi>e</mi><mrow><mo>−</mo><mfrac><msup><mrow><mo stretchy="true" form="prefix">(</mo><mo>ln</mo><mi>x</mi><mo>−</mo><mi>μ</mi><mo stretchy="true" form="postfix">)</mo></mrow><mn>2</mn></msup><mrow><mn>2</mn><msup><mi>σ</mi><mn>2</mn></msup></mrow></mfrac></mrow></msup></mrow><annotation encoding="application/x-tex">
+f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
+</annotation></semantics></math></p>
 </div>
 <div class="section level2">
 <h2 id="logaddexp">logaddexp<a class="anchor" aria-label="anchor" href="#logaddexp"></a>
@@ -2355,8 +2344,8 @@ <h2 id="narrow_copy">narrow_copy<a class="anchor" aria-label="anchor" href="#nar
 <p>Same as <code>Tensor.narrow</code> except returning a copy rather
 than shared storage. This is primarily for sparse tensors, which do not
 have a shared-storage narrow method. Calling
-<code>narrow_copy` with</code>dimemsion &gt; self<span class="math inline">\(sparse_dim()`` will return a copy with the
-relevant dense dimension narrowed, and ``self\)</span>shape`` updated
+<code>narrow_copy` with</code>dimemsion &gt; self<span class="math inline">$sparse_dim()`` will return a copy with the
+relevant dense dimension narrowed, and ``self$</span>shape`` updated
 accordingly.</p>
 </div>
 <div class="section level2">
@@ -2513,9 +2502,10 @@ <h4 id="warning-5">Warning:<a class="anchor" aria-label="anchor" href="#warning-
 </h4>
 <p><code>new_tensor</code> always copies
 <code>data(). If you have a Tensor</code>data` and want to avoid a copy,
-use [<span class="math inline">\(requires_grad_()]
-or [\)</span>detach()]. If you have a numpy array and want to avoid a
-copy, use [torch_from_numpy()].</p>
+use
+[<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>r</mi><mi>e</mi><mi>q</mi><mi>u</mi><mi>i</mi><mi>r</mi><mi>e</mi><msub><mi>s</mi><mi>g</mi></msub><mi>r</mi><mi>a</mi><msub><mi>d</mi><mrow><mo stretchy="true" form="prefix">(</mo><mo stretchy="true" form="postfix">)</mo></mrow></msub><mo stretchy="false" form="postfix">]</mo><mi>o</mi><mi>r</mi><mo stretchy="false" form="prefix">[</mo></mrow><annotation encoding="application/x-tex">requires_grad_()]
+or [</annotation></semantics></math>detach()]. If you have a numpy array
+and want to avoid a copy, use [torch_from_numpy()].</p>
 <p>When data is a tensor <code>x</code>, [new_tensor()()] reads out ‘the
 data’ from whatever it is passed, and constructs a leaf variable.
 Therefore <code>tensor$new_tensor(x)</code> is equivalent to
@@ -3925,9 +3915,9 @@ <h2 id="uniform_">uniform_<a class="anchor" aria-label="anchor" href="#uniform_"
 <p>uniform_(from=0, to=1) -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with numbers sampled from the
 continuous uniform distribution:</p>
-<p><span class="math display">\[
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>P</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>x</mi><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mfrac displaystyle="true"><mn>1</mn><mrow><mtext mathvariant="normal">to</mtext><mo>−</mo><mtext mathvariant="normal">from</mtext></mrow></mfrac></mrow><annotation encoding="application/x-tex">
 P(x) = \dfrac{1}{\text{to} - \text{from}}
-\]</span></p>
+</annotation></semantics></math></p>
 </div>
 <div class="section level2">
 <h2 id="unique">unique<a class="anchor" aria-label="anchor" href="#unique"></a>
@@ -3988,9 +3978,9 @@ <h2 id="view">view<a class="anchor" aria-label="anchor" href="#view"></a>
 <code>d, d+1, \dots, d+k</code> that satisfy the following
 contiguity-like condition that
 <code>\forall i = d, \dots, d+k-1</code>,</p>
-<p><span class="math display">\[
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">stride</mtext><mrow><mo stretchy="true" form="prefix">[</mo><mi>i</mi><mo stretchy="true" form="postfix">]</mo></mrow><mo>=</mo><mtext mathvariant="normal">stride</mtext><mrow><mo stretchy="true" form="prefix">[</mo><mi>i</mi><mo>+</mo><mn>1</mn><mo stretchy="true" form="postfix">]</mo></mrow><mo>×</mo><mtext mathvariant="normal">size</mtext><mrow><mo stretchy="true" form="prefix">[</mo><mi>i</mi><mo>+</mo><mn>1</mn><mo stretchy="true" form="postfix">]</mo></mrow></mrow><annotation encoding="application/x-tex">
 \text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
-\]</span></p>
+</annotation></semantics></math></p>
 <p>Otherwise, it will not be possible to view <code>self</code> tensor
 as <code>shape</code> without copying it (e.g., via
 <code>contiguous</code>). When it is unclear whether a <code>view</code>
@@ -4037,7 +4027,7 @@ <h2 id="zero_">zero_<a class="anchor" aria-label="anchor" href="#zero_"></a>
 <p>zero_() -&gt; Tensor</p>
 <p>Fills <code>self</code> tensor with zeros.</p>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -4048,15 +4038,15 @@ <h2 id="zero_">zero_<a class="anchor" aria-label="anchor" href="#zero_"></a>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/articles/torchscript.html b/articles/torchscript.html
index f737a8d902..463efde560 100644
--- a/articles/torchscript.html
+++ b/articles/torchscript.html
@@ -5,19 +5,14 @@
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+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet">
 <meta property="og:title" content="TorchScript">
-<meta property="og:description" content="torch">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   gtag('js', new Date());
@@ -27,15 +22,15 @@
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
@@ -43,60 +38,49 @@
     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto">
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/installation.html">Installation</a>
-    <h6 class="dropdown-header" data-toc-skip>Tensors</h6>
-    <a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a>
-    <a class="dropdown-item" href="../articles/indexing.html">Indexing</a>
-    <a class="dropdown-item" href="../articles/tensor/index.html">Tensor class</a>
-    <a class="dropdown-item" href="../articles/serialization.html">Serialization</a>
-    <h6 class="dropdown-header" data-toc-skip>Datasets</h6>
-    <a class="dropdown-item" href="../articles/loading-data.html">Loading Data</a>
-    <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
-    <a class="dropdown-item" href="../articles/using-autograd.html">Using autograd</a>
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-    <a class="dropdown-item" href="../articles/python-to-r.html">Python models</a>
-  </div>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/installation.html">Installation</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Tensors</h6></li>
+    <li><a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a></li>
+    <li><a class="dropdown-item" href="../articles/indexing.html">Indexing</a></li>
+    <li><a class="dropdown-item" href="../articles/tensor/index.html">Tensor class</a></li>
+    <li><a class="dropdown-item" href="../articles/serialization.html">Serialization</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Datasets</h6></li>
+    <li><a class="dropdown-item" href="../articles/loading-data.html">Loading Data</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Autograd</h6></li>
+    <li><a class="dropdown-item" href="../articles/using-autograd.html">Using autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/extending-autograd.html">Extending autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/python-to-r.html">Python models</a></li>
+    <li><a class="dropdown-item" href="../articles/torchscript.html">Jit Compilation</a></li>
+  </ul>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-examples">
+<li><a class="dropdown-item" href="../articles/examples/basic-autograd.html">basic-autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/basic-nn-module.html">basic-nn-module</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/dataset.html">dataset</a></li>
+  </ul>
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-  <div class="dropdown-menu" aria-labelledby="dropdown-advanced">
-    <a class="dropdown-item" href="../articles/memory-management.html">Memory management</a>
-    <a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a>
-    <a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a>
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-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced">
+<li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>TorchScript</h1>
+
+      <h1>TorchScript</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/torchscript.Rmd" class="external-link"><code>vignettes/torchscript.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/torchscript.Rmd" class="external-link"><code>vignettes/torchscript.Rmd</code></a></small>
       <div class="d-none name"><code>torchscript.Rmd</code></div>
     </div>
 
@@ -171,9 +156,9 @@ <h3 id="tracing">Tracing<a class="anchor" aria-label="anchor" href="#tracing"></
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">traced_fn</span><span class="op">(</span><span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0.0000</span></span>
-<span><span class="co">#&gt;  0.7543</span></span>
-<span><span class="co">#&gt;  0.0000</span></span>
+<span><span class="co">#&gt;  0.4826</span></span>
+<span><span class="co">#&gt;  0.2149</span></span>
+<span><span class="co">#&gt;  0.1657</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{3} ]</span></span></code></pre></div>
 <p>It’s also possible to trace <code>nn_modules()</code> defined in R,
 for example:</p>
@@ -184,25 +169,28 @@ <h3 id="tracing">Tracing<a class="anchor" aria-label="anchor" href="#tracing"></
 <span>    <span class="va">self</span><span class="op">$</span><span class="va">linear2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span><span class="op">)</span></span>
 <span>  <span class="op">}</span>,</span>
 <span>  forward <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
-<span>    <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
-<span>      <span class="va">self</span><span class="op">$</span><span class="fu">linear1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
-<span>      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
+<span>    <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span></span>
+<span>      <span class="va">self</span><span class="op">$</span><span class="fu">linear1</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span></span>
+<span>      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span></span>
 <span>      <span class="va">self</span><span class="op">$</span><span class="fu">linear2</span><span class="op">(</span><span class="op">)</span></span>
 <span>  <span class="op">}</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">traced_module</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="fu">module</span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p>When using <code>jit_trace</code> with a <code>nn_module</code> only
-the <code>forward</code> method is traced. You can use the
+the <code>forward</code> method is traced. However, by default, one pass
+will be conducted in ‘train’ mode, and another one in ‘eval’ mode, which
+is different from the PyTorch behavior. One can opt out of this by
+specifying <code>respect_mode = FALSE</code> which will only trace the
+forward pass in the mode the network is currently in. You can use the
 <code>jit_trace_module</code> function to pass example inputs to other
 methods. Traced modules look like normal <code>nn_modules()</code>, and
 can be called the same way:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">traced_module</span><span class="op">(</span><span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt; 0.01 *</span></span>
-<span><span class="co">#&gt;  5.6937</span></span>
-<span><span class="co">#&gt;   3.3835</span></span>
-<span><span class="co">#&gt;   4.8311</span></span>
+<span><span class="co">#&gt; -0.1682</span></span>
+<span><span class="co">#&gt; -0.2424</span></span>
+<span><span class="co">#&gt;  0.0244</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{3,1} ][ grad_fn = &lt;AddmmBackward0&gt; ]</span></span></code></pre></div>
 <div class="section level4">
 <h4 id="limitations-of-tracing">Limitations of tracing<a class="anchor" aria-label="anchor" href="#limitations-of-tracing"></a>
@@ -219,8 +207,8 @@ <h4 id="limitations-of-tracing">Limitations of tracing<a class="anchor" aria-lab
 <code class="sourceCode R"><span><span class="co"># fn does does an operation for each dimension of a tensor</span></span>
 <span><span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="va">x</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
-<span>    <span class="fu"><a href="../reference/torch_unbind.html">torch_unbind</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
+<span>    <span class="fu"><a href="../reference/torch_unbind.html">torch_unbind</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span></span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="fu">sum</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span></span>
 <span>    <span class="fu"><a href="../reference/torch_stack.html">torch_stack</a></span><span class="op">(</span>dim <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
 <span><span class="op">}</span></span>
 <span><span class="co"># we trace using as an example a tensor with size (10, 5, 5)</span></span>
@@ -240,94 +228,93 @@ <h4 id="limitations-of-tracing">Limitations of tracing<a class="anchor" aria-lab
 <code class="sourceCode R"><span><span class="va">traced_dropout</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="fu"><a href="../reference/nn_dropout.html">nn_dropout</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu">traced_dropout</span><span class="op">(</span><span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  2  0  0</span></span>
-<span><span class="co">#&gt;  2  0  0</span></span>
-<span><span class="co">#&gt;  2  0  2</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{3,3} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">traced_dropout</span><span class="op">$</span><span class="fu">eval</span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="co">#&gt;  0  0  0</span></span>
+<span><span class="co">#&gt;  0  0  2</span></span>
+<span><span class="co">#&gt;  0  0  2</span></span>
+<span><span class="co">#&gt; [ CPUFloatType{3,3} ]</span></span>
+<span><span class="va">traced_dropout</span><span class="op">$</span><span class="fu">eval</span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] FALSE</span></span>
 <span><span class="co"># even after setting to eval mode, dropout is applied</span></span>
 <span><span class="fu">traced_dropout</span><span class="op">(</span><span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
-<span><span class="co">#&gt;  0  2  0</span></span>
-<span><span class="co">#&gt;  2  0  2</span></span>
-<span><span class="co">#&gt;  0  2  2</span></span>
+<span><span class="co">#&gt;  1  1  1</span></span>
+<span><span class="co">#&gt;  1  1  1</span></span>
+<span><span class="co">#&gt;  1  1  1</span></span>
 <span><span class="co">#&gt; [ CPUFloatType{3,3} ]</span></span></code></pre></div>
 <ol start="3" style="list-style-type: decimal">
 <li>Tracing proegrams can only take tensors and lists of tensors as
 input and return tensors and lists of tensors. For example:</li>
 </ol>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="va">x</span> <span class="op">+</span> <span class="va">y</span></span>
 <span><span class="op">}</span></span>
 <span><span class="fu"><a href="../reference/jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="va">fn</span>, <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span>, <span class="fl">1</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Error in cpp_trace_function(tr_fn, list(...), .compilation_unit, strict, : Only tensors or (possibly nested) dict or tuples of tensors can be inputs to traced functions. Got float</span></span>
-<span><span class="co">#&gt; Exception raised from addInput at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/frontend/tracer.cpp:422 (most recent call first):</span></span>
-<span><span class="co">#&gt; frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;) + 81 (0x105089ca1 in libc10.dylib)</span></span>
-<span><span class="co">#&gt; frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt; const&amp;) + 98 (0x105088342 in libc10.dylib)</span></span>
-<span><span class="co">#&gt; frame #2: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, c10::Type::SingletonOrSharedTypePtr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 6052 (0x1270ce564 in libtorch_cpu.dylib)</span></span>
-<span><span class="co">#&gt; frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, c10::Type::SingletonOrSharedTypePtr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 4445 (0x1270cdf1d in libtorch_cpu.dylib)</span></span>
-<span><span class="co">#&gt; frame #4: torch::jit::tracer::trace(std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt;&gt;, std::__1::function&lt;std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt;&gt; (std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt;&gt;)&gt; const&amp;, std::__1::function&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt; (at::Tensor const&amp;)&gt;, bool, bool, torch::jit::Module*, std::__1::vector&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;, std::__1::allocator&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;&gt;&gt; const&amp;) + 826 (0x1270cb59a in libtorch_cpu.dylib)</span></span>
-<span><span class="co">#&gt; frame #5: _lantern_trace_fn + 597 (0x109b5fb35 in liblantern.dylib)</span></span>
-<span><span class="co">#&gt; frame #6: cpp_trace_function(Rcpp::Function_Impl&lt;Rcpp::PreserveStorage&gt;, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 566 (0x107d05176 in torchpkg.so)</span></span>
-<span><span class="co">#&gt; frame #7: _torch_cpp_trace_function + 719 (0x107b3facf in torchpkg.so)</span></span>
-<span><span class="co">#&gt; frame #8: R_doDotCall + 13245 (0x102395a3d in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #9: bcEval_loop + 146643 (0x1023fd6d3 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #10: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #11: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #12: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #13: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #14: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #15: do_eval + 1253 (0x1023d20e5 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #16: bcEval_loop + 44476 (0x1023e47bc in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #17: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #18: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #19: forcePromise + 230 (0x1023cb5a6 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #20: Rf_eval + 634 (0x1023caafa in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #21: do_withVisible + 57 (0x1023d2479 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #22: do_internal + 362 (0x10244a0aa in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #23: bcEval_loop + 45103 (0x1023e4a2f in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #24: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #25: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #26: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #27: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #28: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #29: do_begin + 429 (0x1023cffad in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #30: Rf_eval + 990 (0x1023cac5e in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #31: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #32: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #33: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #34: do_docall + 615 (0x10235b827 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #35: bcEval_loop + 44476 (0x1023e47bc in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #36: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #37: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #38: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #39: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #40: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #41: do_docall + 615 (0x10235b827 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #42: bcEval_loop + 44476 (0x1023e47bc in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #43: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #44: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #45: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #46: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #47: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #48: forcePromise + 230 (0x1023cb5a6 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #49: bcEval_loop + 19496 (0x1023de628 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #50: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #51: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #52: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #53: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #54: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #55: do_begin + 429 (0x1023cffad in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #56: Rf_eval + 990 (0x1023cac5e in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #57: forcePromise + 230 (0x1023cb5a6 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #58: bcEval_loop + 19496 (0x1023de628 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #59: bcEval + 628 (0x1023cb374 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #60: Rf_eval + 506 (0x1023caa7a in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #61: R_execClosure + 761 (0x1023cd5b9 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #62: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib)</span></span>
-<span><span class="co">#&gt; frame #63: Rf_eval + 1189 (0x1023cad25 in libR.dylib)</span></span>
+<span><span class="co">#&gt; Exception raised from addInput at /Users/runner/work/libtorch-mac-m1/libtorch-mac-m1/pytorch/torch/csrc/jit/frontend/tracer.cpp:422 (most recent call first):</span></span>
+<span><span class="co">#&gt; frame #0: std::__1::shared_ptr&lt;c10::(anonymous namespace)::PyTorchStyleBacktrace&gt; std::__1::make_shared[abi:ue170006]&lt;c10::(anonymous namespace)::PyTorchStyleBacktrace, c10::SourceLocation&amp;, void&gt;(c10::SourceLocation&amp;) + 121 (0x111416639 in libc10.dylib)</span></span>
+<span><span class="co">#&gt; frame #1: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;) + 54 (0x111416776 in libc10.dylib)</span></span>
+<span><span class="co">#&gt; frame #2: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt; const&amp;) + 149 (0x111413035 in libc10.dylib)</span></span>
+<span><span class="co">#&gt; frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, c10::Type::SingletonOrSharedTypePtr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 6225 (0x128a06cf1 in libtorch_cpu.dylib)</span></span>
+<span><span class="co">#&gt; frame #4: torch::jit::tracer::addInput(std::__1::shared_ptr&lt;torch::jit::tracer::TracingState&gt; const&amp;, c10::IValue const&amp;, c10::Type::SingletonOrSharedTypePtr&lt;c10::Type&gt; const&amp;, torch::jit::Value*) + 4799 (0x128a0675f in libtorch_cpu.dylib)</span></span>
+<span><span class="co">#&gt; frame #5: torch::jit::tracer::trace(std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt;&gt;, std::__1::function&lt;std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt;&gt; (std::__1::vector&lt;c10::IValue, std::__1::allocator&lt;c10::IValue&gt;&gt;)&gt; const&amp;, std::__1::function&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt; (at::Tensor const&amp;)&gt;, bool, bool, torch::jit::Module*, std::__1::vector&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;, std::__1::allocator&lt;std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;&gt;&gt; const&amp;) + 666 (0x128a03eaa in libtorch_cpu.dylib)</span></span>
+<span><span class="co">#&gt; frame #6: _lantern_trace_fn + 408 (0x11608de28 in liblantern.dylib)</span></span>
+<span><span class="co">#&gt; frame #7: cpp_trace_function(Rcpp::Function_Impl&lt;Rcpp::PreserveStorage&gt;, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 601 (0x1142e7959 in torchpkg.so)</span></span>
+<span><span class="co">#&gt; frame #8: _torch_cpp_trace_function + 719 (0x11410468f in torchpkg.so)</span></span>
+<span><span class="co">#&gt; frame #9: R_doDotCall + 13245 (0x10e8074bd in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #10: bcEval_loop + 146595 (0x10e86f123 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #11: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #12: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #13: R_execClosure + 761 (0x10e83f039 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #14: applyClosure_core + 128 (0x10e83e140 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #15: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #16: do_eval + 1253 (0x10e843b65 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #17: bcEval_loop + 44444 (0x10e85621c in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #18: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #19: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #20: forcePromise + 230 (0x10e83d026 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #21: Rf_eval + 634 (0x10e83c57a in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #22: do_withVisible + 57 (0x10e843ef9 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #23: do_internal + 362 (0x10e8bbb6a in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #24: bcEval_loop + 45071 (0x10e85648f in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #25: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #26: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #27: forcePromise + 230 (0x10e83d026 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #28: Rf_eval + 634 (0x10e83c57a in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #29: forcePromise + 230 (0x10e83d026 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #30: bcEval_loop + 19464 (0x10e850088 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #31: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #32: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #33: R_execClosure + 761 (0x10e83f039 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #34: applyClosure_core + 128 (0x10e83e140 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #35: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #36: do_eval + 1253 (0x10e843b65 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #37: bcEval_loop + 44444 (0x10e85621c in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #38: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #39: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #40: R_execClosure + 761 (0x10e83f039 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #41: applyClosure_core + 128 (0x10e83e140 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #42: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #43: do_begin + 429 (0x10e841a2d in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #44: Rf_eval + 990 (0x10e83c6de in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #45: R_execClosure + 761 (0x10e83f039 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #46: applyClosure_core + 128 (0x10e83e140 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #47: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #48: do_docall + 615 (0x10e7cd2a7 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #49: bcEval_loop + 44444 (0x10e85621c in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #50: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #51: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #52: R_execClosure + 761 (0x10e83f039 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #53: applyClosure_core + 128 (0x10e83e140 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #54: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #55: do_docall + 615 (0x10e7cd2a7 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #56: bcEval_loop + 44444 (0x10e85621c in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #57: bcEval + 628 (0x10e83cdf4 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #58: Rf_eval + 506 (0x10e83c4fa in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #59: R_execClosure + 761 (0x10e83f039 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #60: applyClosure_core + 128 (0x10e83e140 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #61: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib)</span></span>
+<span><span class="co">#&gt; frame #62: forcePromise + 230 (0x10e83d026 in libR.dylib)</span></span>
 <span><span class="co">#&gt; :</span></span></code></pre></div>
 </div>
 </div>
@@ -337,7 +324,7 @@ <h3 id="compiling-torchscript">Compiling TorchScript<a class="anchor" aria-label
 <p>It’s also possible to create TorchScript programs by compiling
 TorchScript code. TorchScript code looks a lot like standard python
 code. For example:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">tr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_compile.html">jit_compile</a></span><span class="op">(</span><span class="st">"</span></span>
 <span><span class="st">def fn (x: Tensor):</span></span>
 <span><span class="st">  return torch.relu(x)</span></span>
@@ -358,7 +345,7 @@ <h2 id="serializing-and-loading">Serializing and loading<a class="anchor" aria-l
 <code>jit_save</code> function and loaded back from disk with
 <code>jit_load</code>.</p>
 <p>For example:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">fn</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="fu"><a href="../reference/torch_relu.html">torch_relu</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span><span class="op">}</span></span>
@@ -366,7 +353,7 @@ <h2 id="serializing-and-loading">Serializing and loading<a class="anchor" aria-l
 <span><span class="fu"><a href="../reference/jit_save.html">jit_save</a></span><span class="op">(</span><span class="va">tr_fn</span>, <span class="st">"path.pt"</span><span class="op">)</span></span>
 <span><span class="va">loaded</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/jit_load.html">jit_load</a></span><span class="op">(</span><span class="st">"path.pt"</span><span class="op">)</span></span></code></pre></div>
 <p>Loaded programs can be executed as usual:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">loaded</span><span class="op">(</span><span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  0</span></span>
@@ -455,7 +442,7 @@ <h2 id="types">Types<a class="anchor" aria-label="anchor" href="#types"></a>
 </tbody>
 </table>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -466,15 +453,15 @@ <h2 id="types">Types<a class="anchor" aria-label="anchor" href="#types"></a>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/articles/using-autograd.html b/articles/using-autograd.html
index 542633876a..1b40489d48 100644
--- a/articles/using-autograd.html
+++ b/articles/using-autograd.html
@@ -5,19 +5,14 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
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-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet">
 <meta property="og:title" content="Using autograd">
-<meta property="og:description" content="torch">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   gtag('js', new Date());
@@ -27,76 +22,65 @@
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto">
-<li class="active nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/installation.html">Installation</a>
-    <h6 class="dropdown-header" data-toc-skip>Tensors</h6>
-    <a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a>
-    <a class="dropdown-item" href="../articles/indexing.html">Indexing</a>
-    <a class="dropdown-item" href="../articles/tensor/index.html">Tensor class</a>
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-    <h6 class="dropdown-header" data-toc-skip>Datasets</h6>
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+<li><a class="dropdown-item" href="../articles/installation.html">Installation</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Tensors</h6></li>
+    <li><a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a></li>
+    <li><a class="dropdown-item" href="../articles/indexing.html">Indexing</a></li>
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+    <li><a class="dropdown-item" href="../articles/loading-data.html">Loading Data</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Autograd</h6></li>
+    <li><a class="dropdown-item" href="../articles/using-autograd.html">Using autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/extending-autograd.html">Extending autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/python-to-r.html">Python models</a></li>
+    <li><a class="dropdown-item" href="../articles/torchscript.html">Jit Compilation</a></li>
+  </ul>
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-    <a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a>
-    <a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a>
-  </div>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-examples">
+<li><a class="dropdown-item" href="../articles/examples/basic-autograd.html">basic-autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/basic-nn-module.html">basic-nn-module</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/dataset.html">dataset</a></li>
+  </ul>
 </li>
-<li class="nav-item">
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-</li>
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-  <a class="nav-link" href="../news/index.html">Changelog</a>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced">
+<li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul>
 </li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-article">
 
@@ -105,10 +89,11 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Using autograd</h1>
+
+      <h1>Using autograd</h1>
             
       
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/vignettes/using-autograd.Rmd" class="external-link"><code>vignettes/using-autograd.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/vignettes/using-autograd.Rmd" class="external-link"><code>vignettes/using-autograd.Rmd</code></a></small>
       <div class="d-none name"><code>using-autograd.Rmd</code></div>
     </div>
 
@@ -185,33 +170,28 @@ <h2 id="automatic-differentiation-with-autograd">Automatic differentiation with
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># how to compute the gradient for mean, the last operation executed</span></span>
 <span><span class="va">out</span><span class="op">$</span><span class="va">grad_fn</span></span>
-<span><span class="co">#&gt; MeanBackward0</span></span></code></pre></div>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3</span></span>
+<span><span class="co">#&gt; MeanBackward0</span></span>
+<span><span class="co"># how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3</span></span>
 <span><span class="va">out</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
-<span><span class="co">#&gt; MulBackward1</span></span></code></pre></div>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># how to compute the gradient for pow in z = y.pow(2) * 3</span></span>
+<span><span class="co">#&gt; MulBackward1</span></span>
+<span><span class="co"># how to compute the gradient for pow in z = y.pow(2) * 3</span></span>
 <span><span class="va">out</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
-<span><span class="co">#&gt; PowBackward0</span></span></code></pre></div>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># how to compute the gradient for the multiplication in y = x * (x + 2)</span></span>
+<span><span class="co">#&gt; PowBackward0</span></span>
+<span><span class="co"># how to compute the gradient for the multiplication in y = x * (x + 2)</span></span>
 <span><span class="va">out</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
-<span><span class="co">#&gt; MulBackward0</span></span></code></pre></div>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># how to compute the gradient for the two branches of y = x * (x + 2),</span></span>
+<span><span class="co">#&gt; MulBackward0</span></span>
+<span><span class="co"># how to compute the gradient for the two branches of y = x * (x + 2),</span></span>
 <span><span class="co"># where the left branch is a leaf node (AccumulateGrad for x1)</span></span>
 <span><span class="va">out</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
 <span><span class="co">#&gt; torch::autograd::AccumulateGrad</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; [[2]]</span></span>
-<span><span class="co">#&gt; AddBackward1</span></span></code></pre></div>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># here we arrive at the other leaf node (AccumulateGrad for x2)</span></span>
+<span><span class="co">#&gt; AddBackward1</span></span>
+<span><span class="co"># here we arrive at the other leaf node (AccumulateGrad for x2)</span></span>
 <span><span class="va">out</span><span class="op">$</span><span class="va">grad_fn</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">next_functions</span></span>
 <span><span class="co">#&gt; [[1]]</span></span>
 <span><span class="co">#&gt; torch::autograd::AccumulateGrad</span></span></code></pre></div>
@@ -219,26 +199,23 @@ <h2 id="automatic-differentiation-with-autograd">Automatic differentiation with
 will have their respective gradients created. Without our calls to
 <code>retain_grad</code> above, <code>z$grad</code> and
 <code>y$grad</code> would be empty:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">out</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">z</span><span class="op">$</span><span class="va">grad</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  0.2500  0.2500</span></span>
 <span><span class="co">#&gt;  0.2500  0.2500</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">y</span><span class="op">$</span><span class="va">grad</span></span>
+<span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span>
+<span><span class="va">y</span><span class="op">$</span><span class="va">grad</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  4.6500  4.6500</span></span>
 <span><span class="co">#&gt;  4.6500  4.6500</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x2</span><span class="op">$</span><span class="va">grad</span></span>
+<span><span class="co">#&gt; [ CPUFloatType{2,2} ]</span></span>
+<span><span class="va">x2</span><span class="op">$</span><span class="va">grad</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  18.6000</span></span>
-<span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">x1</span><span class="op">$</span><span class="va">grad</span></span>
+<span><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span><span class="va">x1</span><span class="op">$</span><span class="va">grad</span></span>
 <span><span class="co">#&gt; torch_tensor</span></span>
 <span><span class="co">#&gt;  14.4150  14.4150</span></span>
 <span><span class="co">#&gt;  14.4150  14.4150</span></span>
@@ -250,7 +227,7 @@ <h2 id="the-simple-network-now-using-autograd">The simple network, now using aut
 </h2>
 <p>For a single new line calling <code>loss$backward()</code>, now a
 number of lines (that did manual backprop) are gone:</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">### generate training data -----------------------------------------------------</span></span>
 <span><span class="co"># input dimensionality (number of input features)</span></span>
 <span><span class="va">d_in</span> <span class="op">&lt;-</span> <span class="fl">3</span></span>
@@ -306,33 +283,33 @@ <h2 id="the-simple-network-now-using-autograd">The simple network, now using aut
 <span>    <span class="op">}</span><span class="op">)</span></span>
 <span>    </span>
 <span><span class="op">}</span></span>
-<span><span class="co">#&gt; 10 59.5981 </span></span>
-<span><span class="co">#&gt; 20 54.32401 </span></span>
-<span><span class="co">#&gt; 30 49.66991 </span></span>
-<span><span class="co">#&gt; 40 45.58003 </span></span>
-<span><span class="co">#&gt; 50 41.95185 </span></span>
-<span><span class="co">#&gt; 60 38.72104 </span></span>
-<span><span class="co">#&gt; 70 35.83778 </span></span>
-<span><span class="co">#&gt; 80 33.25837 </span></span>
-<span><span class="co">#&gt; 90 30.9521 </span></span>
-<span><span class="co">#&gt; 100 28.87896 </span></span>
-<span><span class="co">#&gt; 110 27.00857 </span></span>
-<span><span class="co">#&gt; 120 25.31741 </span></span>
-<span><span class="co">#&gt; 130 23.78777 </span></span>
-<span><span class="co">#&gt; 140 22.40333 </span></span>
-<span><span class="co">#&gt; 150 21.1463 </span></span>
-<span><span class="co">#&gt; 160 20.00305 </span></span>
-<span><span class="co">#&gt; 170 18.96302 </span></span>
-<span><span class="co">#&gt; 180 18.01377 </span></span>
-<span><span class="co">#&gt; 190 17.14516 </span></span>
-<span><span class="co">#&gt; 200 16.34873</span></span></code></pre></div>
+<span><span class="co">#&gt; 10 28.26874 </span></span>
+<span><span class="co">#&gt; 20 26.89888 </span></span>
+<span><span class="co">#&gt; 30 25.61373 </span></span>
+<span><span class="co">#&gt; 40 24.40569 </span></span>
+<span><span class="co">#&gt; 50 23.26689 </span></span>
+<span><span class="co">#&gt; 60 22.20304 </span></span>
+<span><span class="co">#&gt; 70 21.20444 </span></span>
+<span><span class="co">#&gt; 80 20.26246 </span></span>
+<span><span class="co">#&gt; 90 19.37426 </span></span>
+<span><span class="co">#&gt; 100 18.53972 </span></span>
+<span><span class="co">#&gt; 110 17.75325 </span></span>
+<span><span class="co">#&gt; 120 17.01111 </span></span>
+<span><span class="co">#&gt; 130 16.3092 </span></span>
+<span><span class="co">#&gt; 140 15.64703 </span></span>
+<span><span class="co">#&gt; 150 15.02278 </span></span>
+<span><span class="co">#&gt; 160 14.43382 </span></span>
+<span><span class="co">#&gt; 170 13.87754 </span></span>
+<span><span class="co">#&gt; 180 13.35267 </span></span>
+<span><span class="co">#&gt; 190 12.85716 </span></span>
+<span><span class="co">#&gt; 200 12.3887</span></span></code></pre></div>
 <p>We still manually compute the forward pass, and we still manually
 update the weights. In the last two chapters of this section, we’ll see
 how these parts of the logic can be made more modular and reusable, as
 well.</p>
 </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
 </div>
 
@@ -343,15 +320,15 @@ <h2 id="the-simple-network-now-using-autograd">The simple network, now using aut
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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 </div>
 
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 </html>
diff --git a/authors.html b/authors.html
index eb8f0bfa22..2868a985e5 100644
--- a/authors.html
+++ b/authors.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • torch</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><meta property="og:title" content="Authors and Citation"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   gtag('js', new Date());
@@ -10,136 +7,126 @@
   gtag('config', 'G-9ZJSKW3L0N');
 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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     <a class="navbar-brand me-2" href="index.html">torch</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Authors and Citation</h1>
+
+      <h1>Authors and Citation</h1>
     </div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2>Authors</h2>
-      
+
       <ul class="list-unstyled"><li>
-          <p><strong>Daniel Falbel</strong>. Author, maintainer, copyright holder. 
+          <p><strong>Daniel Falbel</strong>. Author, maintainer, copyright holder.
+          </p>
+        </li>
+        <li>
+          <p><strong>Javier Luraschi</strong>. Author.
           </p>
         </li>
         <li>
-          <p><strong>Javier Luraschi</strong>. Author. 
+          <p><strong>Dmitriy Selivanov</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Dmitriy Selivanov</strong>. Contributor. 
+          <p><strong>Athos Damiani</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Athos Damiani</strong>. Contributor. 
+          <p><strong>Christophe Regouby</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Christophe Regouby</strong>. Contributor. 
+          <p><strong>Krzysztof Joachimiak</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Krzysztof Joachimiak</strong>. Contributor. 
+          <p><strong>Hamada S. Badr</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Hamada S. Badr</strong>. Contributor. 
+          <p><strong>Sebastian Fischer</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong>Sebastian Fischer</strong>. Contributor. 
+          <p><strong>Maximilian Pichler</strong>. Contributor.
           </p>
         </li>
         <li>
-          <p><strong> RStudio</strong>. Copyright holder. 
+          <p><strong> RStudio</strong>. Copyright holder.
           </p>
         </li>
       </ul></div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
+      <p><small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-      <p>Falbel D, Luraschi J (2024).
+      <p>Falbel D, Luraschi J (2025).
 <em>torch: Tensors and Neural Networks with 'GPU' Acceleration</em>.
-R package version 0.13.0, https://github.com/mlverse/torch, <a href="https://torch.mlverse.org/docs">https://torch.mlverse.org/docs</a>. 
+R package version 0.14.0, https://github.com/mlverse/torch, <a href="https://torch.mlverse.org/docs">https://torch.mlverse.org/docs</a>.
 </p>
       <pre>@Manual{,
   title = {torch: Tensors and Neural Networks with 'GPU' Acceleration},
   author = {Daniel Falbel and Javier Luraschi},
-  year = {2024},
-  note = {R package version 0.13.0, https://github.com/mlverse/torch},
+  year = {2025},
+  note = {R package version 0.14.0, https://github.com/mlverse/torch},
   url = {https://torch.mlverse.org/docs},
 }</pre>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +135,14 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/deps/bootstrap-5.3.1/bootstrap.min.css b/deps/bootstrap-5.3.1/bootstrap.min.css
index f44dc15821..dc55de27ad 100644
--- a/deps/bootstrap-5.3.1/bootstrap.min.css
+++ b/deps/bootstrap-5.3.1/bootstrap.min.css
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#212529;--bs-btn-border-color: #212529;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #424649;--bs-btn-hover-border-color: #373b3e;--bs-btn-focus-shadow-rgb: 66,70,73;--bs-btn-active-color: #fff;--bs-btn-active-bg: #4d5154;--bs-btn-active-border-color: #373b3e;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #212529;--bs-btn-disabled-border-color: #212529}.btn-outline-default{--bs-btn-color: #dee2e6;--bs-btn-border-color: #dee2e6;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #dee2e6;--bs-btn-hover-border-color: #dee2e6;--bs-btn-focus-shadow-rgb: 222,226,230;--bs-btn-active-color: #000;--bs-btn-active-bg: #dee2e6;--bs-btn-active-border-color: #dee2e6;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #dee2e6;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #dee2e6;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: #7E1F77;--bs-btn-border-color: #7E1F77;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #7E1F77;--bs-btn-hover-border-color: #7E1F77;--bs-btn-focus-shadow-rgb: 126,31,119;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7E1F77;--bs-btn-active-border-color: #7E1F77;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #7E1F77;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #7E1F77;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6c757d;--bs-btn-hover-border-color: #6c757d;--bs-btn-focus-shadow-rgb: 108,117,125;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6c757d;--bs-btn-active-border-color: #6c757d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6c757d;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: 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transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248,249,250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #212529;--bs-btn-border-color: #212529;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #212529;--bs-btn-hover-border-color: #212529;--bs-btn-focus-shadow-rgb: 33,37,41;--bs-btn-active-color: #fff;--bs-btn-active-bg: #212529;--bs-btn-active-border-color: #212529;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #212529;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #212529;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: var(--bs-link-color);--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: var(--bs-link-hover-color);--bs-btn-hover-border-color: transparent;--bs-btn-active-color: var(--bs-link-hover-color);--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 145,65,139;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: .5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: var(--bs-border-radius-lg)}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: .25rem;--bs-btn-padding-x: 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23320c30'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #bf8fbb;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(126,31,119,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23b279ad'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(126,31,119,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #7E1F77;--bs-pagination-active-border-color: #7E1F77;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #7E1F77;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #7E1F77;--bs-list-group-active-border-color: #7E1F77;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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.show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: .5rem;--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833,37,41,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23320c30'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #bf8fbb;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(126,31,119,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23b279ad'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(126,31,119,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #7E1F77;--bs-pagination-active-border-color: #7E1F77;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #7E1F77;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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diff --git a/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js b/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
new file mode 100644
index 0000000000..c628326a84
--- /dev/null
+++ b/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+!function(a){"use strict";window.Toc={helpers:{findOrFilter:function(e,t){var n=e.find(t);return e.filter(t).add(n).filter(":not([data-toc-skip])")},generateUniqueIdBase:function(e){return a(e).text().trim().replace(/\'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,64).replace(/^-+|-+$/gm,"").toLowerCase()||e.tagName.toLowerCase()},generateUniqueId:function(e){for(var t=this.generateUniqueIdBase(e),n=0;;n++){var r=t;if(0<n&&(r+="-"+n),!document.getElementById(r))return r}},generateAnchor:function(e){if(e.id)return e.id;var t=this.generateUniqueId(e);return e.id=t},createNavList:function(){return a('<ul class="nav navbar-nav"></ul>')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('<a class="nav-link"></a>');n.attr("href","#"+e),n.text(t);var r=a("<li></li>");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1<this.findOrFilter(e,"h"+t).length)return t}return 1},getHeadings:function(e,t){var n="h"+t,r="h"+(t+1);return this.findOrFilter(e,n+","+r)},getNavLevel:function(e){return parseInt(e.tagName.charAt(1),10)},populateNav:function(r,a,e){var i,s=r,c=this;e.each(function(e,t){var n=c.generateNavItem(t);c.getNavLevel(t)===a?s=r:i&&s===r&&(s=c.createChildNavList(i)),s.append(n),i=n})},parseOps:function(e){var t;return(t=e.jquery?{$nav:e}:e).$scope=t.$scope||a(document.body),t}},init:function(e){(e=this.helpers.parseOps(e)).$nav.attr("data-toggle","toc");var t=this.helpers.createChildNavList(e.$nav),n=this.helpers.getTopLevel(e.$scope),r=this.helpers.getHeadings(e.$scope,n);this.helpers.populateNav(t,n,r)}},a(function(){a('nav[data-toggle="toc"]').each(function(e,t){var n=a(t);Toc.init(n)})})}(jQuery);
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diff --git a/deps/clipboard.js-2.0.11/clipboard.min.js b/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 0000000000..1103f811ed
--- /dev/null
+++ b/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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diff --git a/deps/data-deps.txt b/deps/data-deps.txt
index 693b82d1d1..ca4dfd9c5c 100644
--- a/deps/data-deps.txt
+++ b/deps/data-deps.txt
@@ -2,3 +2,12 @@
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
+<link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/deps/font-awesome-6.5.2/css/all.css b/deps/font-awesome-6.5.2/css/all.css
new file mode 100644
index 0000000000..151dd57cb0
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/all.css
@@ -0,0 +1,8028 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, 0));
+          transform: rotate(var(--fa-rotate-angle, 0)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
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+  content: "\f0ea"; }
+
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+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
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+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
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+  content: "\f781"; }
+
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+  content: "\f504"; }
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+  content: "\e539"; }
+
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+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
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+.fa-broom-ball::before {
+  content: "\f458"; }
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+  content: "\f458"; }
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+  content: "\f458"; }
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+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
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+  content: "\f886"; }
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+  content: "\f886"; }
+
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+  content: "\f886"; }
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+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
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+.fa-trowel-bricks::before {
+  content: "\e58a"; }
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+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
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+  content: "\f80d"; }
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+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
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+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
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+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
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+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-table-cells-column-lock::before {
+  content: "\e678"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-table-cells-row-lock::before {
+  content: "\e67a"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
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+
+.fa-facebook-messenger:before {
+  content: "\f39f"; }
+
+.fa-audible:before {
+  content: "\f373"; }
+
+.fa-think-peaks:before {
+  content: "\f731"; }
+
+.fa-bilibili:before {
+  content: "\e3d9"; }
+
+.fa-erlang:before {
+  content: "\f39d"; }
+
+.fa-x-twitter:before {
+  content: "\e61b"; }
+
+.fa-cotton-bureau:before {
+  content: "\f89e"; }
+
+.fa-dashcube:before {
+  content: "\f210"; }
+
+.fa-42-group:before {
+  content: "\e080"; }
+
+.fa-innosoft:before {
+  content: "\e080"; }
+
+.fa-stack-exchange:before {
+  content: "\f18d"; }
+
+.fa-elementor:before {
+  content: "\f430"; }
+
+.fa-square-pied-piper:before {
+  content: "\e01e"; }
+
+.fa-pied-piper-square:before {
+  content: "\e01e"; }
+
+.fa-creative-commons-nd:before {
+  content: "\f4eb"; }
+
+.fa-palfed:before {
+  content: "\f3d8"; }
+
+.fa-superpowers:before {
+  content: "\f2dd"; }
+
+.fa-resolving:before {
+  content: "\f3e7"; }
+
+.fa-xbox:before {
+  content: "\f412"; }
+
+.fa-square-web-awesome-stroke:before {
+  content: "\e684"; }
+
+.fa-searchengin:before {
+  content: "\f3eb"; }
+
+.fa-tiktok:before {
+  content: "\e07b"; }
+
+.fa-square-facebook:before {
+  content: "\f082"; }
+
+.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa-renren:before {
+  content: "\f18b"; }
+
+.fa-linux:before {
+  content: "\f17c"; }
+
+.fa-glide:before {
+  content: "\f2a5"; }
+
+.fa-linkedin:before {
+  content: "\f08c"; }
+
+.fa-hubspot:before {
+  content: "\f3b2"; }
+
+.fa-deploydog:before {
+  content: "\f38e"; }
+
+.fa-twitch:before {
+  content: "\f1e8"; }
+
+.fa-ravelry:before {
+  content: "\f2d9"; }
+
+.fa-mixer:before {
+  content: "\e056"; }
+
+.fa-square-lastfm:before {
+  content: "\f203"; }
+
+.fa-lastfm-square:before {
+  content: "\f203"; }
+
+.fa-vimeo:before {
+  content: "\f40a"; }
+
+.fa-mendeley:before {
+  content: "\f7b3"; }
+
+.fa-uniregistry:before {
+  content: "\f404"; }
+
+.fa-figma:before {
+  content: "\f799"; }
+
+.fa-creative-commons-remix:before {
+  content: "\f4ee"; }
+
+.fa-cc-amazon-pay:before {
+  content: "\f42d"; }
+
+.fa-dropbox:before {
+  content: "\f16b"; }
+
+.fa-instagram:before {
+  content: "\f16d"; }
+
+.fa-cmplid:before {
+  content: "\e360"; }
+
+.fa-upwork:before {
+  content: "\e641"; }
+
+.fa-facebook:before {
+  content: "\f09a"; }
+
+.fa-gripfire:before {
+  content: "\f3ac"; }
+
+.fa-jedi-order:before {
+  content: "\f50e"; }
+
+.fa-uikit:before {
+  content: "\f403"; }
+
+.fa-fort-awesome-alt:before {
+  content: "\f3a3"; }
+
+.fa-phabricator:before {
+  content: "\f3db"; }
+
+.fa-ussunnah:before {
+  content: "\f407"; }
+
+.fa-earlybirds:before {
+  content: "\f39a"; }
+
+.fa-trade-federation:before {
+  content: "\f513"; }
+
+.fa-autoprefixer:before {
+  content: "\f41c"; }
+
+.fa-whatsapp:before {
+  content: "\f232"; }
+
+.fa-square-upwork:before {
+  content: "\e67c"; }
+
+.fa-slideshare:before {
+  content: "\f1e7"; }
+
+.fa-google-play:before {
+  content: "\f3ab"; }
+
+.fa-viadeo:before {
+  content: "\f2a9"; }
+
+.fa-line:before {
+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-square-web-awesome:before {
+  content: "\e683"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-bluesky:before {
+  content: "\e671"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-webflow:before {
+  content: "\e65c"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/deps/font-awesome-6.5.2/css/all.min.css b/deps/font-awesome-6.5.2/css/all.min.css
new file mode 100644
index 0000000000..269bceeaee
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 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diff --git a/deps/font-awesome-6.5.2/css/v4-shims.css b/deps/font-awesome-6.5.2/css/v4-shims.css
new file mode 100644
index 0000000000..ea60ea4d7b
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
+
+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-o:before {
+  content: "\f15b"; }
+
+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
+
+.fa.fa-video-camera:before {
+  content: "\f03d"; }
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+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-picture-o:before {
+  content: "\f03e"; }
+
+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-photo:before {
+  content: "\f03e"; }
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+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-image:before {
+  content: "\f03e"; }
+
+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
+
+.fa.fa-pencil-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
+
+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
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+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
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+.fa.fa-check-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
+
+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
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+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
+  content: "\f058"; }
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+.fa.fa-mail-forward:before {
+  content: "\f064"; }
+
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+  font-weight: 400; }
+
+.fa.fa-modx {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fort-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-usb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-product-hunt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-mixcloud {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-scribd {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/deps/font-awesome-6.5.2/css/v4-shims.min.css b/deps/font-awesome-6.5.2/css/v4-shims.min.css
new file mode 100644
index 0000000000..09baf5fcd4
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-play-circle-o:before{content:"\f144"}.fa.fa-repeat:before,.fa.fa-rotate-right:before{content:"\f01e"}.fa.fa-refresh:before{content:"\f021"}.fa.fa-list-alt{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-list-alt:before{content:"\f022"}.fa.fa-dedent:before{content:"\f03b"}.fa.fa-video-camera:before{content:"\f03d"}.fa.fa-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-picture-o:before{content:"\f03e"}.fa.fa-photo{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-photo:before{content:"\f03e"}.fa.fa-image{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-image:before{content:"\f03e"}.fa.fa-map-marker:before{content:"\f3c5"}.fa.fa-pencil-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pencil-square-o:before{content:"\f044"}.fa.fa-edit{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-edit:before{content:"\f044"}.fa.fa-share-square-o:before{content:"\f14d"}.fa.fa-check-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-square-o:before{content:"\f14a"}.fa.fa-arrows:before{content:"\f0b2"}.fa.fa-times-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-circle-o:before{content:"\f057"}.fa.fa-check-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-circle-o:before{content:"\f058"}.fa.fa-mail-forward:before{content:"\f064"}.fa.fa-expand:before{content:"\f424"}.fa.fa-compress:before{content:"\f422"}.fa.fa-eye,.fa.fa-eye-slash{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-warning:before{content:"\f071"}.fa.fa-calendar:before{content:"\f073"}.fa.fa-arrows-v:before{content:"\f338"}.fa.fa-arrows-h:before{content:"\f337"}.fa.fa-bar-chart-o:before,.fa.fa-bar-chart:before{content:"\e0e3"}.fa.fa-twitter-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-twitter-square:before{content:"\f081"}.fa.fa-facebook-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-square:before{content:"\f082"}.fa.fa-gears:before{content:"\f085"}.fa.fa-thumbs-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-up:before{content:"\f164"}.fa.fa-thumbs-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-down:before{content:"\f165"}.fa.fa-heart-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-heart-o:before{content:"\f004"}.fa.fa-sign-out:before{content:"\f2f5"}.fa.fa-linkedin-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin-square:before{content:"\f08c"}.fa.fa-thumb-tack:before{content:"\f08d"}.fa.fa-external-link:before{content:"\f35d"}.fa.fa-sign-in:before{content:"\f2f6"}.fa.fa-github-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-github-square:before{content:"\f092"}.fa.fa-lemon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lemon-o:before{content:"\f094"}.fa.fa-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-square-o:before{content:"\f0c8"}.fa.fa-bookmark-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bookmark-o:before{content:"\f02e"}.fa.fa-facebook,.fa.fa-twitter{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook:before{content:"\f39e"}.fa.fa-facebook-f{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-f:before{content:"\f39e"}.fa.fa-github{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-feed:before{content:"\f09e"}.fa.fa-hdd-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hdd-o:before{content:"\f0a0"}.fa.fa-hand-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-right:before{content:"\f0a4"}.fa.fa-hand-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-left:before{content:"\f0a5"}.fa.fa-hand-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-up:before{content:"\f0a6"}.fa.fa-hand-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-down:before{content:"\f0a7"}.fa.fa-globe:before{content:"\f57d"}.fa.fa-tasks:before{content:"\f828"}.fa.fa-arrows-alt:before{content:"\f31e"}.fa.fa-group:before{content:"\f0c0"}.fa.fa-chain:before{content:"\f0c1"}.fa.fa-cut:before{content:"\f0c4"}.fa.fa-files-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-files-o:before{content:"\f0c5"}.fa.fa-floppy-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-floppy-o:before{content:"\f0c7"}.fa.fa-save{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-save:before{content:"\f0c7"}.fa.fa-navicon:before,.fa.fa-reorder:before{content:"\f0c9"}.fa.fa-magic:before{content:"\e2ca"}.fa.fa-pinterest,.fa.fa-pinterest-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pinterest-square:before{content:"\f0d3"}.fa.fa-google-plus-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-square:before{content:"\f0d4"}.fa.fa-google-plus{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus:before{content:"\f0d5"}.fa.fa-money:before{content:"\f3d1"}.fa.fa-unsorted:before{content:"\f0dc"}.fa.fa-sort-desc:before{content:"\f0dd"}.fa.fa-sort-asc:before{content:"\f0de"}.fa.fa-linkedin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin:before{content:"\f0e1"}.fa.fa-rotate-left:before{content:"\f0e2"}.fa.fa-legal:before{content:"\f0e3"}.fa.fa-dashboard:before,.fa.fa-tachometer:before{content:"\f625"}.fa.fa-comment-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comment-o:before{content:"\f075"}.fa.fa-comments-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comments-o:before{content:"\f086"}.fa.fa-flash:before{content:"\f0e7"}.fa.fa-clipboard:before{content:"\f0ea"}.fa.fa-lightbulb-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lightbulb-o:before{content:"\f0eb"}.fa.fa-exchange:before{content:"\f362"}.fa.fa-cloud-download:before{content:"\f0ed"}.fa.fa-cloud-upload:before{content:"\f0ee"}.fa.fa-bell-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-o:before{content:"\f0f3"}.fa.fa-cutlery:before{content:"\f2e7"}.fa.fa-file-text-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-text-o:before{content:"\f15c"}.fa.fa-building-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-building-o:before{content:"\f1ad"}.fa.fa-hospital-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hospital-o:before{content:"\f0f8"}.fa.fa-tablet:before{content:"\f3fa"}.fa.fa-mobile-phone:before,.fa.fa-mobile:before{content:"\f3cd"}.fa.fa-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-o:before{content:"\f111"}.fa.fa-mail-reply:before{content:"\f3e5"}.fa.fa-github-alt{font-family:"Font Awesome 6 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+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/deps/headroom-0.11.0/jQuery.headroom.min.js b/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000000..17f70c9e31
--- /dev/null
+++ b/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/deps/search-1.0.0/autocomplete.jquery.min.js b/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000000..fb63a6a282
--- /dev/null
+++ b/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 0===a.hint||!!a.hint,minLength:a.minLength,autoselect:a.autoselect,autoselectOnBlur:a.autoselectOnBlur,tabAutocomplete:a.tabAutocomplete,openOnFocus:a.openOnFocus,templates:a.templates,debug:a.debug,clearOnSelected:a.clearOnSelected,cssClasses:a.cssClasses,datasets:b,keyboardShortcuts:a.keyboardShortcuts,appendTo:a.appendTo,autoWidth:a.autoWidth}),d.data(h,c)}return b=f.isArray(b)?b:[].slice.call(arguments,1),a=a||{},this.each(c)},open:function(){function a(){var a,b=e(this);(a=b.data(h))&&a.open()}return this.each(a)},close:function(){function a(){var a,b=e(this);(a=b.data(h))&&a.close()}return this.each(a)},val:function(a){function b(){var b,c=e(this);(b=c.data(h))&&b.setVal(a)}return arguments.length?this.each(b):function(a){var b,c;return(b=a.data(h))&&(c=b.getVal()),c}(this.first())},destroy:function(){function a(){var a,b=e(this);(a=b.data(h))&&(a.destroy(),b.removeData(h))}return this.each(a)}},e.fn.autocomplete=function(a){var b;return i[a]&&"initialize"!==a?(b=this.filter(function(){return!!e(this).data(h)}),i[a].apply(b,[].slice.call(arguments,1))):i.initialize.apply(this,arguments)},e.fn.autocomplete.noConflict=function(){return e.fn.autocomplete=g,this},e.fn.autocomplete.sources=j.sources,e.fn.autocomplete.escapeHighlightedString=f.escapeHighlightedString,a.exports=e.fn.autocomplete},function(a,b){"use strict";a.exports={element:null}},function(a,b){a.exports=jQuery},function(a,b,c){"use strict";function d(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}var e=c(2);a.exports={isArray:null,isFunction:null,isObject:null,bind:null,each:null,map:null,mixin:null,isMsie:function(a){if(void 0===a&&(a=navigator.userAgent),/(msie|trident)/i.test(a)){var b=a.match(/(msie |rv:)(\d+(.\d+)?)/i);if(b)return b[2]}return!1},escapeRegExChars:function(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")},isNumber:function(a){return"number"==typeof a},toStr:function(a){return void 0===a||null===a?"":a+""},cloneDeep:function(a){var b=this.mixin({},a),c=this;return this.each(b,function(a,d){a&&(c.isArray(a)?b[d]=[].concat(a):c.isObject(a)&&(b[d]=c.cloneDeep(a)))}),b},error:function(a){throw new Error(a)},every:function(a,b){var c=!0;return a?(this.each(a,function(d,e){c&&(c=b.call(null,d,e,a)&&c)}),!!c):c},any:function(a,b){var c=!1;return a?(this.each(a,function(d,e){if(b.call(null,d,e,a))return c=!0,!1}),c):c},getUniqueId:function(){var a=0;return function(){return a++}}(),templatify:function(a){if(this.isFunction(a))return a;var b=e.element(a);return"SCRIPT"===b.prop("tagName")?function(){return b.text()}:function(){return String(a)}},defer:function(a){setTimeout(a,0)},noop:function(){},formatPrefix:function(a,b){return b?"":a+"-"},className:function(a,b,c){return(c?"":".")+a+b},escapeHighlightedString:function(a,b,c){b=b||"<em>";var e=document.createElement("div");e.appendChild(document.createTextNode(b)),c=c||"</em>";var f=document.createElement("div");f.appendChild(document.createTextNode(c));var g=document.createElement("div");return g.appendChild(document.createTextNode(a)),g.innerHTML.replace(RegExp(d(e.innerHTML),"g"),b).replace(RegExp(d(f.innerHTML),"g"),c)}}},function(a,b,c){"use strict";function d(a){var b,c;if(a=a||{},a.input||i.error("missing input"),this.isActivated=!1,this.debug=!!a.debug,this.autoselect=!!a.autoselect,this.autoselectOnBlur=!!a.autoselectOnBlur,this.openOnFocus=!!a.openOnFocus,this.minLength=i.isNumber(a.minLength)?a.minLength:1,this.autoWidth=void 0===a.autoWidth||!!a.autoWidth,this.clearOnSelected=!!a.clearOnSelected,this.tabAutocomplete=void 0===a.tabAutocomplete||!!a.tabAutocomplete,a.hint=!!a.hint,a.hint&&a.appendTo)throw new Error("[autocomplete.js] hint and appendTo options can't be used at the same 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d.Dropdown({appendTo:a.appendTo,wrapper:this.$node,menu:b,datasets:a.datasets,templates:a.templates,cssClasses:a.cssClasses,minLength:this.minLength}).onSync("suggestionClicked",this._onSuggestionClicked,this).onSync("cursorMoved",this._onCursorMoved,this).onSync("cursorRemoved",this._onCursorRemoved,this).onSync("opened",this._onOpened,this).onSync("closed",this._onClosed,this).onSync("shown",this._onShown,this).onSync("empty",this._onEmpty,this).onSync("redrawn",this._onRedrawn,this).onAsync("datasetRendered",this._onDatasetRendered,this),this.input=new 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g=n.dropdown.replace("%PREFIX%",a.cssClasses.prefix).replace("%DROPDOWN_MENU%",a.cssClasses.dropdownMenu);d=j.element(g).css(a.css.dropdown).attr({role:"listbox",id:a.listboxId}),a.templates&&a.templates.dropdownMenu&&d.html(i.templatify(a.templates.dropdownMenu)()),e=b.clone().css(a.css.hint).css(f(b)),e.val("").addClass(i.className(a.cssClasses.prefix,a.cssClasses.hint,!0)).removeAttr("id name placeholder required").prop("readonly",!0).attr({"aria-hidden":"true",autocomplete:"off",spellcheck:"false",tabindex:-1}),e.removeData&&e.removeData(),b.data(h,{"aria-autocomplete":b.attr("aria-autocomplete"),"aria-expanded":b.attr("aria-expanded"),"aria-owns":b.attr("aria-owns"),autocomplete:b.attr("autocomplete"),dir:b.attr("dir"),role:b.attr("role"),spellcheck:b.attr("spellcheck"),style:b.attr("style"),type:b.attr("type")}),b.addClass(i.className(a.cssClasses.prefix,a.cssClasses.input,!0)).attr({autocomplete:"off",spellcheck:!1,role:"combobox","aria-autocomplete":a.datasets&&a.datasets[0]&&a.datasets[0].displayKey?"both":"list","aria-expanded":"false","aria-label":a.ariaLabel,"aria-owns":a.listboxId}).css(a.hint?a.css.input:a.css.inputWithNoHint);try{b.attr("dir")||b.attr("dir","auto")}catch(a){}return c=a.appendTo?c.appendTo(j.element(a.appendTo).eq(0)).eq(0):b.wrap(c).parent(),c.prepend(a.hint?e:null).append(d),{wrapper:c,input:b,hint:e,menu:d}}function f(a){return{backgroundAttachment:a.css("background-attachment"),backgroundClip:a.css("background-clip"),backgroundColor:a.css("background-color"),backgroundImage:a.css("background-image"),backgroundOrigin:a.css("background-origin"),backgroundPosition:a.css("background-position"),backgroundRepeat:a.css("background-repeat"),backgroundSize:a.css("background-size")}}function g(a,b){var c=a.find(i.className(b.prefix,b.input));i.each(c.data(h),function(a,b){void 0===a?c.removeAttr(b):c.attr(b,a)}),c.detach().removeClass(i.className(b.prefix,b.input,!0)).insertAfter(a),c.removeData&&c.removeData(h),a.remove()}var h="aaAttrs",i=c(4),j=c(2),k=c(6),l=c(7),m=c(16),n=c(18),o=c(19);i.mixin(d.prototype,{_bindKeyboardShortcuts:function(a){if(a.keyboardShortcuts){var b=this.$input,c=[];i.each(a.keyboardShortcuts,function(a){"string"==typeof a&&(a=a.toUpperCase().charCodeAt(0)),c.push(a)}),j.element(document).keydown(function(a){var 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c=this.dropdown.getDatumForCursor(),d=this.dropdown.getCurrentCursor().attr("id");this.input.setActiveDescendant(d),c&&(b&&this.input.setInputValue(c.value,!0),this.eventBus.trigger("cursorchanged",c.raw,c.datasetName))},_onCursorRemoved:function(){this.input.resetInputValue(),this._updateHint(),this.eventBus.trigger("cursorremoved")},_onDatasetRendered:function(){this._updateHint(),this.eventBus.trigger("updated")},_onOpened:function(){this._updateHint(),this.input.expand(),this.eventBus.trigger("opened")},_onEmpty:function(){this.eventBus.trigger("empty")},_onRedrawn:function(){this.$node.css("top","0px"),this.$node.css("left","0px");var a=this.$input[0].getBoundingClientRect();this.autoWidth&&this.$node.css("width",a.width+"px");var b=this.$node[0].getBoundingClientRect(),c=a.bottom-b.top;this.$node.css("top",c+"px");var d=a.left-b.left;this.$node.css("left",d+"px"),this.eventBus.trigger("redrawn")},_onShown:function(){this.eventBus.trigger("shown"),this.autoselect&&this.dropdown.cursorTopSuggestion()},_onClosed:function(){this.input.clearHint(),this.input.removeActiveDescendant(),this.input.collapse(),this.eventBus.trigger("closed")},_onFocused:function(){if(this.isActivated=!0,this.openOnFocus){var a=this.input.getQuery();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty(),this.dropdown.open()}},_onBlurred:function(){var a,b;a=this.dropdown.getDatumForCursor(),b=this.dropdown.getDatumForTopSuggestion();var c={selectionMethod:"blur"};this.debug||(this.autoselectOnBlur&&a?this._select(a,c):this.autoselectOnBlur&&b?this._select(b,c):(this.isActivated=!1,this.dropdown.empty(),this.dropdown.close()))},_onEnterKeyed:function(a,b){var c,d;c=this.dropdown.getDatumForCursor(),d=this.dropdown.getDatumForTopSuggestion();var 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+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
\ No newline at end of file
diff --git a/deps/search-1.0.0/fuse.min.js b/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000000..7def5985ba
--- /dev/null
+++ b/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){i(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function a(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),t&&u(e,t)}function s(e){return(s=Object.setPrototypeOf?Object.getPrototypeOf:function(e){return e.__proto__||Object.getPrototypeOf(e)})(e)}function u(e,t){return(u=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e})(e,t)}function h(e,t){return!t||"object"!=typeof t&&"function"!=typeof t?function(e){if(void 0===e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}(e):t}function f(e){var t=function(){if("undefined"==typeof 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\ No newline at end of file
diff --git a/deps/search-1.0.0/mark.min.js b/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000000..1eea053389
--- /dev/null
+++ b/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 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diff --git a/index.html b/index.html
index 03170abd3b..02899ae591 100644
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 <meta property="og:title" content="Tensors and Neural Networks with GPU Acceleration">
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@@ -27,15 +24,15 @@
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-    <a class="dropdown-item" href="articles/using-autograd.html">Using autograd</a>
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-    <a class="dropdown-item" href="articles/python-to-r.html">Python models</a>
-  </div>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/installation.html">Installation</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Tensors</h6></li>
+    <li><a class="dropdown-item" href="articles/tensor-creation.html">Creating tensors</a></li>
+    <li><a class="dropdown-item" href="articles/indexing.html">Indexing</a></li>
+    <li><a class="dropdown-item" href="articles/tensor/index.html">Tensor class</a></li>
+    <li><a class="dropdown-item" href="articles/serialization.html">Serialization</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Datasets</h6></li>
+    <li><a class="dropdown-item" href="articles/loading-data.html">Loading Data</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Autograd</h6></li>
+    <li><a class="dropdown-item" href="articles/using-autograd.html">Using autograd</a></li>
+    <li><a class="dropdown-item" href="articles/extending-autograd.html">Extending autograd</a></li>
+    <li><a class="dropdown-item" href="articles/python-to-r.html">Python models</a></li>
+    <li><a class="dropdown-item" href="articles/torchscript.html">Jit Compilation</a></li>
+  </ul>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-examples">
+<li><a class="dropdown-item" href="articles/examples/basic-autograd.html">basic-autograd</a></li>
+    <li><a class="dropdown-item" href="articles/examples/basic-nn-module.html">basic-nn-module</a></li>
+    <li><a class="dropdown-item" href="articles/examples/dataset.html">dataset</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced">
+<li><a class="dropdown-item" href="articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul>
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+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html">Changelog</a></li>
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-<form class="form-inline my-2 my-lg-0" role="search">
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+<ul class="navbar-nav">
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@@ -217,15 +203,15 @@ <h2 data-toc-skip>Developers</h2>
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/katex-auto.js b/katex-auto.js
new file mode 100644
index 0000000000..20651d9fdc
--- /dev/null
+++ b/katex-auto.js
@@ -0,0 +1,14 @@
+// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345
+document.addEventListener("DOMContentLoaded", function () {
+  var mathElements = document.getElementsByClassName("math");
+  var macros = [];
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+    }}});
diff --git a/lightswitch.js b/lightswitch.js
new file mode 100644
index 0000000000..9467125ae2
--- /dev/null
+++ b/lightswitch.js
@@ -0,0 +1,85 @@
+
+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
+ * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0.
+ */
+
+const getStoredTheme = () => localStorage.getItem('theme')
+const setStoredTheme = theme => localStorage.setItem('theme', theme)
+
+const getPreferredTheme = () => {
+  const storedTheme = getStoredTheme()
+  if (storedTheme) {
+    return storedTheme
+  }
+
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'
+}
+
+const setTheme = theme => {
+  if (theme === 'auto') {
+    document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'))
+  } else {
+    document.documentElement.setAttribute('data-bs-theme', theme)
+  }
+}
+
+function bsSetupThemeToggle () {
+  'use strict'
+
+  const showActiveTheme = (theme, focus = false) => {
+    var activeLabel, activeIcon;
+
+    document.querySelectorAll('[data-bs-theme-value]').forEach(element => {
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+      const isActive = buttonTheme == theme
+      
+      element.classList.toggle('active', isActive)
+      element.setAttribute('aria-pressed', isActive)
+
+      if (isActive) {
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+        activeIcon = element.querySelector('span').classList.value;
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+    const themeSwitcher = document.querySelector('#dropdown-lightswitch')
+    if (!themeSwitcher) {
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+
+    themeSwitcher.setAttribute('aria-label', activeLabel)
+    themeSwitcher.querySelector('span').classList.value = activeIcon;
+
+    if (focus) {
+      themeSwitcher.focus()
+    }
+  }
+
+  window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+    const storedTheme = getStoredTheme()
+    if (storedTheme !== 'light' && storedTheme !== 'dark') {
+      setTheme(getPreferredTheme())
+    }
+  })
+
+  window.addEventListener('DOMContentLoaded', () => {
+    showActiveTheme(getPreferredTheme())
+
+    document
+      .querySelectorAll('[data-bs-theme-value]')
+      .forEach(toggle => {
+        toggle.addEventListener('click', () => {
+          const theme = toggle.getAttribute('data-bs-theme-value')
+          setTheme(theme)
+          setStoredTheme(theme)
+          showActiveTheme(theme, true)
+        })
+      })
+  })
+}
+
+setTheme(getPreferredTheme());
+bsSetupThemeToggle();
diff --git a/news/index.html b/news/index.html
index bb43b39749..9f0cf153cd 100644
--- a/news/index.html
+++ b/news/index.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Changelog"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Changelog"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,79 +7,84 @@
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+
 
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Changelog</h1>
-      <small>Source: <a href="https://github.com/mlverse/torch/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
+
+      <h1>Changelog</h1>
+      <small>Source: <a href="https://github.com/mlverse/torch/blob/main/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.14.0" id="torch-0140">torch 0.14.0<a class="anchor" aria-label="anchor" href="#torch-0140"></a></h2><p class="text-muted">CRAN release: 2025-01-30</p>
+<div class="section level3">
+<h3 id="breaking-changes-0-14-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-14-0"></a></h3>
+<ul><li>Updated to LibTorch v2.5.1 (<a href="https://github.com/mlverse/torch/issues/1204" class="external-link">#1204</a>) – potentially breaking change!</li>
+</ul></div>
+<div class="section level3">
+<h3 id="new-features-0-14-0">New features<a class="anchor" aria-label="anchor" href="#new-features-0-14-0"></a></h3>
+<ul><li>Feature: Faster optimizers (<code>optim_ignite_&lt;name&gt;()</code>) are available: Adam, AdamW, Adagrad, RMSprop,SGD. These can be used as drop-in replacements for <code>optim_&lt;name&gt;</code> but are considerably faster as they wrap the LibTorch implementation of the optimizer. The biggest speed differences can be observed for complex optimizers such as <code>AdamW</code>.</li>
+</ul></div>
+<div class="section level3">
+<h3 id="bug-fixes-0-14-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-14-0"></a></h3>
+<ul><li>
+<code><a href="../reference/torch_iinfo.html">torch_iinfo()</a></code> now support all integer dtypes (<a href="https://github.com/mlverse/torch/issues/1190" class="external-link">#1190</a> <a href="https://github.com/cregouby" class="external-link">@cregouby</a>)</li>
+<li>Fixed float key_padding_mask in <code><a href="../reference/nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> (<a href="https://github.com/mlverse/torch/issues/1205" class="external-link">#1205</a>)</li>
+<li>Fix french translation (<a href="https://github.com/mlverse/torch/issues/1176" class="external-link">#1176</a> <a href="https://github.com/cregouby" class="external-link">@cregouby</a>)</li>
+<li>Trace jitted modules now respect ‘train’ and ‘eval’ mode (<a href="https://github.com/mlverse/torch/issues/1211" class="external-link">#1211</a>)</li>
+<li>Fix: Avoid name clashes between multiple calls to <code>jit_trace</code> (<a href="https://github.com/mlverse/torch/issues/1246" class="external-link">#1246</a>)</li>
+</ul></div>
+</div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.13.0" id="torch-0130">torch 0.13.0<a class="anchor" aria-label="anchor" href="#torch-0130"></a></h2><p class="text-muted">CRAN release: 2024-05-21</p>
 <div class="section level3">
@@ -614,7 +616,7 @@ <h3 id="documentation-0-2-1">Documentation<a class="anchor" aria-label="anchor"
 <h2 class="pkg-version" data-toc-text="0.2.0" id="torch-020">torch 0.2.0<a class="anchor" aria-label="anchor" href="#torch-020"></a></h2><p class="text-muted">CRAN release: 2020-12-15</p>
 <div class="section level3">
 <h3 id="breaking-changes-0-2-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-2-0"></a></h3>
-<ul><li>Dataloaders now returns a <code><a href="https://rdrr.io/pkg/coro/man/iterator.html" class="external-link">coro::exhausted</a></code> intead of raising <code>stop_iteration_error</code> when the dataloader exceeds. (<a href="https://github.com/mlverse/torch/issues/366" class="external-link">#366</a>)</li>
+<ul><li>Dataloaders now returns a <code><a href="https://coro.r-lib.org/reference/iterator.html" class="external-link">coro::exhausted</a></code> intead of raising <code>stop_iteration_error</code> when the dataloader exceeds. (<a href="https://github.com/mlverse/torch/issues/366" class="external-link">#366</a>)</li>
 <li>Fixed bug that would happen with functions that need to transform tensors from 0-based to 1-based in the GPU. (<a href="https://github.com/mlverse/torch/issues/317" class="external-link">#317</a>)</li>
 <li>Fixed <code>torch_argsort</code> and <code>x$argsort</code> to return 1-based indexes (<a href="https://github.com/mlverse/torch/issues/342" class="external-link">#342</a>)</li>
 <li>Fixed <code>torch_argmax</code>, <code>torch_argmin</code>, <code>x$argmax()</code> and <code>x$argmin()</code> return 1-based indexes. (<a href="https://github.com/mlverse/torch/issues/389" class="external-link">#389</a>)</li>
@@ -703,7 +705,7 @@ <h2 class="pkg-version" data-toc-text="0.0.2" id="torch-002">torch 0.0.2<a class
 <ul><li>Added a <code>NEWS.md</code> file to track changes to the package.</li>
 <li>Auto install when loading the package for the first time.</li>
 </ul></div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -712,14 +714,14 @@ <h2 class="pkg-version" data-toc-text="0.0.2" id="torch-002">torch 0.0.2<a class
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/pkgdown.js b/pkgdown.js
index 9bd6621ee5..1a99c65f5c 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -153,4 +153,10 @@ async function searchFuse(query, callback) {
   });
 })(window.jQuery || window.$)
 
-
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/pkgdown.yml b/pkgdown.yml
index 0bf1fe3651..51c158d5ac 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,27 +1,26 @@
 pandoc: 3.1.11
-pkgdown: 2.0.9
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
-  amp: amp.html
-  basic-autograd: examples/basic-autograd.html
-  basic-nn-module: examples/basic-nn-module.html
-  dataset: examples/dataset.html
+  articles/amp: amp.html
+  examples/basic-autograd: examples/basic-autograd.html
+  examples/basic-nn-module: examples/basic-nn-module.html
+  examples/dataset: examples/dataset.html
   distributions: distributions.html
   extending-autograd: extending-autograd.html
-  index: examples/index.html
-  index: tensor/index.html
+  examples/index: examples/index.html
+  tensor/index: tensor/index.html
   indexing: indexing.html
   installation: installation.html
   loading-data: loading-data.html
-  memory-management: memory-management.html
-  modifying-source-code: modifying-source-code.html
+  articles/memory-management: memory-management.html
+  articles/modifying-source-code: modifying-source-code.html
   python-to-r: python-to-r.html
   serialization: serialization.html
   tensor-creation: tensor-creation.html
   torchscript: torchscript.html
   using-autograd: using-autograd.html
-last_built: 2024-05-22T16:59Z
+last_built: 2025-01-30T21:27Z
 urls:
   reference: https://torch.mlverse.org/docs/reference
   article: https://torch.mlverse.org/docs/articles
-
diff --git a/reference/AutogradContext.html b/reference/AutogradContext.html
index 17ff26eca9..9ee9dbc3a8 100644
--- a/reference/AutogradContext.html
+++ b/reference/AutogradContext.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-Class representing the context."><title>Class representing the context. — AutogradContext • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Class representing the context. — AutogradContext"><meta property="og:description" content="Class representing the context.
-Class representing the context."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Class representing the context. — AutogradContext • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Class representing the context. — AutogradContext"><meta name="description" content="Class representing the context.
+Class representing the context."><meta property="og:description" content="Class representing the context.
+Class representing the context."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
   window.dataLayer = window.dataLayer || [];
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
@@ -12,77 +9,62 @@
   gtag('config', 'G-9ZJSKW3L0N');
 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Class representing the context.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Class representing the context.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>AutogradContext.Rd</code></div>
     </div>
 
@@ -114,7 +96,7 @@ <h2 id="active-bindings">Active bindings<a class="anchor" aria-label="anchor" hr
     </div>
     <div class="section level2">
     <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
-    
+
 <div class="section">
 <h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
 
@@ -243,7 +225,7 @@ <h4 id="arguments-4">Arguments<a class="anchor" aria-label="anchor" href="#argum
 
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/Constraint.html b/reference/Constraint.html
index 4207d9f9bc..2788337ed5 100644
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@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Abstract base class for constraints.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-constraints.R" class="external-link"><code>R/distributions-constraints.R</code></a></small>
+
+      <h1>Abstract base class for constraints.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-constraints.R" class="external-link"><code>R/distributions-constraints.R</code></a></small>
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diff --git a/reference/Distribution.html b/reference/Distribution.html
index bb60c3c9cb..470f9eee0f 100644
--- a/reference/Distribution.html
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@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Distribution is the abstract base class for probability distributions.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generic R6 class representing distributions — Distribution • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Generic R6 class representing distributions — Distribution"><meta name="description" content="Distribution is the abstract base class for probability distributions.
 Note: in Python, adding torch.Size objects works as concatenation
-Try for example: torch.Size((2, 1)) + torch.Size((1,))"><title>Generic R6 class representing distributions — Distribution • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Generic R6 class representing distributions — Distribution"><meta property="og:description" content="Distribution is the abstract base class for probability distributions.
+Try for example: torch.Size((2, 1)) + torch.Size((1,))"><meta property="og:description" content="Distribution is the abstract base class for probability distributions.
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+
+      <h1>Generic R6 class representing distributions</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions.R" class="external-link"><code>R/distributions.R</code></a></small>
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@@ -147,7 +129,7 @@ <h2 id="active-bindings">Active bindings<a class="anchor" aria-label="anchor" hr
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+
+      <h1>Abstract Base Class for LibTorch Optimizers</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>OptimizerIgnite.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Abstract base class for wrapping LibTorch C++ optimizers.</p>
+    </div>
+
+
+    <div class="section level2">
+    <h2 id="super-class">Super class<a class="anchor" aria-label="anchor" href="#super-class"></a></h2>
+    <p><code>torch::torch_optimizer</code> -&gt; <code>OptimizerIgnite</code></p>
+    </div>
+    <div class="section level2">
+    <h2 id="methods">Methods<a class="anchor" aria-label="anchor" href="#methods"></a></h2>
+
+<div class="section">
+<h3 id="public-methods">Public methods<a class="anchor" aria-label="anchor" href="#public-methods"></a></h3>
+
+<ul><li><p><a href="#method-OptimizerIgnite-new"><code>OptimizerIgnite$new()</code></a></p></li>
+<li><p><a href="#method-OptimizerIgnite-state_dict"><code>OptimizerIgnite$state_dict()</code></a></p></li>
+<li><p><a href="#method-OptimizerIgnite-load_state_dict"><code>OptimizerIgnite$load_state_dict()</code></a></p></li>
+<li><p><a href="#method-OptimizerIgnite-step"><code>OptimizerIgnite$step()</code></a></p></li>
+<li><p><a href="#method-OptimizerIgnite-zero_grad"><code>OptimizerIgnite$zero_grad()</code></a></p></li>
+<li><p><a href="#method-OptimizerIgnite-add_param_group"><code>OptimizerIgnite$add_param_group()</code></a></p></li>
+<li><p><a href="#method-OptimizerIgnite-clone"><code>OptimizerIgnite$clone()</code></a></p></li>
+</ul></div><p></p><hr><a id="method-OptimizerIgnite-new"></a><div class="section">
+<h3 id="method-new-">Method <code>new()</code><a class="anchor" aria-label="anchor" href="#method-new-"></a></h3>
+<p>Initializes the optimizer with the specified parameters and defaults.</p><div class="section">
+<h4 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va"><a href="../reference/OptimizerIgnite.html">OptimizerIgnite</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="va">params</span>, <span class="va">defaults</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>params</code></dt>
+<dd><p>(<code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>)<br>
+Either a list of tensors or a list of parameter groups, each containing the <code>params</code> to optimizer
+as well as the optimizer options such as the learning rate, weight decay, etc.</p></dd>
+
+
+<dt><code>defaults</code></dt>
+<dd><p>(<code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>)<br>
+A list of default optimizer options.</p></dd>
+
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-OptimizerIgnite-state_dict"></a><div class="section">
+<h3 id="method-state-dict-">Method <code>state_dict()</code><a class="anchor" aria-label="anchor" href="#method-state-dict-"></a></h3>
+<p>Returns the state dictionary containing the current state of the optimizer.
+The returned <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code> contains two lists:</p><ul><li><p><code>param_groups</code>: The parameter groups of the optimizer (<code>lr</code>, ...) as well as to which
+parameters they are applied (<code>params</code>, integer indices)</p></li>
+<li><p><code>state</code>: The states of the optimizer. The names are the indices of the parameters to which
+they belong, converted to character.</p></li>
+</ul><div class="section">
+<h4 id="usage-1">Usage<a class="anchor" aria-label="anchor" href="#usage-1"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">OptimizerIgnite</span><span class="op">$</span><span class="fu">state_dict</span><span class="op">(</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="returns">Returns<a class="anchor" aria-label="anchor" href="#returns"></a></h4>
+<p>(<code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>)</p>
+</div>
+
+</div><p></p><hr><a id="method-OptimizerIgnite-load_state_dict"></a><div class="section">
+<h3 id="method-load-state-dict-">Method <code><a href="load_state_dict.html">load_state_dict()</a></code><a class="anchor" aria-label="anchor" href="#method-load-state-dict-"></a></h3>
+<p>Loads the state dictionary into the optimizer.</p><div class="section">
+<h4 id="usage-2">Usage<a class="anchor" aria-label="anchor" href="#usage-2"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">OptimizerIgnite</span><span class="op">$</span><span class="fu">load_state_dict</span><span class="op">(</span><span class="va">state_dict</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-1">Arguments<a class="anchor" aria-label="anchor" href="#arguments-1"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>state_dict</code></dt>
+<dd><p>(<code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>)<br>
+The state dictionary to load into the optimizer.</p></dd>
+
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-OptimizerIgnite-step"></a><div class="section">
+<h3 id="method-step-">Method <code><a href="https://rdrr.io/r/stats/step.html" class="external-link">step()</a></code><a class="anchor" aria-label="anchor" href="#method-step-"></a></h3>
+<p>Performs a single optimization step.</p><div class="section">
+<h4 id="usage-3">Usage<a class="anchor" aria-label="anchor" href="#usage-3"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">OptimizerIgnite</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span>closure <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-2">Arguments<a class="anchor" aria-label="anchor" href="#arguments-2"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>closure</code></dt>
+<dd><p>(<code>function()</code>)<br>
+A closure that conducts the forward pass and returns the loss.</p></dd>
+
+
+</dl><p></p></div>
+</div>
+<div class="section">
+<h4 id="returns-1">Returns<a class="anchor" aria-label="anchor" href="#returns-1"></a></h4>
+<p>(<code><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric()</a></code>)<br>
+The loss.</p>
+</div>
+
+</div><p></p><hr><a id="method-OptimizerIgnite-zero_grad"></a><div class="section">
+<h3 id="method-zero-grad-">Method <code>zero_grad()</code><a class="anchor" aria-label="anchor" href="#method-zero-grad-"></a></h3>
+<p>Zeros out the gradients of the parameters.</p><div class="section">
+<h4 id="usage-4">Usage<a class="anchor" aria-label="anchor" href="#usage-4"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">OptimizerIgnite</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+
+</div><p></p><hr><a id="method-OptimizerIgnite-add_param_group"></a><div class="section">
+<h3 id="method-add-param-group-">Method <code>add_param_group()</code><a class="anchor" aria-label="anchor" href="#method-add-param-group-"></a></h3>
+<p>Adds a new parameter group to the optimizer.</p><div class="section">
+<h4 id="usage-5">Usage<a class="anchor" aria-label="anchor" href="#usage-5"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">OptimizerIgnite</span><span class="op">$</span><span class="fu">add_param_group</span><span class="op">(</span><span class="va">param_group</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-3">Arguments<a class="anchor" aria-label="anchor" href="#arguments-3"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>param_group</code></dt>
+<dd><p>(<code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>)<br>
+A parameter group to add to the optimizer.
+This should contain the <code>params</code> to optimize as well as the optimizer options.
+For all options that are not specified, the defaults are used.</p></dd>
+
+
+</dl><p></p></div>
+</div>
+
+</div><p></p><hr><a id="method-OptimizerIgnite-clone"></a><div class="section">
+<h3 id="method-clone-">Method <code>clone()</code><a class="anchor" aria-label="anchor" href="#method-clone-"></a></h3>
+<p>The objects of this class are cloneable with this method.</p><div class="section">
+<h4 id="usage-6">Usage<a class="anchor" aria-label="anchor" href="#usage-6"></a></h4>
+<p></p><div class="r"><div class="sourceCode"><pre><code><span><span class="va">OptimizerIgnite</span><span class="op">$</span><span class="fu">clone</span><span class="op">(</span>deep <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div><p></p></div>
+</div>
+
+<div class="section">
+<h4 id="arguments-4">Arguments<a class="anchor" aria-label="anchor" href="#arguments-4"></a></h4>
+<p></p><div class="arguments"><dl><dt><code>deep</code></dt>
+<dd><p>Whether to make a deep clone.</p></dd>
+
+
+</dl><p></p></div>
+</div>
+
+</div>
+
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
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+
+<div class="pkgdown-footer-right">
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+
diff --git a/reference/Rplot001.png b/reference/Rplot001.png
deleted file mode 100644
index 17a358060a..0000000000
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diff --git a/reference/as_array.html b/reference/as_array.html
index 21386d2ef3..64abb2ab14 100644
--- a/reference/as_array.html
+++ b/reference/as_array.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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-      <img src="" class="logo" alt=""><h1>Converts to array</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
+
+      <h1>Converts to array</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
       <div class="d-none name"><code>as_array.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>object to be converted into an array</p></dd>
 
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -109,14 +93,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/as_iterator.html b/reference/as_iterator.html
new file mode 100644
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@@ -0,0 +1,8 @@
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diff --git a/reference/autograd_backward.html b/reference/autograd_backward.html
index fbfc0db14e..8f7f8579bd 100644
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+++ b/reference/autograd_backward.html
@@ -1,22 +1,19 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The graph is differentiated using the chain rule. If any of tensors are
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward"><meta name="description" content="The graph is differentiated using the chain rule. If any of tensors are
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 usually the gradient of the differentiated function w.r.t. corresponding
 tensors (None is an acceptable value for all tensors that don’t need gradient
-tensors)."><title>Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward"><meta property="og:description" content="The graph is differentiated using the chain rule. If any of tensors are
+tensors)."><meta property="og:description" content="The graph is differentiated using the chain rule. If any of tensors are
 non-scalar (i.e. their data has more than one element) and require gradient,
 then the Jacobian-vector product would be computed, in this case the function
 additionally requires specifying grad_tensors. It should be a sequence of
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 usually the gradient of the differentiated function w.r.t. corresponding
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>autograd_backward.Rd</code></div>
     </div>
 
@@ -121,24 +103,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(list of Tensor) – Tensors of which the derivative will
 be computed.</p></dd>
 
 
-<dt>grad_tensors</dt>
+<dt id="arg-grad-tensors">grad_tensors<a class="anchor" aria-label="anchor" href="#arg-grad-tensors"></a></dt>
 <dd><p>(list of (Tensor or <code>NULL)) – The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or ones that don’t require grad. If a</code>NULL` value would be acceptable for all
 grad_tensors, then this argument is optional.</p></dd>
 
 
-<dt>retain_graph</dt>
+<dt id="arg-retain-graph">retain_graph<a class="anchor" aria-label="anchor" href="#arg-retain-graph"></a></dt>
 <dd><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
 the grad will be freed. Note that in nearly all cases setting this option to
 <code>TRUE</code> is not needed and often can be worked around in a much more efficient
 way. Defaults to the value of <code>create_graph</code>.</p></dd>
 
 
-<dt>create_graph</dt>
+<dt id="arg-create-graph">create_graph<a class="anchor" aria-label="anchor" href="#arg-create-graph"></a></dt>
 <dd><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
 be constructed, allowing to compute higher order derivative products.
 Defaults to <code>FALSE</code>.</p></dd>
@@ -163,7 +147,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -172,14 +156,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/autograd_function.html b/reference/autograd_function.html
index 2e3af3ad9b..fef74e26b1 100644
--- a/reference/autograd_function.html
+++ b/reference/autograd_function.html
@@ -1,18 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Every operation performed on Tensor's creates a new function object, that
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Records operation history and defines formulas for differentiating ops. — autograd_function • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Records operation history and defines formulas for differentiating ops. — autograd_function"><meta name="description" content="Every operation performed on Tensor's creates a new function object, that
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 retained in the form of a DAG of functions, with edges denoting data
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 processed in the topological ordering, by calling backward() methods of each
-Function object, and passing returned gradients on to next Function's."><title>Records operation history and defines formulas for differentiating ops. — autograd_function • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Records operation history and defines formulas for differentiating ops. — autograd_function"><meta property="og:description" content="Every operation performed on Tensor's creates a new function object, that
+Function object, and passing returned gradients on to next Function's."><meta property="og:description" content="Every operation performed on Tensor's creates a new function object, that
 performs the computation, and records that it happened. The history is
 retained in the form of a DAG of functions, with edges denoting data
 dependencies (input &amp;lt;- output). Then, when backward is called, the graph is
 processed in the topological ordering, by calling backward() methods of each
-Function object, and passing returned gradients on to next Function's."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Records operation history and defines formulas for differentiating ops.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Records operation history and defines formulas for differentiating ops.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>autograd_function.Rd</code></div>
     </div>
 
@@ -110,14 +92,16 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>forward</dt>
+
+
+<dl><dt id="arg-forward">forward<a class="anchor" aria-label="anchor" href="#arg-forward"></a></dt>
 <dd><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
 followed by any number of arguments (tensors or other types). The context can be
 used to store tensors that can be then retrieved during the backward pass.
 See <a href="AutogradContext.html">AutogradContext</a> for more information about context methods.</p></dd>
 
 
-<dt>backward</dt>
+<dt id="arg-backward">backward<a class="anchor" aria-label="anchor" href="#arg-backward"></a></dt>
 <dd><p>Defines a formula for differentiating the operation. It must accept
 a context <code>ctx</code> as the first argument, followed by as many outputs ad <code>forward()</code>
 returned (as a <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>). The names of the arguments don't matter and they are passed
@@ -150,7 +134,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/autograd_grad.html b/reference/autograd_grad.html
index 7a15163327..f87f804764 100644
--- a/reference/autograd_grad.html
+++ b/reference/autograd_grad.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="grad_outputs should be a list of length matching output containing the “vector”
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad"><meta name="description" content="grad_outputs should be a list of length matching output containing the “vector”
 in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
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+the outputs. If an output doesn’t require_grad, then the gradient can be None)."><meta property="og:description" content="grad_outputs should be a list of length matching output containing the “vector”
 in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
-the outputs. If an output doesn’t require_grad, then the gradient can be None)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>autograd_grad.Rd</code></div>
     </div>
 
@@ -108,34 +90,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>outputs</dt>
+
+
+<dl><dt id="arg-outputs">outputs<a class="anchor" aria-label="anchor" href="#arg-outputs"></a></dt>
 <dd><p>(sequence of Tensor) – outputs of the differentiated function.</p></dd>
 
 
-<dt>inputs</dt>
+<dt id="arg-inputs">inputs<a class="anchor" aria-label="anchor" href="#arg-inputs"></a></dt>
 <dd><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
 returned (and not accumulated into .grad).</p></dd>
 
 
-<dt>grad_outputs</dt>
+<dt id="arg-grad-outputs">grad_outputs<a class="anchor" aria-label="anchor" href="#arg-grad-outputs"></a></dt>
 <dd><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
 product. Usually gradients w.r.t. each output. None values can be specified for
 scalar Tensors or ones that don’t require grad. If a None value would be acceptable
 for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></dd>
 
 
-<dt>retain_graph</dt>
+<dt id="arg-retain-graph">retain_graph<a class="anchor" aria-label="anchor" href="#arg-retain-graph"></a></dt>
 <dd><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
 grad will be freed. Note that in nearly all cases setting this option to <code>TRUE</code> is
 not needed and often can be worked around in a much more efficient way.
 Defaults to the value of <code>create_graph</code>.</p></dd>
 
 
-<dt>create_graph</dt>
+<dt id="arg-create-graph">create_graph<a class="anchor" aria-label="anchor" href="#arg-create-graph"></a></dt>
 <dd><p>(bool, optional) – If <code>TRUE, graph of the derivative will be constructed, allowing to compute higher order derivative products. Default: </code>FALSE`.</p></dd>
 
 
-<dt>allow_unused</dt>
+<dt id="arg-allow-unused">allow_unused<a class="anchor" aria-label="anchor" href="#arg-allow-unused"></a></dt>
 <dd><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
 not used when computing outputs (and therefore their grad is always zero) is an
 error. Defaults to <code>FALSE</code></p></dd>
@@ -173,7 +157,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -182,14 +166,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/autograd_set_grad_mode.html b/reference/autograd_set_grad_mode.html
index 179646a617..7bfcc9602a 100644
--- a/reference/autograd_set_grad_mode.html
+++ b/reference/autograd_set_grad_mode.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Set grad mode</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Set grad mode</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>autograd_set_grad_mode.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>enabled</dt>
+
+
+<dl><dt id="arg-enabled">enabled<a class="anchor" aria-label="anchor" href="#arg-enabled"></a></dt>
 <dd><p>bool wether to enable or disable the gradient recording.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -109,14 +93,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/backends_cudnn_is_available.html b/reference/backends_cudnn_is_available.html
index d6b717631f..b5d9906751 100644
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+++ b/reference/backends_cudnn_is_available.html
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index 041585aaa9..f201e54b18 100644
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diff --git a/reference/backends_mkl_is_available.html b/reference/backends_mkl_is_available.html
index cfbfb3ee4f..5111e15c21 100644
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+++ b/reference/backends_mkl_is_available.html
@@ -1,8 +1,5 @@
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@@ -95,12 +77,10 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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diff --git a/reference/backends_mkldnn_is_available.html b/reference/backends_mkldnn_is_available.html
index 66a89cec8b..5e9bb40f32 100644
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-      <img src="" class="logo" alt=""><h1>MKLDNN is available</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/backends.R" class="external-link"><code>R/backends.R</code></a></small>
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       <div class="d-none name"><code>backends_mkldnn_is_available.Rd</code></div>
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@@ -95,12 +77,10 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-    
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-<p>Returns whether LibTorch is built with MKL-DNN support.</p>
+    <p>Returns whether LibTorch is built with MKL-DNN support.</p>
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diff --git a/reference/backends_mps_is_available.html b/reference/backends_mps_is_available.html
index 906f645d4d..a336ff8825 100644
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+++ b/reference/backends_mps_is_available.html
@@ -1,8 +1,5 @@
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/backends.R" class="external-link"><code>R/backends.R</code></a></small>
+
+      <h1>MPS is available</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/backends.R" class="external-link"><code>R/backends.R</code></a></small>
       <div class="d-none name"><code>backends_mps_is_available.Rd</code></div>
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@@ -95,12 +77,10 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns whether LibTorch is built with MPS support.</p>
+    <p>Returns whether LibTorch is built with MPS support.</p>
     </div>
 
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@@ -109,14 +89,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
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index 3488aceb28..08c8f26ec2 100644
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+++ b/reference/backends_openmp_is_available.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>OpenMP is available</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/backends.R" class="external-link"><code>R/backends.R</code></a></small>
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@@ -95,12 +77,10 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    
-
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diff --git a/reference/broadcast_all.html b/reference/broadcast_all.html
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-      <img src="" class="logo" alt=""><h1>Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules:</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-utils.R" class="external-link"><code>R/distributions-utils.R</code></a></small>
+
+      <h1>Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules:</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-utils.R" class="external-link"><code>R/distributions-utils.R</code></a></small>
       <div class="d-none name"><code>broadcast_all.Rd</code></div>
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@@ -104,7 +86,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>values</dt>
+
+
+<dl><dt id="arg-values">values<a class="anchor" aria-label="anchor" href="#arg-values"></a></dt>
 <dd><p>List of:</p><ul><li><p><code>torch.*Tensor</code> instances are broadcasted as per <code>_broadcasting-semantics</code>.</p></li>
 <li><p><code>numeric</code> instances (scalars) are upcast to tensors having
 the same size and type as the first tensor passed to <code>values</code>.  If all the
@@ -114,7 +98,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
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diff --git a/reference/buffer_from_torch_tensor.html b/reference/buffer_from_torch_tensor.html
new file mode 100644
index 0000000000..8c5b7d51ef
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index b0e0c82358..f1e1840078 100644
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@@ -1,20 +1,17 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function allows calling a function prefixed with torch_, including unexported
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 may be helpful to read the torch source code for help with this, as well as
 the documentation for the corresponding function in the Pytorch C++ API. Generally
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 may be helpful to read the torch source code for help with this, as well as
 the documentation for the corresponding function in the Pytorch C++ API. Generally
-for development and advanced use only."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Call a (Potentially Unexported) Torch Function</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/call_torch_function.R" class="external-link"><code>R/call_torch_function.R</code></a></small>
+
+      <h1>Call a (Potentially Unexported) Torch Function</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/call_torch_function.R" class="external-link"><code>R/call_torch_function.R</code></a></small>
       <div class="d-none name"><code>call_torch_function.Rd</code></div>
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@@ -113,27 +95,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>name</dt>
+
+
+<dl><dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>Name of the function to call as a string. Should start with "torch_"</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>A list of arguments to pass to the function. Argument splicing with
 <code>!!!</code> is supported.</p></dd>
 
 
-<dt>quiet</dt>
+<dt id="arg-quiet">quiet<a class="anchor" aria-label="anchor" href="#arg-quiet"></a></dt>
 <dd><p>If TRUE, suppress warnings with valuable information about the dangers of
 this function.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The return value from calling the function <code>name</code> with arguments <code>...</code></p>
-
-
+    <p>The return value from calling the function <code>name</code> with arguments <code>...</code></p>
     </div>
 
     <div class="section level2">
@@ -161,7 +141,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -170,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/clone_module.html b/reference/clone_module.html
index c2cb2b239a..3cecc0aef3 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Clone a torch module.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Clone a torch module.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
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@@ -95,22 +77,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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 The module to clone</p></dd>
 
 
-<dt>deep</dt>
+<dt id="arg-deep">deep<a class="anchor" aria-label="anchor" href="#arg-deep"></a></dt>
 <dd><p>(<code>logical(1)</code>)<br>
 Whether to create a deep clone.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>(any)<br>
 Additional parameters, currently unused.</p></dd>
 
 
-<dt>replace_values</dt>
+<dt id="arg-replace-values">replace_values<a class="anchor" aria-label="anchor" href="#arg-replace-values"></a></dt>
 <dd><p>(<code>logical(1)</code>)<br>
 Whether to replace parameters and buffers with the cloned values.</p></dd>
 
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> • bias: Float [1:1]</span>
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diff --git a/reference/contrib_sort_vertices.html b/reference/contrib_sort_vertices.html
index 366e08f7ee..bb831a54f1 100644
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+++ b/reference/contrib_sort_vertices.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Contrib sort vertices</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/contrib.R" class="external-link"><code>R/contrib.R</code></a></small>
+
+      <h1>Contrib sort vertices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/contrib.R" class="external-link"><code>R/contrib.R</code></a></small>
       <div class="d-none name"><code>contrib_sort_vertices.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>vertices</dt>
+
+
+<dl><dt id="arg-vertices">vertices<a class="anchor" aria-label="anchor" href="#arg-vertices"></a></dt>
 <dd><p>A Tensor with the vertices.</p></dd>
 
 
-<dt>mask</dt>
+<dt id="arg-mask">mask<a class="anchor" aria-label="anchor" href="#arg-mask"></a></dt>
 <dd><p>A tensors containing the masks.</p></dd>
 
 
-<dt>num_valid</dt>
+<dt id="arg-num-valid">num_valid<a class="anchor" aria-label="anchor" href="#arg-num-valid"></a></dt>
 <dd><p>A integer tensors.</p></dd>
 
 </dl></div>
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/cuda_amp_grad_scaler.html b/reference/cuda_amp_grad_scaler.html
index 6a5da47496..de32be4606 100644
--- a/reference/cuda_amp_grad_scaler.html
+++ b/reference/cuda_amp_grad_scaler.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Creates a gradient scaler</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autocast.R" class="external-link"><code>R/autocast.R</code></a></small>
+
+      <h1>Creates a gradient scaler</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autocast.R" class="external-link"><code>R/autocast.R</code></a></small>
       <div class="d-none name"><code>cuda_amp_grad_scaler.Rd</code></div>
     </div>
 
@@ -104,34 +86,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>init_scale</dt>
+
+
+<dl><dt id="arg-init-scale">init_scale<a class="anchor" aria-label="anchor" href="#arg-init-scale"></a></dt>
 <dd><p>a numeric value indicating the initial scale factor.</p></dd>
 
 
-<dt>growth_factor</dt>
+<dt id="arg-growth-factor">growth_factor<a class="anchor" aria-label="anchor" href="#arg-growth-factor"></a></dt>
 <dd><p>a numeric value indicating the growth factor.</p></dd>
 
 
-<dt>backoff_factor</dt>
+<dt id="arg-backoff-factor">backoff_factor<a class="anchor" aria-label="anchor" href="#arg-backoff-factor"></a></dt>
 <dd><p>a numeric value indicating the backoff factor.</p></dd>
 
 
-<dt>growth_interval</dt>
+<dt id="arg-growth-interval">growth_interval<a class="anchor" aria-label="anchor" href="#arg-growth-interval"></a></dt>
 <dd><p>a numeric value indicating the growth interval.</p></dd>
 
 
-<dt>enabled</dt>
+<dt id="arg-enabled">enabled<a class="anchor" aria-label="anchor" href="#arg-enabled"></a></dt>
 <dd><p>a logical value indicating whether the gradient scaler should be enabled.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A gradient scaler object.</p>
+    <p>A gradient scaler object.</p>
     </div>
 
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -140,14 +122,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
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diff --git a/reference/cuda_current_device.html b/reference/cuda_current_device.html
index 67771ec497..da498c2d02 100644
--- a/reference/cuda_current_device.html
+++ b/reference/cuda_current_device.html
@@ -1,8 +1,5 @@
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index c5f7b4d3b3..b537a25f1c 100644
--- a/reference/cuda_empty_cache.html
+++ b/reference/cuda_empty_cache.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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@@ -103,7 +85,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 cases. See Memory management article for more details about GPU memory management.</p>
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diff --git a/reference/cuda_get_device_capability.html b/reference/cuda_get_device_capability.html
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Returns the major and minor CUDA capability of <code>device</code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+
+      <h1>Returns the major and minor CUDA capability of <code>device</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
       <div class="d-none name"><code>cuda_get_device_capability.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>device</dt>
+
+
+<dl><dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>Integer value of the CUDA device to return capabilities of.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/cuda_get_rng_state.html b/reference/cuda_get_rng_state.html
new file mode 100644
index 0000000000..2637dbbdf2
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diff --git a/reference/cuda_is_available.html b/reference/cuda_is_available.html
index 693093fa21..d288bdf02f 100644
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-      <img src="" class="logo" alt=""><h1>Returns a bool indicating if CUDA is currently available.</h1>
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diff --git a/reference/cuda_memory_stats.html b/reference/cuda_memory_stats.html
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-      <img src="" class="logo" alt=""><h1>Returns a dictionary of CUDA memory allocator statistics for a given device.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+
+      <h1>Returns a dictionary of CUDA memory allocator statistics for a given device.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
       <div class="d-none name"><code>cuda_memory_stats.Rd</code></div>
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@@ -100,13 +82,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>device</dt>
+
+
+<dl><dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>Integer value of the CUDA device to return capabilities of.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="core-statistics">Core statistics<a class="anchor" aria-label="anchor" href="#core-statistics"></a></h2>
-    
+
 
 <ul><li><p>"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}": number of allocation requests received by the memory allocator.</p></li>
 <li><p>"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}": amount of allocated memory.</p></li>
@@ -127,13 +111,13 @@ <h2 id="core-statistics">Core statistics<a class="anchor" aria-label="anchor" hr
 </ul></div>
     <div class="section level2">
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-    
+
 
 <ul><li><p>"num_alloc_retries": number of failed cudaMalloc calls that result in a cache flush and retry.</p></li>
 <li><p>"num_ooms": number of out-of-memory errors thrown.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/cuda_memory_summary.html b/reference/cuda_memory_summary.html
new file mode 100644
index 0000000000..ff875e73d7
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index 6e7b2a89f2..623428a893 100644
--- a/reference/cuda_runtime_version.html
+++ b/reference/cuda_runtime_version.html
@@ -1,8 +1,5 @@
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Waits for all kernels in all streams on a CUDA device to complete.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
+
+      <h1>Waits for all kernels in all streams on a CUDA device to complete.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/cuda.R" class="external-link"><code>R/cuda.R</code></a></small>
       <div class="d-none name"><code>cuda_synchronize.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>device</dt>
+
+
+<dl><dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>device for which to synchronize. It uses the current device
 given by <code><a href="cuda_current_device.html">cuda_current_device()</a></code> if no device is specified.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -110,14 +94,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 4726d03e9c..b563b72eac 100644
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@@ -1,10 +1,7 @@
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader"><meta name="description" content="Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset."><meta property="og:description" content="Data loader. Combines a dataset and a sampler, and provides
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
+
+      <h1>Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
       <div class="d-none name"><code>dataloader.Rd</code></div>
     </div>
 
@@ -112,69 +94,71 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dataset</dt>
+
+
+<dl><dt id="arg-dataset">dataset<a class="anchor" aria-label="anchor" href="#arg-dataset"></a></dt>
 <dd><p>(Dataset): dataset from which to load the data.</p></dd>
 
 
-<dt>batch_size</dt>
+<dt id="arg-batch-size">batch_size<a class="anchor" aria-label="anchor" href="#arg-batch-size"></a></dt>
 <dd><p>(int, optional): how many samples per batch to load
 (default: <code>1</code>).</p></dd>
 
 
-<dt>shuffle</dt>
+<dt id="arg-shuffle">shuffle<a class="anchor" aria-label="anchor" href="#arg-shuffle"></a></dt>
 <dd><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
 at every epoch (default: <code>FALSE</code>).</p></dd>
 
 
-<dt>sampler</dt>
+<dt id="arg-sampler">sampler<a class="anchor" aria-label="anchor" href="#arg-sampler"></a></dt>
 <dd><p>(Sampler, optional): defines the strategy to draw samples from
 the dataset. If specified, <code>shuffle</code> must be False. Custom samplers can be
 created with <code><a href="sampler.html">sampler()</a></code>.</p></dd>
 
 
-<dt>batch_sampler</dt>
+<dt id="arg-batch-sampler">batch_sampler<a class="anchor" aria-label="anchor" href="#arg-batch-sampler"></a></dt>
 <dd><p>(Sampler, optional): like sampler, but returns a batch of
 indices at a time. Mutually exclusive with <code>batch_size</code>,
 <code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>. Custom samplers can be created with
 <code><a href="sampler.html">sampler()</a></code>.</p></dd>
 
 
-<dt>num_workers</dt>
+<dt id="arg-num-workers">num_workers<a class="anchor" aria-label="anchor" href="#arg-num-workers"></a></dt>
 <dd><p>(int, optional): how many subprocesses to use for data
 loading. 0 means that the data will be loaded in the main process.
 (default: <code>0</code>)</p></dd>
 
 
-<dt>collate_fn</dt>
+<dt id="arg-collate-fn">collate_fn<a class="anchor" aria-label="anchor" href="#arg-collate-fn"></a></dt>
 <dd><p>(callable, optional): merges a list of samples to form a mini-batch.</p></dd>
 
 
-<dt>pin_memory</dt>
+<dt id="arg-pin-memory">pin_memory<a class="anchor" aria-label="anchor" href="#arg-pin-memory"></a></dt>
 <dd><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
 into CUDA pinned memory before returning them.  If your data elements
 are a custom type, or your <code>collate_fn</code> returns a batch that is a custom type
 see the example below.</p></dd>
 
 
-<dt>drop_last</dt>
+<dt id="arg-drop-last">drop_last<a class="anchor" aria-label="anchor" href="#arg-drop-last"></a></dt>
 <dd><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
 if the dataset size is not divisible by the batch size. If <code>FALSE</code> and
 the size of dataset is not divisible by the batch size, then the last batch
 will be smaller. (default: <code>FALSE</code>)</p></dd>
 
 
-<dt>timeout</dt>
+<dt id="arg-timeout">timeout<a class="anchor" aria-label="anchor" href="#arg-timeout"></a></dt>
 <dd><p>(numeric, optional): if positive, the timeout value for collecting a batch
 from workers. -1 means no timeout. (default: <code>-1</code>)</p></dd>
 
 
-<dt>worker_init_fn</dt>
+<dt id="arg-worker-init-fn">worker_init_fn<a class="anchor" aria-label="anchor" href="#arg-worker-init-fn"></a></dt>
 <dd><p>(callable, optional): If not <code>NULL</code>, this will be called on each
 worker subprocess with the worker id (an int in <code>[1, num_workers]</code>) as
 input, after seeding and before data loading. (default: <code>NULL</code>)</p></dd>
 
 
-<dt>worker_globals</dt>
+<dt id="arg-worker-globals">worker_globals<a class="anchor" aria-label="anchor" href="#arg-worker-globals"></a></dt>
 <dd><p>(list or character vector, optional) only used when
 <code>num_workers &gt; 0</code>. If a character vector, then objects with those names are
 copied from the global environment to the workers. If a named list, then
@@ -182,7 +166,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 that the objects are copied only once at the worker initialization.</p></dd>
 
 
-<dt>worker_packages</dt>
+<dt id="arg-worker-packages">worker_packages<a class="anchor" aria-label="anchor" href="#arg-worker-packages"></a></dt>
 <dd><p>(character vector, optional) Only used if <code>num_workers &gt; 0</code>
 optional character vector naming packages that should be loaded in
 each worker.</p></dd>
@@ -190,7 +174,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
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 <p>When using <code>num_workers &gt; 0</code> data loading will happen in parallel for each
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index 740e1ef4fe..5786a75cbb 100644
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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diff --git a/reference/dataloader_next.html b/reference/dataloader_next.html
index d8b975c2e7..d08ded6c92 100644
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+++ b/reference/dataloader_next.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Get the next element of a dataloader iterator</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
+
+      <h1>Get the next element of a dataloader iterator</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
       <div class="d-none name"><code>dataloader_next.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>iter</dt>
+
+
+<dl><dt id="arg-iter">iter<a class="anchor" aria-label="anchor" href="#arg-iter"></a></dt>
 <dd><p>a DataLoader iter created with <a href="dataloader_make_iter.html">dataloader_make_iter</a>.</p></dd>
 
 
-<dt>completed</dt>
+<dt id="arg-completed">completed<a class="anchor" aria-label="anchor" href="#arg-completed"></a></dt>
 <dd><p>the returned value when the iterator is exhausted.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -113,14 +97,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/dataset.html b/reference/dataset.html
index c396dbb2bb..064abbed40 100644
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+++ b/reference/dataset.html
@@ -1,18 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="All datasets that represent a map from keys to data samples should subclass this
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Helper function to create an function that generates R6 instances of class dataset — dataset • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Helper function to create an function that generates R6 instances of class dataset — dataset"><meta name="description" content="All datasets that represent a map from keys to data samples should subclass this
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 fetching a data sample for a given key. Subclasses could also optionally
 overwrite .length(), which is expected to return the size of the dataset
 (e.g. number of samples) used by many sampler implementations
-and the default options of dataloader()."><title>Helper function to create an function that generates R6 instances of class dataset — dataset • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Helper function to create an function that generates R6 instances of class dataset — dataset"><meta property="og:description" content="All datasets that represent a map from keys to data samples should subclass this
+and the default options of dataloader()."><meta property="og:description" content="All datasets that represent a map from keys to data samples should subclass this
 class. All subclasses should overwrite the .getitem() method, which supports
 fetching a data sample for a given key. Subclasses could also optionally
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 (e.g. number of samples) used by many sampler implementations
-and the default options of dataloader()."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Helper function to create an function that generates R6 instances of class <code>dataset</code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data.R" class="external-link"><code>R/utils-data.R</code></a></small>
+
+      <h1>Helper function to create an function that generates R6 instances of class <code>dataset</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data.R" class="external-link"><code>R/utils-data.R</code></a></small>
       <div class="d-none name"><code>dataset.Rd</code></div>
     </div>
 
@@ -117,41 +99,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>name</dt>
+
+
+<dl><dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>a name for the dataset. It it's also used as the class
 for it.</p></dd>
 
 
-<dt>inherit</dt>
+<dt id="arg-inherit">inherit<a class="anchor" aria-label="anchor" href="#arg-inherit"></a></dt>
 <dd><p>you can optionally inherit from a dataset when creating a
 new dataset.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>public methods for the dataset class</p></dd>
 
 
-<dt>private</dt>
+<dt id="arg-private">private<a class="anchor" aria-label="anchor" href="#arg-private"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 
-<dt>active</dt>
+<dt id="arg-active">active<a class="anchor" aria-label="anchor" href="#arg-active"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 
-<dt>parent_env</dt>
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
 <dd><p>An environment to use as the parent of newly-created
 objects.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The output is a function <code>f</code> with class <code>dataset_generator</code>. Calling <code>f()</code></p>
-
-
-<p>creates a new instance of the R6 class <code>dataset</code>. The R6 class is stored in the
+    <p>The output is a function <code>f</code> with class <code>dataset_generator</code>. Calling <code>f()</code>
+creates a new instance of the R6 class <code>dataset</code>. The R6 class is stored in the
 enclosing environment of <code>f</code> and can also be accessed through <code>f</code>s attribute
 <code>Dataset</code>.</p>
     </div>
@@ -163,7 +143,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="get-a-batch-of-observations">Get a batch of observations<a class="anchor" aria-label="anchor" href="#get-a-batch-of-observations"></a></h2>
-    
+
 
 
 <p>By default datasets are iterated by returning each observation/item individually.
@@ -178,7 +158,7 @@ <h2 id="get-a-batch-of-observations">Get a batch of observations<a class="anchor
 For more on this see the the <code><a href="../articles/loading-data.html">vignette("loading-data")</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -187,14 +167,14 @@ <h2 id="get-a-batch-of-observations">Get a batch of observations<a class="anchor
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/dataset_subset.html b/reference/dataset_subset.html
index 045370cffc..b65ffb0e4d 100644
--- a/reference/dataset_subset.html
+++ b/reference/dataset_subset.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Subset of a dataset at specified indices."><title>Dataset Subset — dataset_subset • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dataset Subset — dataset_subset"><meta property="og:description" content="Subset of a dataset at specified indices."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dataset Subset — dataset_subset • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dataset Subset — dataset_subset"><meta name="description" content="Subset of a dataset at specified indices."><meta property="og:description" content="Subset of a dataset at specified indices."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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+
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Dataset Subset</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data.R" class="external-link"><code>R/utils-data.R</code></a></small>
+
+      <h1>Dataset Subset</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data.R" class="external-link"><code>R/utils-data.R</code></a></small>
       <div class="d-none name"><code>dataset_subset.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dataset</dt>
+
+
+<dl><dt id="arg-dataset">dataset<a class="anchor" aria-label="anchor" href="#arg-dataset"></a></dt>
 <dd><p>(Dataset): The whole Dataset</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>(sequence): Indices in the whole set selected for subset</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -113,14 +97,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/default_dtype.html b/reference/default_dtype.html
index 95a427b4cd..f59a1520ae 100644
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+++ b/reference/default_dtype.html
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-      <img src="" class="logo" alt=""><h1>Gets and sets the default floating point dtype.</h1>
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-    <dl><dt>d</dt>
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 <dd><p>The default floating point dtype to set. Initially set to
 <code><a href="torch_dtype.html">torch_float()</a></code>.</p></dd>
 
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diff --git a/reference/distr_bernoulli.html b/reference/distr_bernoulli.html
index de23a62f27..730ab7f1f1 100644
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 or logits (but not both).
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a Bernoulli distribution parameterized by <code>probs</code> or <code>logits</code> (but not both). Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code> and <code>0</code> with probability <code>1 - p</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-bernoulli.R" class="external-link"><code>R/distributions-bernoulli.R</code></a></small>
+
+      <h1>Creates a Bernoulli distribution parameterized by <code>probs</code> or <code>logits</code> (but not both). Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code> and <code>0</code> with probability <code>1 - p</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-bernoulli.R" class="external-link"><code>R/distributions-bernoulli.R</code></a></small>
       <div class="d-none name"><code>distr_bernoulli.Rd</code></div>
     </div>
 
@@ -104,22 +86,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>probs</dt>
+
+
+<dl><dt id="arg-probs">probs<a class="anchor" aria-label="anchor" href="#arg-probs"></a></dt>
 <dd><p>(numeric or torch_tensor): the probability of sampling <code>1</code></p></dd>
 
 
-<dt>logits</dt>
+<dt id="arg-logits">logits<a class="anchor" aria-label="anchor" href="#arg-logits"></a></dt>
 <dd><p>(numeric or torch_tensor): the log-odds of sampling <code>1</code></p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>whether to validate arguments or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
-<p>Other distributions: 
+<p>Other distributions:
 <code><a href="distr_chi2.html">distr_chi2</a>()</code>,
 <code><a href="distr_gamma.html">distr_gamma</a>()</code>,
 <code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
@@ -134,11 +118,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># 30% chance 1; 70% chance 0</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -147,14 +131,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
+
+
 
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diff --git a/reference/distr_categorical.html b/reference/distr_categorical.html
index 32c3a9748d..2ec2de790f 100644
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+++ b/reference/distr_categorical.html
@@ -1,10 +1,7 @@
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a categorical distribution parameterized by either <code>probs</code> or <code>logits</code> (but not both).</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-categorical.R" class="external-link"><code>R/distributions-categorical.R</code></a></small>
+
+      <h1>Creates a categorical distribution parameterized by either <code>probs</code> or <code>logits</code> (but not both).</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-categorical.R" class="external-link"><code>R/distributions-categorical.R</code></a></small>
       <div class="d-none name"><code>distr_categorical.Rd</code></div>
     </div>
 
@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>probs</dt>
+
+
+<dl><dt id="arg-probs">probs<a class="anchor" aria-label="anchor" href="#arg-probs"></a></dt>
 <dd><p>(Tensor): event probabilities</p></dd>
 
 
-<dt>logits</dt>
+<dt id="arg-logits">logits<a class="anchor" aria-label="anchor" href="#arg-logits"></a></dt>
 <dd><p>(Tensor): event log probabilities (unnormalized)</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>Additional arguments</p></dd>
 
 </dl></div>
@@ -136,11 +120,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># equal probability of 1,2,3,4</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
+
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diff --git a/reference/distr_chi2.html b/reference/distr_chi2.html
index b08cdbb227..72b18947e6 100644
--- a/reference/distr_chi2.html
+++ b/reference/distr_chi2.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5)"><title>Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2 • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2"><meta property="og:description" content="Creates a Chi2 distribution parameterized by shape parameter df.
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2 • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2"><meta name="description" content="Creates a Chi2 distribution parameterized by shape parameter df.
+This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5)"><meta property="og:description" content="Creates a Chi2 distribution parameterized by shape parameter df.
+This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5)"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>. This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-chi2.R" class="external-link"><code>R/distributions-chi2.R</code></a></small>
+
+      <h1>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>. This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-chi2.R" class="external-link"><code>R/distributions-chi2.R</code></a></small>
       <div class="d-none name"><code>distr_chi2.Rd</code></div>
     </div>
 
@@ -98,18 +80,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>df</dt>
+
+
+<dl><dt id="arg-df">df<a class="anchor" aria-label="anchor" href="#arg-df"></a></dt>
 <dd><p>(float or torch_tensor): shape parameter of the distribution</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>whether to validate arguments or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
-<p>Other distributions: 
+<p>Other distributions:
 <code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
 <code><a href="distr_gamma.html">distr_gamma</a>()</code>,
 <code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +122,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/distr_gamma.html b/reference/distr_gamma.html
index fa1101aaf3..a042267e75 100644
--- a/reference/distr_gamma.html
+++ b/reference/distr_gamma.html
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-      <img src="" class="logo" alt=""><h1>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-gamma.R" class="external-link"><code>R/distributions-gamma.R</code></a></small>
+
+      <h1>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-gamma.R" class="external-link"><code>R/distributions-gamma.R</code></a></small>
       <div class="d-none name"><code>distr_gamma.Rd</code></div>
     </div>
 
@@ -95,24 +77,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>concentration</dt>
+
+
+<dl><dt id="arg-concentration">concentration<a class="anchor" aria-label="anchor" href="#arg-concentration"></a></dt>
 <dd><p>(float or Tensor): shape parameter of the distribution
 (often referred to as alpha)</p></dd>
 
 
-<dt>rate</dt>
+<dt id="arg-rate">rate<a class="anchor" aria-label="anchor" href="#arg-rate"></a></dt>
 <dd><p>(float or Tensor): rate = 1 / scale of the distribution
 (often referred to as beta)</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>whether to validate arguments or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
-<p>Other distributions: 
+<p>Other distributions:
 <code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
 <code><a href="distr_chi2.html">distr_chi2</a>()</code>,
 <code><a href="distr_multivariate_normal.html">distr_multivariate_normal</a>()</code>,
@@ -127,11 +111,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># Gamma distributed with concentration=1 and rate=1</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0188</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7523</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/distr_mixture_same_family.html b/reference/distr_mixture_same_family.html
index 7fb4638ed7..318e054d2b 100644
--- a/reference/distr_mixture_same_family.html
+++ b/reference/distr_mixture_same_family.html
@@ -1,18 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='The MixtureSameFamily distribution implements a (batch of) mixture
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Mixture of components in the same family — distr_mixture_same_family • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Mixture of components in the same family — distr_mixture_same_family"><meta name="description" content='The MixtureSameFamily distribution implements a (batch of) mixture
 distribution where all component are from different parameterizations of
 the same distribution type. It is parameterized by a Categorical
 selecting distribution" (over k component) and a component
 distribution, i.e., a Distribution with a rightmost batch shape
-(equal to [k]) which indexes each (batch of) component.'><title>Mixture of components in the same family — distr_mixture_same_family • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Mixture of components in the same family — distr_mixture_same_family"><meta property="og:description" content='The MixtureSameFamily distribution implements a (batch of) mixture
+(equal to [k]) which indexes each (batch of) component.'><meta property="og:description" content='The MixtureSameFamily distribution implements a (batch of) mixture
 distribution where all component are from different parameterizations of
 the same distribution type. It is parameterized by a Categorical
 selecting distribution" (over k component) and a component
 distribution, i.e., a Distribution with a rightmost batch shape
-(equal to [k]) which indexes each (batch of) component.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mixture of components in the same family</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-mixture_same_family.R" class="external-link"><code>R/distributions-mixture_same_family.R</code></a></small>
+
+      <h1>Mixture of components in the same family</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-mixture_same_family.R" class="external-link"><code>R/distributions-mixture_same_family.R</code></a></small>
       <div class="d-none name"><code>distr_mixture_same_family.Rd</code></div>
     </div>
 
@@ -114,7 +96,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mixture_distribution</dt>
+
+
+<dl><dt id="arg-mixture-distribution">mixture_distribution<a class="anchor" aria-label="anchor" href="#arg-mixture-distribution"></a></dt>
 <dd><p><code>torch_distributions.Categorical</code>-like
 instance. Manages the probability of selecting component.
 The number of categories must match the rightmost batch
@@ -123,12 +107,12 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>component_distribution.batch_shape[:-1]</code></p></dd>
 
 
-<dt>component_distribution</dt>
+<dt id="arg-component-distribution">component_distribution<a class="anchor" aria-label="anchor" href="#arg-component-distribution"></a></dt>
 <dd><p><code>torch_distributions.Distribution</code>-like
 instance. Right-most batch dimension indexes component.</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>Additional arguments</p></dd>
 
 </dl></div>
@@ -144,7 +128,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -153,14 +137,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/distr_multivariate_normal.html b/reference/distr_multivariate_normal.html
index 1cd5b1ae02..329797106c 100644
--- a/reference/distr_multivariate_normal.html
+++ b/reference/distr_multivariate_normal.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a multivariate normal (also called Gaussian) distribution
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+parameterized by a mean vector and a covariance matrix."><meta property="og:description" content="Creates a multivariate normal (also called Gaussian) distribution
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Gaussian distribution</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-multivariate_normal.R" class="external-link"><code>R/distributions-multivariate_normal.R</code></a></small>
+
+      <h1>Gaussian distribution</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-multivariate_normal.R" class="external-link"><code>R/distributions-multivariate_normal.R</code></a></small>
       <div class="d-none name"><code>distr_multivariate_normal.Rd</code></div>
     </div>
 
@@ -104,23 +86,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>loc</dt>
+
+
+<dl><dt id="arg-loc">loc<a class="anchor" aria-label="anchor" href="#arg-loc"></a></dt>
 <dd><p>(Tensor): mean of the distribution</p></dd>
 
 
-<dt>covariance_matrix</dt>
+<dt id="arg-covariance-matrix">covariance_matrix<a class="anchor" aria-label="anchor" href="#arg-covariance-matrix"></a></dt>
 <dd><p>(Tensor): positive-definite covariance matrix</p></dd>
 
 
-<dt>precision_matrix</dt>
+<dt id="arg-precision-matrix">precision_matrix<a class="anchor" aria-label="anchor" href="#arg-precision-matrix"></a></dt>
 <dd><p>(Tensor): positive-definite precision matrix</p></dd>
 
 
-<dt>scale_tril</dt>
+<dt id="arg-scale-tril">scale_tril<a class="anchor" aria-label="anchor" href="#arg-scale-tril"></a></dt>
 <dd><p>(Tensor): lower-triangular factor of covariance, with positive-valued diagonal</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>Bool wether to validate the arguments or not.</p></dd>
 
 </dl></div>
@@ -146,7 +130,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
-<p>Other distributions: 
+<p>Other distributions:
 <code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
 <code><a href="distr_chi2.html">distr_chi2</a>()</code>,
 <code><a href="distr_gamma.html">distr_gamma</a>()</code>,
@@ -161,12 +145,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># normally distributed with mean=`[0,0]` and covariance_matrix=`I`</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2418</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1116</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7218</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1566</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -175,14 +159,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/distr_normal.html b/reference/distr_normal.html
index c0a1e2efa8..8867659735 100644
--- a/reference/distr_normal.html
+++ b/reference/distr_normal.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a normal (also called Gaussian) distribution parameterized by
-loc and scale."><title>Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal"><meta property="og:description" content="Creates a normal (also called Gaussian) distribution parameterized by
-loc and scale."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal"><meta name="description" content="Creates a normal (also called Gaussian) distribution parameterized by
+loc and scale."><meta property="og:description" content="Creates a normal (also called Gaussian) distribution parameterized by
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@@ -12,77 +9,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a normal (also called Gaussian) distribution parameterized by <code>loc</code> and <code>scale</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-normal.R" class="external-link"><code>R/distributions-normal.R</code></a></small>
+
+      <h1>Creates a normal (also called Gaussian) distribution parameterized by <code>loc</code> and <code>scale</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-normal.R" class="external-link"><code>R/distributions-normal.R</code></a></small>
       <div class="d-none name"><code>distr_normal.Rd</code></div>
     </div>
 
@@ -98,28 +80,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>loc</dt>
+
+
+<dl><dt id="arg-loc">loc<a class="anchor" aria-label="anchor" href="#arg-loc"></a></dt>
 <dd><p>(float or Tensor): mean of the distribution (often referred to as mu)</p></dd>
 
 
-<dt>scale</dt>
+<dt id="arg-scale">scale<a class="anchor" aria-label="anchor" href="#arg-scale"></a></dt>
 <dd><p>(float or Tensor): standard deviation of the distribution (often referred to as sigma)</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>Additional arguments</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Object of <code>torch_Normal</code> class</p>
+    <p>Object of <code>torch_Normal</code> class</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
-<p>Other distributions: 
+<p>Other distributions:
 <code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
 <code><a href="distr_chi2.html">distr_chi2</a>()</code>,
 <code><a href="distr_gamma.html">distr_gamma</a>()</code>,
@@ -134,11 +116,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span> <span class="co"># normally distributed with loc=0 and scale=1</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3422</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3157</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -147,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/distr_poisson.html b/reference/distr_poisson.html
index bdcb41ce7a..e4282484f6 100644
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+++ b/reference/distr_poisson.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Samples are nonnegative integers, with a pmf given by
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson"><meta name="description" content="Samples are nonnegative integers, with a pmf given by
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 \mbox{rate}^{k} \frac{e^{-\mbox{rate}}}{k!}
-$$"><title>Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson"><meta property="og:description" content="Samples are nonnegative integers, with a pmf given by
+$$"><meta property="og:description" content="Samples are nonnegative integers, with a pmf given by
 $$
 \mbox{rate}^{k} \frac{e^{-\mbox{rate}}}{k!}
-$$"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/distributions-poisson.R" class="external-link"><code>R/distributions-poisson.R</code></a></small>
+
+      <h1>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/distributions-poisson.R" class="external-link"><code>R/distributions-poisson.R</code></a></small>
       <div class="d-none name"><code>distr_poisson.Rd</code></div>
     </div>
 
@@ -104,18 +86,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>rate</dt>
+
+
+<dl><dt id="arg-rate">rate<a class="anchor" aria-label="anchor" href="#arg-rate"></a></dt>
 <dd><p>(numeric, torch_tensor): the rate parameter</p></dd>
 
 
-<dt>validate_args</dt>
+<dt id="arg-validate-args">validate_args<a class="anchor" aria-label="anchor" href="#arg-validate-args"></a></dt>
 <dd><p>whether to validate arguments or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><a href="Distribution.html">Distribution</a> for details on the available methods.</p>
-<p>Other distributions: 
+<p>Other distributions:
 <code><a href="distr_bernoulli.html">distr_bernoulli</a>()</code>,
 <code><a href="distr_chi2.html">distr_chi2</a>()</code>,
 <code><a href="distr_gamma.html">distr_gamma</a>()</code>,
@@ -130,11 +114,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">m</span><span class="op">$</span><span class="fu">sample</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/enumerate.dataloader.html b/reference/enumerate.dataloader.html
index 9cfdc98f30..9cc23b0a51 100644
--- a/reference/enumerate.dataloader.html
+++ b/reference/enumerate.dataloader.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Enumerate an iterator</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-enum.R" class="external-link"><code>R/utils-data-enum.R</code></a></small>
+
+      <h1>Enumerate an iterator</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data-enum.R" class="external-link"><code>R/utils-data-enum.R</code></a></small>
       <div class="d-none name"><code>enumerate.dataloader.Rd</code></div>
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@@ -90,26 +72,28 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
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-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for dataloader</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for class 'dataloader'</span></span>
 <span><span class="fu"><a href="enumerate.html">enumerate</a></span><span class="op">(</span><span class="va">x</span>, max_len <span class="op">=</span> <span class="fl">1e+06</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
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     <div class="section level2">
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-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the generator to enumerate.</p></dd>
 
 
-<dt>max_len</dt>
+<dt id="arg-max-len">max_len<a class="anchor" aria-label="anchor" href="#arg-max-len"></a></dt>
 <dd><p>maximum number of iterations.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>passed to specific methods.</p></dd>
 
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+
+      <h1>Enumerate an iterator</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data-enum.R" class="external-link"><code>R/utils-data-enum.R</code></a></small>
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@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-    <dl><dt>x</dt>
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+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
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 <dd><p>passed to specific methods.</p></dd>
 
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+
         </dt>
         <dd>Full</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_linspace.html">torch_linspace()</a></code> 
+
+          <code><a href="torch_linspace.html">torch_linspace()</a></code>
+
         </dt>
         <dd>Linspace</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logspace.html">torch_logspace()</a></code> 
+
+          <code><a href="torch_logspace.html">torch_logspace()</a></code>
+
         </dt>
         <dd>Logspace</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ones.html">torch_ones()</a></code> 
+
+          <code><a href="torch_ones.html">torch_ones()</a></code>
+
         </dt>
         <dd>Ones</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_rand.html">torch_rand()</a></code> 
+
+          <code><a href="torch_rand.html">torch_rand()</a></code>
+
         </dt>
         <dd>Rand</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_randint.html">torch_randint()</a></code> 
+
+          <code><a href="torch_randint.html">torch_randint()</a></code>
+
         </dt>
         <dd>Randint</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_randn.html">torch_randn()</a></code> 
+
+          <code><a href="torch_randn.html">torch_randn()</a></code>
+
         </dt>
         <dd>Randn</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_randperm.html">torch_randperm()</a></code> 
+
+          <code><a href="torch_randperm.html">torch_randperm()</a></code>
+
         </dt>
         <dd>Randperm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_zeros.html">torch_zeros()</a></code> 
+
+          <code><a href="torch_zeros.html">torch_zeros()</a></code>
+
         </dt>
         <dd>Zeros</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_empty_like.html">torch_empty_like()</a></code> 
+
+          <code><a href="torch_empty_like.html">torch_empty_like()</a></code>
+
         </dt>
         <dd>Empty_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_full_like.html">torch_full_like()</a></code> 
+
+          <code><a href="torch_full_like.html">torch_full_like()</a></code>
+
         </dt>
         <dd>Full_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ones_like.html">torch_ones_like()</a></code> 
+
+          <code><a href="torch_ones_like.html">torch_ones_like()</a></code>
+
         </dt>
         <dd>Ones_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_rand_like.html">torch_rand_like()</a></code> 
+
+          <code><a href="torch_rand_like.html">torch_rand_like()</a></code>
+
         </dt>
         <dd>Rand_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_randint_like.html">torch_randint_like()</a></code> 
+
+          <code><a href="torch_randint_like.html">torch_randint_like()</a></code>
+
         </dt>
         <dd>Randint_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_randn_like.html">torch_randn_like()</a></code> 
+
+          <code><a href="torch_randn_like.html">torch_randn_like()</a></code>
+
         </dt>
         <dd>Randn_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> 
+
+          <code><a href="torch_zeros_like.html">torch_zeros_like()</a></code>
+
         </dt>
         <dd>Zeros_like</dd>
       </dl><dl><dt>
-          
-          <code><a href="as_array.html">as_array()</a></code> 
+
+          <code><a href="as_array.html">as_array()</a></code>
+
         </dt>
         <dd>Converts to array</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tensor_from_buffer.html">torch_tensor_from_buffer()</a></code> <code><a href="torch_tensor_from_buffer.html">buffer_from_torch_tensor()</a></code> 
+
+          <code><a href="torch_tensor_from_buffer.html">torch_tensor_from_buffer()</a></code> <code><a href="torch_tensor_from_buffer.html">buffer_from_torch_tensor()</a></code>
+
         </dt>
         <dd>Creates a tensor from a buffer of memory</dd>
       </dl></div><div class="section level2">
       <h2 id="tensor-attributes">Tensor attributes<a class="anchor" aria-label="anchor" href="#tensor-attributes"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> 
+
+          <code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code>
+
         </dt>
         <dd>Gets and sets the default floating point dtype.</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_torch_device.html">is_torch_device()</a></code> 
+
+          <code><a href="is_torch_device.html">is_torch_device()</a></code>
+
         </dt>
         <dd>Checks if object is a device</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> 
+
+          <code><a href="is_torch_dtype.html">is_torch_dtype()</a></code>
+
         </dt>
         <dd>Check if object is a torch data type</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_cfloat32()</a></code> <code><a href="torch_dtype.html">torch_chalf()</a></code> <code><a href="torch_dtype.html">torch_cfloat()</a></code> <code><a href="torch_dtype.html">torch_cfloat64()</a></code> <code><a href="torch_dtype.html">torch_cdouble()</a></code> <code><a href="torch_dtype.html">torch_cfloat128()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> 
+
+          <code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_cfloat32()</a></code> <code><a href="torch_dtype.html">torch_chalf()</a></code> <code><a href="torch_dtype.html">torch_cfloat()</a></code> <code><a href="torch_dtype.html">torch_cfloat64()</a></code> <code><a href="torch_dtype.html">torch_cdouble()</a></code> <code><a href="torch_dtype.html">torch_cfloat128()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code>
+
         </dt>
         <dd>Torch data types</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_finfo.html">torch_finfo()</a></code> 
+
+          <code><a href="torch_finfo.html">torch_finfo()</a></code>
+
         </dt>
         <dd>Floating point type info</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_iinfo.html">torch_iinfo()</a></code> 
+
+          <code><a href="torch_iinfo.html">torch_iinfo()</a></code>
+
         </dt>
         <dd>Integer type info</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> 
+
+          <code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code>
+
         </dt>
         <dd>Creates the corresponding Scheme object</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> 
+
+          <code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code>
+
         </dt>
         <dd>Creates the reduction objet</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_torch_layout.html">is_torch_layout()</a></code> 
+
+          <code><a href="is_torch_layout.html">is_torch_layout()</a></code>
+
         </dt>
         <dd>Check if an object is a torch layout.</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> 
+
+          <code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code>
+
         </dt>
         <dd>Check if an object is a memory format</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> 
+
+          <code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code>
+
         </dt>
         <dd>Checks if an object is a QScheme</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> 
+
+          <code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code>
+
         </dt>
         <dd>Checks if a tensor is undefined</dd>
       </dl></div><div class="section level2">
       <h2 id="serialization">Serialization<a class="anchor" aria-label="anchor" href="#serialization"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="load_state_dict.html">load_state_dict()</a></code> 
+
+          <code><a href="load_state_dict.html">load_state_dict()</a></code>
+
         </dt>
         <dd>Load a state dict file</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_load.html">torch_load()</a></code> 
+
+          <code><a href="torch_load.html">torch_load()</a></code>
+
         </dt>
         <dd>Loads a saved object</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_save.html">torch_save()</a></code> 
+
+          <code><a href="torch_save.html">torch_save()</a></code>
+
         </dt>
         <dd>Saves an object to a disk file.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_serialize.html">torch_serialize()</a></code> 
+
+          <code><a href="torch_serialize.html">torch_serialize()</a></code>
+
         </dt>
         <dd>Serialize a torch object returning a raw object</dd>
       </dl><dl><dt>
-          
-          <code><a href="clone_module.html">clone_module()</a></code> 
+
+          <code><a href="clone_module.html">clone_module()</a></code>
+
         </dt>
         <dd>Clone a torch module.</dd>
       </dl></div><div class="section level2">
       <h2 id="mathematical-operations-on-tensors">Mathematical operations on tensors<a class="anchor" aria-label="anchor" href="#mathematical-operations-on-tensors"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="threads.html">torch_set_num_threads()</a></code> <code><a href="threads.html">torch_set_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_threads()</a></code> 
+
+          <code><a href="threads.html">torch_set_num_threads()</a></code> <code><a href="threads.html">torch_set_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_interop_threads()</a></code> <code><a href="threads.html">torch_get_num_threads()</a></code>
+
         </dt>
         <dd>Number of threads</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_abs.html">torch_abs()</a></code> 
+
+          <code><a href="torch_abs.html">torch_abs()</a></code>
+
         </dt>
         <dd>Abs</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_absolute.html">torch_absolute()</a></code> 
+
+          <code><a href="torch_absolute.html">torch_absolute()</a></code>
+
         </dt>
         <dd>Absolute</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_acos.html">torch_acos()</a></code> 
+
+          <code><a href="torch_acos.html">torch_acos()</a></code>
+
         </dt>
         <dd>Acos</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_acosh.html">torch_acosh()</a></code> 
+
+          <code><a href="torch_acosh.html">torch_acosh()</a></code>
+
         </dt>
         <dd>Acosh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code> 
+
+          <code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code>
+
         </dt>
         <dd>Adaptive_avg_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_add.html">torch_add()</a></code> 
+
+          <code><a href="torch_add.html">torch_add()</a></code>
+
         </dt>
         <dd>Add</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_addbmm.html">torch_addbmm()</a></code> 
+
+          <code><a href="torch_addbmm.html">torch_addbmm()</a></code>
+
         </dt>
         <dd>Addbmm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_addcdiv.html">torch_addcdiv()</a></code> 
+
+          <code><a href="torch_addcdiv.html">torch_addcdiv()</a></code>
+
         </dt>
         <dd>Addcdiv</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_addcmul.html">torch_addcmul()</a></code> 
+
+          <code><a href="torch_addcmul.html">torch_addcmul()</a></code>
+
         </dt>
         <dd>Addcmul</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_addmm.html">torch_addmm()</a></code> 
+
+          <code><a href="torch_addmm.html">torch_addmm()</a></code>
+
         </dt>
         <dd>Addmm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_addmv.html">torch_addmv()</a></code> 
+
+          <code><a href="torch_addmv.html">torch_addmv()</a></code>
+
         </dt>
         <dd>Addmv</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_addr.html">torch_addr()</a></code> 
+
+          <code><a href="torch_addr.html">torch_addr()</a></code>
+
         </dt>
         <dd>Addr</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_allclose.html">torch_allclose()</a></code> 
+
+          <code><a href="torch_allclose.html">torch_allclose()</a></code>
+
         </dt>
         <dd>Allclose</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_amax.html">torch_amax()</a></code> 
+
+          <code><a href="torch_amax.html">torch_amax()</a></code>
+
         </dt>
         <dd>Amax</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_amin.html">torch_amin()</a></code> 
+
+          <code><a href="torch_amin.html">torch_amin()</a></code>
+
         </dt>
         <dd>Amin</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_angle.html">torch_angle()</a></code> 
+
+          <code><a href="torch_angle.html">torch_angle()</a></code>
+
         </dt>
         <dd>Angle</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_arccos.html">torch_arccos()</a></code> 
+
+          <code><a href="torch_arccos.html">torch_arccos()</a></code>
+
         </dt>
         <dd>Arccos</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_arccosh.html">torch_arccosh()</a></code> 
+
+          <code><a href="torch_arccosh.html">torch_arccosh()</a></code>
+
         </dt>
         <dd>Arccosh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_arcsin.html">torch_arcsin()</a></code> 
+
+          <code><a href="torch_arcsin.html">torch_arcsin()</a></code>
+
         </dt>
         <dd>Arcsin</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_arcsinh.html">torch_arcsinh()</a></code> 
+
+          <code><a href="torch_arcsinh.html">torch_arcsinh()</a></code>
+
         </dt>
         <dd>Arcsinh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_arctan.html">torch_arctan()</a></code> 
+
+          <code><a href="torch_arctan.html">torch_arctan()</a></code>
+
         </dt>
         <dd>Arctan</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_arctanh.html">torch_arctanh()</a></code> 
+
+          <code><a href="torch_arctanh.html">torch_arctanh()</a></code>
+
         </dt>
         <dd>Arctanh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_argmax.html">torch_argmax</a></code> 
+
+          <code><a href="torch_argmax.html">torch_argmax</a></code>
+
         </dt>
         <dd>Argmax</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_argmin.html">torch_argmin</a></code> 
+
+          <code><a href="torch_argmin.html">torch_argmin</a></code>
+
         </dt>
         <dd>Argmin</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_argsort.html">torch_argsort()</a></code> 
+
+          <code><a href="torch_argsort.html">torch_argsort()</a></code>
+
         </dt>
         <dd>Argsort</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_as_strided.html">torch_as_strided()</a></code> 
+
+          <code><a href="torch_as_strided.html">torch_as_strided()</a></code>
+
         </dt>
         <dd>As_strided</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_asin.html">torch_asin()</a></code> 
+
+          <code><a href="torch_asin.html">torch_asin()</a></code>
+
         </dt>
         <dd>Asin</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_asinh.html">torch_asinh()</a></code> 
+
+          <code><a href="torch_asinh.html">torch_asinh()</a></code>
+
         </dt>
         <dd>Asinh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_atan.html">torch_atan()</a></code> 
+
+          <code><a href="torch_atan.html">torch_atan()</a></code>
+
         </dt>
         <dd>Atan</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_atan2.html">torch_atan2()</a></code> 
+
+          <code><a href="torch_atan2.html">torch_atan2()</a></code>
+
         </dt>
         <dd>Atan2</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_atanh.html">torch_atanh()</a></code> 
+
+          <code><a href="torch_atanh.html">torch_atanh()</a></code>
+
         </dt>
         <dd>Atanh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_atleast_1d.html">torch_atleast_1d()</a></code> 
+
+          <code><a href="torch_atleast_1d.html">torch_atleast_1d()</a></code>
+
         </dt>
         <dd>Atleast_1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_atleast_2d.html">torch_atleast_2d()</a></code> 
+
+          <code><a href="torch_atleast_2d.html">torch_atleast_2d()</a></code>
+
         </dt>
         <dd>Atleast_2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_atleast_3d.html">torch_atleast_3d()</a></code> 
+
+          <code><a href="torch_atleast_3d.html">torch_atleast_3d()</a></code>
+
         </dt>
         <dd>Atleast_3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code> 
+
+          <code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code>
+
         </dt>
         <dd>Avg_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_baddbmm.html">torch_baddbmm()</a></code> 
+
+          <code><a href="torch_baddbmm.html">torch_baddbmm()</a></code>
+
         </dt>
         <dd>Baddbmm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code> 
+
+          <code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code>
+
         </dt>
         <dd>Bartlett_window</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bernoulli.html">torch_bernoulli()</a></code> 
+
+          <code><a href="torch_bernoulli.html">torch_bernoulli()</a></code>
+
         </dt>
         <dd>Bernoulli</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bincount.html">torch_bincount</a></code> 
+
+          <code><a href="torch_bincount.html">torch_bincount</a></code>
+
         </dt>
         <dd>Bincount</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code> 
+
+          <code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code>
+
         </dt>
         <dd>Bitwise_and</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code> 
+
+          <code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code>
+
         </dt>
         <dd>Bitwise_not</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code> 
+
+          <code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code>
+
         </dt>
         <dd>Bitwise_or</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code> 
+
+          <code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code>
+
         </dt>
         <dd>Bitwise_xor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_blackman_window.html">torch_blackman_window()</a></code> 
+
+          <code><a href="torch_blackman_window.html">torch_blackman_window()</a></code>
+
         </dt>
         <dd>Blackman_window</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_block_diag.html">torch_block_diag()</a></code> 
+
+          <code><a href="torch_block_diag.html">torch_block_diag()</a></code>
+
         </dt>
         <dd>Block_diag</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bmm.html">torch_bmm()</a></code> 
+
+          <code><a href="torch_bmm.html">torch_bmm()</a></code>
+
         </dt>
         <dd>Bmm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code> 
+
+          <code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code>
+
         </dt>
         <dd>Broadcast_tensors</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_bucketize.html">torch_bucketize()</a></code> 
+
+          <code><a href="torch_bucketize.html">torch_bucketize()</a></code>
+
         </dt>
         <dd>Bucketize</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_can_cast.html">torch_can_cast()</a></code> 
+
+          <code><a href="torch_can_cast.html">torch_can_cast()</a></code>
+
         </dt>
         <dd>Can_cast</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code> 
+
+          <code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code>
+
         </dt>
         <dd>Cartesian_prod</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cat.html">torch_cat()</a></code> 
+
+          <code><a href="torch_cat.html">torch_cat()</a></code>
+
         </dt>
         <dd>Cat</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cdist.html">torch_cdist()</a></code> 
+
+          <code><a href="torch_cdist.html">torch_cdist()</a></code>
+
         </dt>
         <dd>Cdist</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ceil.html">torch_ceil()</a></code> 
+
+          <code><a href="torch_ceil.html">torch_ceil()</a></code>
+
         </dt>
         <dd>Ceil</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_celu.html">torch_celu()</a></code> 
+
+          <code><a href="torch_celu.html">torch_celu()</a></code>
+
         </dt>
         <dd>Celu</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_celu_.html">torch_celu_()</a></code> 
+
+          <code><a href="torch_celu_.html">torch_celu_()</a></code>
+
         </dt>
         <dd>Celu_</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code> 
+
+          <code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code>
+
         </dt>
         <dd>Chain_matmul</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_channel_shuffle.html">torch_channel_shuffle()</a></code> 
+
+          <code><a href="torch_channel_shuffle.html">torch_channel_shuffle()</a></code>
+
         </dt>
         <dd>Channel_shuffle</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cholesky.html">torch_cholesky()</a></code> 
+
+          <code><a href="torch_cholesky.html">torch_cholesky()</a></code>
+
         </dt>
         <dd>Cholesky</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code> 
+
+          <code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code>
+
         </dt>
         <dd>Cholesky_inverse</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code> 
+
+          <code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code>
+
         </dt>
         <dd>Cholesky_solve</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_chunk.html">torch_chunk()</a></code> 
+
+          <code><a href="torch_chunk.html">torch_chunk()</a></code>
+
         </dt>
         <dd>Chunk</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_clamp.html">torch_clamp()</a></code> 
+
+          <code><a href="torch_clamp.html">torch_clamp()</a></code>
+
         </dt>
         <dd>Clamp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_clip.html">torch_clip()</a></code> 
+
+          <code><a href="torch_clip.html">torch_clip()</a></code>
+
         </dt>
         <dd>Clip</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_clone.html">torch_clone()</a></code> 
+
+          <code><a href="torch_clone.html">torch_clone()</a></code>
+
         </dt>
         <dd>Clone</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_combinations.html">torch_combinations()</a></code> 
+
+          <code><a href="torch_combinations.html">torch_combinations()</a></code>
+
         </dt>
         <dd>Combinations</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_complex.html">torch_complex()</a></code> 
+
+          <code><a href="torch_complex.html">torch_complex()</a></code>
+
         </dt>
         <dd>Complex</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conj.html">torch_conj()</a></code> 
+
+          <code><a href="torch_conj.html">torch_conj()</a></code>
+
         </dt>
         <dd>Conj</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv1d.html">torch_conv1d()</a></code> 
+
+          <code><a href="torch_conv1d.html">torch_conv1d()</a></code>
+
         </dt>
         <dd>Conv1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv2d.html">torch_conv2d()</a></code> 
+
+          <code><a href="torch_conv2d.html">torch_conv2d()</a></code>
+
         </dt>
         <dd>Conv2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv3d.html">torch_conv3d()</a></code> 
+
+          <code><a href="torch_conv3d.html">torch_conv3d()</a></code>
+
         </dt>
         <dd>Conv3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code> 
+
+          <code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code>
+
         </dt>
         <dd>Conv_tbc</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code> 
+
+          <code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code>
+
         </dt>
         <dd>Conv_transpose1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code> 
+
+          <code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code>
+
         </dt>
         <dd>Conv_transpose2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code> 
+
+          <code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code>
+
         </dt>
         <dd>Conv_transpose3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cos.html">torch_cos()</a></code> 
+
+          <code><a href="torch_cos.html">torch_cos()</a></code>
+
         </dt>
         <dd>Cos</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cosh.html">torch_cosh()</a></code> 
+
+          <code><a href="torch_cosh.html">torch_cosh()</a></code>
+
         </dt>
         <dd>Cosh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code> 
+
+          <code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code>
+
         </dt>
         <dd>Cosine_similarity</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_count_nonzero.html">torch_count_nonzero()</a></code> 
+
+          <code><a href="torch_count_nonzero.html">torch_count_nonzero()</a></code>
+
         </dt>
         <dd>Count_nonzero</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cross.html">torch_cross()</a></code> 
+
+          <code><a href="torch_cross.html">torch_cross()</a></code>
+
         </dt>
         <dd>Cross</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cummax.html">torch_cummax()</a></code> 
+
+          <code><a href="torch_cummax.html">torch_cummax()</a></code>
+
         </dt>
         <dd>Cummax</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cummin.html">torch_cummin()</a></code> 
+
+          <code><a href="torch_cummin.html">torch_cummin()</a></code>
+
         </dt>
         <dd>Cummin</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cumprod.html">torch_cumprod()</a></code> 
+
+          <code><a href="torch_cumprod.html">torch_cumprod()</a></code>
+
         </dt>
         <dd>Cumprod</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_cumsum.html">torch_cumsum()</a></code> 
+
+          <code><a href="torch_cumsum.html">torch_cumsum()</a></code>
+
         </dt>
         <dd>Cumsum</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_deg2rad.html">torch_deg2rad()</a></code> 
+
+          <code><a href="torch_deg2rad.html">torch_deg2rad()</a></code>
+
         </dt>
         <dd>Deg2rad</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_dequantize.html">torch_dequantize()</a></code> 
+
+          <code><a href="torch_dequantize.html">torch_dequantize()</a></code>
+
         </dt>
         <dd>Dequantize</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_det.html">torch_det()</a></code> 
+
+          <code><a href="torch_det.html">torch_det()</a></code>
+
         </dt>
         <dd>Det</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_device.html">torch_device()</a></code> 
+
+          <code><a href="torch_device.html">torch_device()</a></code>
+
         </dt>
         <dd>Create a Device object</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_diag.html">torch_diag()</a></code> 
+
+          <code><a href="torch_diag.html">torch_diag()</a></code>
+
         </dt>
         <dd>Diag</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_diag_embed.html">torch_diag_embed()</a></code> 
+
+          <code><a href="torch_diag_embed.html">torch_diag_embed()</a></code>
+
         </dt>
         <dd>Diag_embed</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_diagflat.html">torch_diagflat()</a></code> 
+
+          <code><a href="torch_diagflat.html">torch_diagflat()</a></code>
+
         </dt>
         <dd>Diagflat</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_diagonal.html">torch_diagonal()</a></code> 
+
+          <code><a href="torch_diagonal.html">torch_diagonal()</a></code>
+
         </dt>
         <dd>Diagonal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_diff.html">torch_diff()</a></code> 
+
+          <code><a href="torch_diff.html">torch_diff()</a></code>
+
         </dt>
         <dd>Computes the n-th forward difference along the given dimension.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_digamma.html">torch_digamma()</a></code> 
+
+          <code><a href="torch_digamma.html">torch_digamma()</a></code>
+
         </dt>
         <dd>Digamma</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_dist.html">torch_dist()</a></code> 
+
+          <code><a href="torch_dist.html">torch_dist()</a></code>
+
         </dt>
         <dd>Dist</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_div.html">torch_div()</a></code> 
+
+          <code><a href="torch_div.html">torch_div()</a></code>
+
         </dt>
         <dd>Div</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_divide.html">torch_divide()</a></code> 
+
+          <code><a href="torch_divide.html">torch_divide()</a></code>
+
         </dt>
         <dd>Divide</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_dot.html">torch_dot()</a></code> 
+
+          <code><a href="torch_dot.html">torch_dot()</a></code>
+
         </dt>
         <dd>Dot</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_dstack.html">torch_dstack()</a></code> 
+
+          <code><a href="torch_dstack.html">torch_dstack()</a></code>
+
         </dt>
         <dd>Dstack</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_eig.html">torch_eig</a></code> 
+
+          <code><a href="torch_eig.html">torch_eig</a></code>
+
         </dt>
         <dd>Eig</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_einsum.html">torch_einsum()</a></code> 
+
+          <code><a href="torch_einsum.html">torch_einsum()</a></code>
+
         </dt>
         <dd>Einsum</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_empty_strided.html">torch_empty_strided()</a></code> 
+
+          <code><a href="torch_empty_strided.html">torch_empty_strided()</a></code>
+
         </dt>
         <dd>Empty_strided</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_eq.html">torch_eq()</a></code> 
+
+          <code><a href="torch_eq.html">torch_eq()</a></code>
+
         </dt>
         <dd>Eq</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_equal.html">torch_equal()</a></code> 
+
+          <code><a href="torch_equal.html">torch_equal()</a></code>
+
         </dt>
         <dd>Equal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_erf.html">torch_erf()</a></code> 
+
+          <code><a href="torch_erf.html">torch_erf()</a></code>
+
         </dt>
         <dd>Erf</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_erfc.html">torch_erfc()</a></code> 
+
+          <code><a href="torch_erfc.html">torch_erfc()</a></code>
+
         </dt>
         <dd>Erfc</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_erfinv.html">torch_erfinv()</a></code> 
+
+          <code><a href="torch_erfinv.html">torch_erfinv()</a></code>
+
         </dt>
         <dd>Erfinv</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_exp.html">torch_exp()</a></code> 
+
+          <code><a href="torch_exp.html">torch_exp()</a></code>
+
         </dt>
         <dd>Exp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_exp2.html">torch_exp2()</a></code> 
+
+          <code><a href="torch_exp2.html">torch_exp2()</a></code>
+
         </dt>
         <dd>Exp2</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_expm1.html">torch_expm1()</a></code> 
+
+          <code><a href="torch_expm1.html">torch_expm1()</a></code>
+
         </dt>
         <dd>Expm1</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fft_fft.html">torch_fft_fft()</a></code> 
+
+          <code><a href="torch_fft_fft.html">torch_fft_fft()</a></code>
+
         </dt>
         <dd>Fft</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fft_fftfreq.html">torch_fft_fftfreq()</a></code> 
+
+          <code><a href="torch_fft_fftfreq.html">torch_fft_fftfreq()</a></code>
+
         </dt>
         <dd>fftfreq</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fft_ifft.html">torch_fft_ifft()</a></code> 
+
+          <code><a href="torch_fft_ifft.html">torch_fft_ifft()</a></code>
+
         </dt>
         <dd>Ifft</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fft_irfft.html">torch_fft_irfft()</a></code> 
+
+          <code><a href="torch_fft_irfft.html">torch_fft_irfft()</a></code>
+
         </dt>
         <dd>Irfft</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code> 
+
+          <code><a href="torch_fft_rfft.html">torch_fft_rfft()</a></code>
+
         </dt>
         <dd>Rfft</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fix.html">torch_fix()</a></code> 
+
+          <code><a href="torch_fix.html">torch_fix()</a></code>
+
         </dt>
         <dd>Fix</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_flatten.html">torch_flatten()</a></code> 
+
+          <code><a href="torch_flatten.html">torch_flatten()</a></code>
+
         </dt>
         <dd>Flatten</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_flip.html">torch_flip()</a></code> 
+
+          <code><a href="torch_flip.html">torch_flip()</a></code>
+
         </dt>
         <dd>Flip</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fliplr.html">torch_fliplr()</a></code> 
+
+          <code><a href="torch_fliplr.html">torch_fliplr()</a></code>
+
         </dt>
         <dd>Fliplr</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_flipud.html">torch_flipud()</a></code> 
+
+          <code><a href="torch_flipud.html">torch_flipud()</a></code>
+
         </dt>
         <dd>Flipud</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_floor.html">torch_floor()</a></code> 
+
+          <code><a href="torch_floor.html">torch_floor()</a></code>
+
         </dt>
         <dd>Floor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> 
+
+          <code><a href="torch_floor_divide.html">torch_floor_divide()</a></code>
+
         </dt>
         <dd>Floor_divide</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_fmod.html">torch_fmod()</a></code> 
+
+          <code><a href="torch_fmod.html">torch_fmod()</a></code>
+
         </dt>
         <dd>Fmod</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_frac.html">torch_frac()</a></code> 
+
+          <code><a href="torch_frac.html">torch_frac()</a></code>
+
         </dt>
         <dd>Frac</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_gather.html">torch_gather()</a></code> 
+
+          <code><a href="torch_gather.html">torch_gather()</a></code>
+
         </dt>
         <dd>Gather</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_gcd.html">torch_gcd()</a></code> 
+
+          <code><a href="torch_gcd.html">torch_gcd()</a></code>
+
         </dt>
         <dd>Gcd</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ge.html">torch_ge()</a></code> 
+
+          <code><a href="torch_ge.html">torch_ge()</a></code>
+
         </dt>
         <dd>Ge</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_generator.html">torch_generator()</a></code> 
+
+          <code><a href="torch_generator.html">torch_generator()</a></code>
+
         </dt>
         <dd>Create a Generator object</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_geqrf.html">torch_geqrf()</a></code> 
+
+          <code><a href="torch_geqrf.html">torch_geqrf()</a></code>
+
         </dt>
         <dd>Geqrf</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ger.html">torch_ger()</a></code> 
+
+          <code><a href="torch_ger.html">torch_ger()</a></code>
+
         </dt>
         <dd>Ger</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_get_rng_state.html">torch_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">torch_set_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_set_rng_state()</a></code> 
+
+          <code><a href="torch_get_rng_state.html">torch_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">torch_set_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_set_rng_state()</a></code>
+
         </dt>
         <dd>RNG state management</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_greater.html">torch_greater()</a></code> 
+
+          <code><a href="torch_greater.html">torch_greater()</a></code>
+
         </dt>
         <dd>Greater</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_greater_equal.html">torch_greater_equal()</a></code> 
+
+          <code><a href="torch_greater_equal.html">torch_greater_equal()</a></code>
+
         </dt>
         <dd>Greater_equal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_gt.html">torch_gt()</a></code> 
+
+          <code><a href="torch_gt.html">torch_gt()</a></code>
+
         </dt>
         <dd>Gt</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_hamming_window.html">torch_hamming_window()</a></code> 
+
+          <code><a href="torch_hamming_window.html">torch_hamming_window()</a></code>
+
         </dt>
         <dd>Hamming_window</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_hann_window.html">torch_hann_window()</a></code> 
+
+          <code><a href="torch_hann_window.html">torch_hann_window()</a></code>
+
         </dt>
         <dd>Hann_window</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_heaviside.html">torch_heaviside()</a></code> 
+
+          <code><a href="torch_heaviside.html">torch_heaviside()</a></code>
+
         </dt>
         <dd>Heaviside</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_histc.html">torch_histc()</a></code> 
+
+          <code><a href="torch_histc.html">torch_histc()</a></code>
+
         </dt>
         <dd>Histc</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_hstack.html">torch_hstack()</a></code> 
+
+          <code><a href="torch_hstack.html">torch_hstack()</a></code>
+
         </dt>
         <dd>Hstack</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_hypot.html">torch_hypot()</a></code> 
+
+          <code><a href="torch_hypot.html">torch_hypot()</a></code>
+
         </dt>
         <dd>Hypot</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_i0.html">torch_i0()</a></code> 
+
+          <code><a href="torch_i0.html">torch_i0()</a></code>
+
         </dt>
         <dd>I0</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_imag.html">torch_imag()</a></code> 
+
+          <code><a href="torch_imag.html">torch_imag()</a></code>
+
         </dt>
         <dd>Imag</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_index.html">torch_index()</a></code> 
+
+          <code><a href="torch_index.html">torch_index()</a></code>
+
         </dt>
         <dd>Index torch tensors</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_index_put.html">torch_index_put()</a></code> 
+
+          <code><a href="torch_index_put.html">torch_index_put()</a></code>
+
         </dt>
         <dd>Modify values selected by <code>indices</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_index_put_.html">torch_index_put_()</a></code> 
+
+          <code><a href="torch_index_put_.html">torch_index_put_()</a></code>
+
         </dt>
         <dd>In-place version of <code>torch_index_put</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_index_select.html">torch_index_select()</a></code> 
+
+          <code><a href="torch_index_select.html">torch_index_select()</a></code>
+
         </dt>
         <dd>Index_select</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_install_path.html">torch_install_path()</a></code> 
+
+          <code><a href="torch_install_path.html">torch_install_path()</a></code>
+
         </dt>
         <dd>A simple exported version of install_path Returns the torch installation path.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_inverse.html">torch_inverse()</a></code> 
+
+          <code><a href="torch_inverse.html">torch_inverse()</a></code>
+
         </dt>
         <dd>Inverse</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_is_complex.html">torch_is_complex()</a></code> 
+
+          <code><a href="torch_is_complex.html">torch_is_complex()</a></code>
+
         </dt>
         <dd>Is_complex</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code> 
+
+          <code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code>
+
         </dt>
         <dd>Is_floating_point</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_is_installed.html">torch_is_installed()</a></code> 
+
+          <code><a href="torch_is_installed.html">torch_is_installed()</a></code>
+
         </dt>
         <dd>Verifies if torch is installed</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_is_nonzero.html">torch_is_nonzero()</a></code> 
+
+          <code><a href="torch_is_nonzero.html">torch_is_nonzero()</a></code>
+
         </dt>
         <dd>Is_nonzero</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isclose.html">torch_isclose()</a></code> 
+
+          <code><a href="torch_isclose.html">torch_isclose()</a></code>
+
         </dt>
         <dd>Isclose</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isfinite.html">torch_isfinite()</a></code> 
+
+          <code><a href="torch_isfinite.html">torch_isfinite()</a></code>
+
         </dt>
         <dd>Isfinite</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isinf.html">torch_isinf()</a></code> 
+
+          <code><a href="torch_isinf.html">torch_isinf()</a></code>
+
         </dt>
         <dd>Isinf</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isnan.html">torch_isnan()</a></code> 
+
+          <code><a href="torch_isnan.html">torch_isnan()</a></code>
+
         </dt>
         <dd>Isnan</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isneginf.html">torch_isneginf()</a></code> 
+
+          <code><a href="torch_isneginf.html">torch_isneginf()</a></code>
+
         </dt>
         <dd>Isneginf</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isposinf.html">torch_isposinf()</a></code> 
+
+          <code><a href="torch_isposinf.html">torch_isposinf()</a></code>
+
         </dt>
         <dd>Isposinf</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_isreal.html">torch_isreal()</a></code> 
+
+          <code><a href="torch_isreal.html">torch_isreal()</a></code>
+
         </dt>
         <dd>Isreal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_istft.html">torch_istft()</a></code> 
+
+          <code><a href="torch_istft.html">torch_istft()</a></code>
+
         </dt>
         <dd>Istft</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_kaiser_window.html">torch_kaiser_window()</a></code> 
+
+          <code><a href="torch_kaiser_window.html">torch_kaiser_window()</a></code>
+
         </dt>
         <dd>Kaiser_window</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_kron.html">torch_kron()</a></code> 
+
+          <code><a href="torch_kron.html">torch_kron()</a></code>
+
         </dt>
         <dd>Kronecker product</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_kthvalue.html">torch_kthvalue()</a></code> 
+
+          <code><a href="torch_kthvalue.html">torch_kthvalue()</a></code>
+
         </dt>
         <dd>Kthvalue</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> 
+
+          <code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code>
+
         </dt>
         <dd>Creates the corresponding layout</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lcm.html">torch_lcm()</a></code> 
+
+          <code><a href="torch_lcm.html">torch_lcm()</a></code>
+
         </dt>
         <dd>Lcm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_le.html">torch_le()</a></code> 
+
+          <code><a href="torch_le.html">torch_le()</a></code>
+
         </dt>
         <dd>Le</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lerp.html">torch_lerp()</a></code> 
+
+          <code><a href="torch_lerp.html">torch_lerp()</a></code>
+
         </dt>
         <dd>Lerp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_less.html">torch_less()</a></code> 
+
+          <code><a href="torch_less.html">torch_less()</a></code>
+
         </dt>
         <dd>Less</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_less_equal.html">torch_less_equal()</a></code> 
+
+          <code><a href="torch_less_equal.html">torch_less_equal()</a></code>
+
         </dt>
         <dd>Less_equal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lgamma.html">torch_lgamma()</a></code> 
+
+          <code><a href="torch_lgamma.html">torch_lgamma()</a></code>
+
         </dt>
         <dd>Lgamma</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_log.html">torch_log()</a></code> 
+
+          <code><a href="torch_log.html">torch_log()</a></code>
+
         </dt>
         <dd>Log</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_log10.html">torch_log10()</a></code> 
+
+          <code><a href="torch_log10.html">torch_log10()</a></code>
+
         </dt>
         <dd>Log10</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_log1p.html">torch_log1p()</a></code> 
+
+          <code><a href="torch_log1p.html">torch_log1p()</a></code>
+
         </dt>
         <dd>Log1p</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_log2.html">torch_log2()</a></code> 
+
+          <code><a href="torch_log2.html">torch_log2()</a></code>
+
         </dt>
         <dd>Log2</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logaddexp.html">torch_logaddexp()</a></code> 
+
+          <code><a href="torch_logaddexp.html">torch_logaddexp()</a></code>
+
         </dt>
         <dd>Logaddexp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logaddexp2.html">torch_logaddexp2()</a></code> 
+
+          <code><a href="torch_logaddexp2.html">torch_logaddexp2()</a></code>
+
         </dt>
         <dd>Logaddexp2</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logcumsumexp.html">torch_logcumsumexp()</a></code> 
+
+          <code><a href="torch_logcumsumexp.html">torch_logcumsumexp()</a></code>
+
         </dt>
         <dd>Logcumsumexp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logdet.html">torch_logdet()</a></code> 
+
+          <code><a href="torch_logdet.html">torch_logdet()</a></code>
+
         </dt>
         <dd>Logdet</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logical_and.html">torch_logical_and()</a></code> 
+
+          <code><a href="torch_logical_and.html">torch_logical_and()</a></code>
+
         </dt>
         <dd>Logical_and</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logical_not.html">torch_logical_not</a></code> 
+
+          <code><a href="torch_logical_not.html">torch_logical_not</a></code>
+
         </dt>
         <dd>Logical_not</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logical_or.html">torch_logical_or()</a></code> 
+
+          <code><a href="torch_logical_or.html">torch_logical_or()</a></code>
+
         </dt>
         <dd>Logical_or</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logical_xor.html">torch_logical_xor()</a></code> 
+
+          <code><a href="torch_logical_xor.html">torch_logical_xor()</a></code>
+
         </dt>
         <dd>Logical_xor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logit.html">torch_logit()</a></code> 
+
+          <code><a href="torch_logit.html">torch_logit()</a></code>
+
         </dt>
         <dd>Logit</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> 
+
+          <code><a href="torch_logsumexp.html">torch_logsumexp()</a></code>
+
         </dt>
         <dd>Logsumexp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lstsq.html">torch_lstsq</a></code> 
+
+          <code><a href="torch_lstsq.html">torch_lstsq</a></code>
+
         </dt>
         <dd>Lstsq</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lt.html">torch_lt()</a></code> 
+
+          <code><a href="torch_lt.html">torch_lt()</a></code>
+
         </dt>
         <dd>Lt</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lu.html">torch_lu()</a></code> 
+
+          <code><a href="torch_lu.html">torch_lu()</a></code>
+
         </dt>
         <dd>LU</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lu_solve.html">torch_lu_solve()</a></code> 
+
+          <code><a href="torch_lu_solve.html">torch_lu_solve()</a></code>
+
         </dt>
         <dd>Lu_solve</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_lu_unpack.html">torch_lu_unpack()</a></code> 
+
+          <code><a href="torch_lu_unpack.html">torch_lu_unpack()</a></code>
+
         </dt>
         <dd>Lu_unpack</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">local_torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">with_torch_manual_seed()</a></code> 
+
+          <code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">local_torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">with_torch_manual_seed()</a></code>
+
         </dt>
         <dd>Sets the seed for generating random numbers.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_masked_select.html">torch_masked_select()</a></code> 
+
+          <code><a href="torch_masked_select.html">torch_masked_select()</a></code>
+
         </dt>
         <dd>Masked_select</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_matmul.html">torch_matmul()</a></code> 
+
+          <code><a href="torch_matmul.html">torch_matmul()</a></code>
+
         </dt>
         <dd>Matmul</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_matrix_exp.html">torch_matrix_exp()</a></code> 
+
+          <code><a href="torch_matrix_exp.html">torch_matrix_exp()</a></code>
+
         </dt>
         <dd>Matrix_exp</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_matrix_power.html">torch_matrix_power()</a></code> 
+
+          <code><a href="torch_matrix_power.html">torch_matrix_power()</a></code>
+
         </dt>
         <dd>Matrix_power</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_matrix_rank.html">torch_matrix_rank</a></code> 
+
+          <code><a href="torch_matrix_rank.html">torch_matrix_rank</a></code>
+
         </dt>
         <dd>Matrix_rank</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_max.html">torch_max</a></code> 
+
+          <code><a href="torch_max.html">torch_max</a></code>
+
         </dt>
         <dd>Max</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_maximum.html">torch_maximum()</a></code> 
+
+          <code><a href="torch_maximum.html">torch_maximum()</a></code>
+
         </dt>
         <dd>Maximum</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_mean.html">torch_mean()</a></code> 
+
+          <code><a href="torch_mean.html">torch_mean()</a></code>
+
         </dt>
         <dd>Mean</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_median.html">torch_median()</a></code> 
+
+          <code><a href="torch_median.html">torch_median()</a></code>
+
         </dt>
         <dd>Median</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> 
+
+          <code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code>
+
         </dt>
         <dd>Memory format</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_meshgrid.html">torch_meshgrid()</a></code> 
+
+          <code><a href="torch_meshgrid.html">torch_meshgrid()</a></code>
+
         </dt>
         <dd>Meshgrid</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_min.html">torch_min</a></code> 
+
+          <code><a href="torch_min.html">torch_min</a></code>
+
         </dt>
         <dd>Min</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_minimum.html">torch_minimum()</a></code> 
+
+          <code><a href="torch_minimum.html">torch_minimum()</a></code>
+
         </dt>
         <dd>Minimum</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_mm.html">torch_mm()</a></code> 
+
+          <code><a href="torch_mm.html">torch_mm()</a></code>
+
         </dt>
         <dd>Mm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_mode.html">torch_mode()</a></code> 
+
+          <code><a href="torch_mode.html">torch_mode()</a></code>
+
         </dt>
         <dd>Mode</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_movedim.html">torch_movedim()</a></code> 
+
+          <code><a href="torch_movedim.html">torch_movedim()</a></code>
+
         </dt>
         <dd>Movedim</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_mul.html">torch_mul()</a></code> 
+
+          <code><a href="torch_mul.html">torch_mul()</a></code>
+
         </dt>
         <dd>Mul</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_multinomial.html">torch_multinomial()</a></code> 
+
+          <code><a href="torch_multinomial.html">torch_multinomial()</a></code>
+
         </dt>
         <dd>Multinomial</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_multiply.html">torch_multiply()</a></code> 
+
+          <code><a href="torch_multiply.html">torch_multiply()</a></code>
+
         </dt>
         <dd>Multiply</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_mv.html">torch_mv()</a></code> 
+
+          <code><a href="torch_mv.html">torch_mv()</a></code>
+
         </dt>
         <dd>Mv</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code> 
+
+          <code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code>
+
         </dt>
         <dd>Mvlgamma</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_nanquantile.html">torch_nanquantile()</a></code> 
+
+          <code><a href="torch_nanquantile.html">torch_nanquantile()</a></code>
+
         </dt>
         <dd>Nanquantile</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_nansum.html">torch_nansum()</a></code> 
+
+          <code><a href="torch_nansum.html">torch_nansum()</a></code>
+
         </dt>
         <dd>Nansum</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_narrow.html">torch_narrow()</a></code> 
+
+          <code><a href="torch_narrow.html">torch_narrow()</a></code>
+
         </dt>
         <dd>Narrow</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ne.html">torch_ne()</a></code> 
+
+          <code><a href="torch_ne.html">torch_ne()</a></code>
+
         </dt>
         <dd>Ne</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_neg.html">torch_neg()</a></code> 
+
+          <code><a href="torch_neg.html">torch_neg()</a></code>
+
         </dt>
         <dd>Neg</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_negative.html">torch_negative()</a></code> 
+
+          <code><a href="torch_negative.html">torch_negative()</a></code>
+
         </dt>
         <dd>Negative</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_nextafter.html">torch_nextafter()</a></code> 
+
+          <code><a href="torch_nextafter.html">torch_nextafter()</a></code>
+
         </dt>
         <dd>Nextafter</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_nonzero.html">torch_nonzero()</a></code> 
+
+          <code><a href="torch_nonzero.html">torch_nonzero()</a></code>
+
         </dt>
         <dd>Nonzero</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_norm.html">torch_norm()</a></code> 
+
+          <code><a href="torch_norm.html">torch_norm()</a></code>
+
         </dt>
         <dd>Norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_normal.html">torch_normal()</a></code> 
+
+          <code><a href="torch_normal.html">torch_normal()</a></code>
+
         </dt>
         <dd>Normal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_not_equal.html">torch_not_equal()</a></code> 
+
+          <code><a href="torch_not_equal.html">torch_not_equal()</a></code>
+
         </dt>
         <dd>Not_equal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_orgqr.html">torch_orgqr()</a></code> 
+
+          <code><a href="torch_orgqr.html">torch_orgqr()</a></code>
+
         </dt>
         <dd>Orgqr</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_ormqr.html">torch_ormqr()</a></code> 
+
+          <code><a href="torch_ormqr.html">torch_ormqr()</a></code>
+
         </dt>
         <dd>Ormqr</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_outer.html">torch_outer()</a></code> 
+
+          <code><a href="torch_outer.html">torch_outer()</a></code>
+
         </dt>
         <dd>Outer</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_pdist.html">torch_pdist()</a></code> 
+
+          <code><a href="torch_pdist.html">torch_pdist()</a></code>
+
         </dt>
         <dd>Pdist</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_pinverse.html">torch_pinverse()</a></code> 
+
+          <code><a href="torch_pinverse.html">torch_pinverse()</a></code>
+
         </dt>
         <dd>Pinverse</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code> 
+
+          <code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code>
+
         </dt>
         <dd>Pixel_shuffle</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_poisson.html">torch_poisson()</a></code> 
+
+          <code><a href="torch_poisson.html">torch_poisson()</a></code>
+
         </dt>
         <dd>Poisson</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_polar.html">torch_polar()</a></code> 
+
+          <code><a href="torch_polar.html">torch_polar()</a></code>
+
         </dt>
         <dd>Polar</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_polygamma.html">torch_polygamma()</a></code> 
+
+          <code><a href="torch_polygamma.html">torch_polygamma()</a></code>
+
         </dt>
         <dd>Polygamma</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_pow.html">torch_pow()</a></code> 
+
+          <code><a href="torch_pow.html">torch_pow()</a></code>
+
         </dt>
         <dd>Pow</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_prod.html">torch_prod()</a></code> 
+
+          <code><a href="torch_prod.html">torch_prod()</a></code>
+
         </dt>
         <dd>Prod</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_promote_types.html">torch_promote_types()</a></code> 
+
+          <code><a href="torch_promote_types.html">torch_promote_types()</a></code>
+
         </dt>
         <dd>Promote_types</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_qr.html">torch_qr()</a></code> 
+
+          <code><a href="torch_qr.html">torch_qr()</a></code>
+
         </dt>
         <dd>Qr</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_quantile.html">torch_quantile()</a></code> 
+
+          <code><a href="torch_quantile.html">torch_quantile()</a></code>
+
         </dt>
         <dd>Quantile</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code> 
+
+          <code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code>
+
         </dt>
         <dd>Quantize_per_channel</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code> 
+
+          <code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code>
+
         </dt>
         <dd>Quantize_per_tensor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_rad2deg.html">torch_rad2deg()</a></code> 
+
+          <code><a href="torch_rad2deg.html">torch_rad2deg()</a></code>
+
         </dt>
         <dd>Rad2deg</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_range.html">torch_range()</a></code> 
+
+          <code><a href="torch_range.html">torch_range()</a></code>
+
         </dt>
         <dd>Range</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_real.html">torch_real()</a></code> 
+
+          <code><a href="torch_real.html">torch_real()</a></code>
+
         </dt>
         <dd>Real</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_reciprocal.html">torch_reciprocal()</a></code> 
+
+          <code><a href="torch_reciprocal.html">torch_reciprocal()</a></code>
+
         </dt>
         <dd>Reciprocal</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_relu.html">torch_relu()</a></code> 
+
+          <code><a href="torch_relu.html">torch_relu()</a></code>
+
         </dt>
         <dd>Relu</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_relu_.html">torch_relu_()</a></code> 
+
+          <code><a href="torch_relu_.html">torch_relu_()</a></code>
+
         </dt>
         <dd>Relu_</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_remainder.html">torch_remainder()</a></code> 
+
+          <code><a href="torch_remainder.html">torch_remainder()</a></code>
+
         </dt>
         <dd>Remainder</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_renorm.html">torch_renorm()</a></code> 
+
+          <code><a href="torch_renorm.html">torch_renorm()</a></code>
+
         </dt>
         <dd>Renorm</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code> 
+
+          <code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code>
+
         </dt>
         <dd>Repeat_interleave</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_reshape.html">torch_reshape()</a></code> 
+
+          <code><a href="torch_reshape.html">torch_reshape()</a></code>
+
         </dt>
         <dd>Reshape</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_result_type.html">torch_result_type()</a></code> 
+
+          <code><a href="torch_result_type.html">torch_result_type()</a></code>
+
         </dt>
         <dd>Result_type</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_roll.html">torch_roll()</a></code> 
+
+          <code><a href="torch_roll.html">torch_roll()</a></code>
+
         </dt>
         <dd>Roll</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_rot90.html">torch_rot90()</a></code> 
+
+          <code><a href="torch_rot90.html">torch_rot90()</a></code>
+
         </dt>
         <dd>Rot90</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_round.html">torch_round()</a></code> 
+
+          <code><a href="torch_round.html">torch_round()</a></code>
+
         </dt>
         <dd>Round</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_rrelu_.html">torch_rrelu_()</a></code> 
+
+          <code><a href="torch_rrelu_.html">torch_rrelu_()</a></code>
+
         </dt>
         <dd>Rrelu_</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_rsqrt.html">torch_rsqrt()</a></code> 
+
+          <code><a href="torch_rsqrt.html">torch_rsqrt()</a></code>
+
         </dt>
         <dd>Rsqrt</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_scalar_tensor.html">torch_scalar_tensor()</a></code> 
+
+          <code><a href="torch_scalar_tensor.html">torch_scalar_tensor()</a></code>
+
         </dt>
         <dd>Scalar tensor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_searchsorted.html">torch_searchsorted()</a></code> 
+
+          <code><a href="torch_searchsorted.html">torch_searchsorted()</a></code>
+
         </dt>
         <dd>Searchsorted</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_selu.html">torch_selu()</a></code> 
+
+          <code><a href="torch_selu.html">torch_selu()</a></code>
+
         </dt>
         <dd>Selu</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_selu_.html">torch_selu_()</a></code> 
+
+          <code><a href="torch_selu_.html">torch_selu_()</a></code>
+
         </dt>
         <dd>Selu_</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sgn.html">torch_sgn()</a></code> 
+
+          <code><a href="torch_sgn.html">torch_sgn()</a></code>
+
         </dt>
         <dd>Sgn</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sigmoid.html">torch_sigmoid()</a></code> 
+
+          <code><a href="torch_sigmoid.html">torch_sigmoid()</a></code>
+
         </dt>
         <dd>Sigmoid</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sign.html">torch_sign()</a></code> 
+
+          <code><a href="torch_sign.html">torch_sign()</a></code>
+
         </dt>
         <dd>Sign</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_signbit.html">torch_signbit()</a></code> 
+
+          <code><a href="torch_signbit.html">torch_signbit()</a></code>
+
         </dt>
         <dd>Signbit</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sin.html">torch_sin()</a></code> 
+
+          <code><a href="torch_sin.html">torch_sin()</a></code>
+
         </dt>
         <dd>Sin</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sinh.html">torch_sinh()</a></code> 
+
+          <code><a href="torch_sinh.html">torch_sinh()</a></code>
+
         </dt>
         <dd>Sinh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_slogdet.html">torch_slogdet()</a></code> 
+
+          <code><a href="torch_slogdet.html">torch_slogdet()</a></code>
+
         </dt>
         <dd>Slogdet</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sort.html">torch_sort</a></code> 
+
+          <code><a href="torch_sort.html">torch_sort</a></code>
+
         </dt>
         <dd>Sort</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code> 
+
+          <code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code>
+
         </dt>
         <dd>Sparse_coo_tensor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_split.html">torch_split()</a></code> 
+
+          <code><a href="torch_split.html">torch_split()</a></code>
+
         </dt>
         <dd>Split</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sqrt.html">torch_sqrt()</a></code> 
+
+          <code><a href="torch_sqrt.html">torch_sqrt()</a></code>
+
         </dt>
         <dd>Sqrt</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_square.html">torch_square()</a></code> 
+
+          <code><a href="torch_square.html">torch_square()</a></code>
+
         </dt>
         <dd>Square</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_squeeze.html">torch_squeeze()</a></code> 
+
+          <code><a href="torch_squeeze.html">torch_squeeze()</a></code>
+
         </dt>
         <dd>Squeeze</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_stack.html">torch_stack()</a></code> 
+
+          <code><a href="torch_stack.html">torch_stack()</a></code>
+
         </dt>
         <dd>Stack</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_std.html">torch_std()</a></code> 
+
+          <code><a href="torch_std.html">torch_std()</a></code>
+
         </dt>
         <dd>Std</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_std_mean.html">torch_std_mean()</a></code> 
+
+          <code><a href="torch_std_mean.html">torch_std_mean()</a></code>
+
         </dt>
         <dd>Std_mean</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_stft.html">torch_stft()</a></code> 
+
+          <code><a href="torch_stft.html">torch_stft()</a></code>
+
         </dt>
         <dd>Stft</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sub.html">torch_sub()</a></code> 
+
+          <code><a href="torch_sub.html">torch_sub()</a></code>
+
         </dt>
         <dd>Sub</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_subtract.html">torch_subtract()</a></code> 
+
+          <code><a href="torch_subtract.html">torch_subtract()</a></code>
+
         </dt>
         <dd>Subtract</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_sum.html">torch_sum()</a></code> 
+
+          <code><a href="torch_sum.html">torch_sum()</a></code>
+
         </dt>
         <dd>Sum</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_svd.html">torch_svd()</a></code> 
+
+          <code><a href="torch_svd.html">torch_svd()</a></code>
+
         </dt>
         <dd>Svd</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_t.html">torch_t()</a></code> 
+
+          <code><a href="torch_t.html">torch_t()</a></code>
+
         </dt>
         <dd>T</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_take.html">torch_take()</a></code> 
+
+          <code><a href="torch_take.html">torch_take()</a></code>
+
         </dt>
         <dd>Take</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tan.html">torch_tan()</a></code> 
+
+          <code><a href="torch_take_along_dim.html">torch_take_along_dim()</a></code>
+
+        </dt>
+        <dd>Selects values from input at the 1-dimensional indices from indices along the given dim.</dd>
+      </dl><dl><dt>
+
+          <code><a href="torch_tan.html">torch_tan()</a></code>
+
         </dt>
         <dd>Tan</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tanh.html">torch_tanh()</a></code> 
+
+          <code><a href="torch_tanh.html">torch_tanh()</a></code>
+
         </dt>
         <dd>Tanh</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tensor.html">torch_tensor()</a></code> 
+
+          <code><a href="torch_tensor.html">torch_tensor()</a></code>
+
         </dt>
         <dd>Converts R objects to a torch tensor</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tensor_from_buffer.html">torch_tensor_from_buffer()</a></code> <code><a href="torch_tensor_from_buffer.html">buffer_from_torch_tensor()</a></code> 
+
+          <code><a href="torch_tensor_from_buffer.html">torch_tensor_from_buffer()</a></code> <code><a href="torch_tensor_from_buffer.html">buffer_from_torch_tensor()</a></code>
+
         </dt>
         <dd>Creates a tensor from a buffer of memory</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tensordot.html">torch_tensordot()</a></code> 
+
+          <code><a href="torch_tensordot.html">torch_tensordot()</a></code>
+
         </dt>
         <dd>Tensordot</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_threshold_.html">torch_threshold_()</a></code> 
+
+          <code><a href="torch_threshold_.html">torch_threshold_()</a></code>
+
         </dt>
         <dd>Threshold_</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_topk.html">torch_topk()</a></code> 
+
+          <code><a href="torch_topk.html">torch_topk()</a></code>
+
         </dt>
         <dd>Topk</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_trace.html">torch_trace()</a></code> 
+
+          <code><a href="torch_trace.html">torch_trace()</a></code>
+
         </dt>
         <dd>Trace</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_transpose.html">torch_transpose()</a></code> 
+
+          <code><a href="torch_transpose.html">torch_transpose()</a></code>
+
         </dt>
         <dd>Transpose</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_trapz.html">torch_trapz()</a></code> 
+
+          <code><a href="torch_trapz.html">torch_trapz()</a></code>
+
         </dt>
         <dd>Trapz</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code> 
+
+          <code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code>
+
         </dt>
         <dd>Triangular_solve</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tril.html">torch_tril()</a></code> 
+
+          <code><a href="torch_tril.html">torch_tril()</a></code>
+
         </dt>
         <dd>Tril</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_tril_indices.html">torch_tril_indices()</a></code> 
+
+          <code><a href="torch_tril_indices.html">torch_tril_indices()</a></code>
+
         </dt>
         <dd>Tril_indices</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_triu.html">torch_triu()</a></code> 
+
+          <code><a href="torch_triu.html">torch_triu()</a></code>
+
         </dt>
         <dd>Triu</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_triu_indices.html">torch_triu_indices()</a></code> 
+
+          <code><a href="torch_triu_indices.html">torch_triu_indices()</a></code>
+
         </dt>
         <dd>Triu_indices</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_true_divide.html">torch_true_divide()</a></code> 
+
+          <code><a href="torch_true_divide.html">torch_true_divide()</a></code>
+
         </dt>
         <dd>TRUE_divide</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_trunc.html">torch_trunc()</a></code> 
+
+          <code><a href="torch_trunc.html">torch_trunc()</a></code>
+
         </dt>
         <dd>Trunc</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_unbind.html">torch_unbind()</a></code> 
+
+          <code><a href="torch_unbind.html">torch_unbind()</a></code>
+
         </dt>
         <dd>Unbind</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code> 
+
+          <code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code>
+
         </dt>
         <dd>Unique_consecutive</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_unsafe_chunk.html">torch_unsafe_chunk()</a></code> 
+
+          <code><a href="torch_unsafe_chunk.html">torch_unsafe_chunk()</a></code>
+
         </dt>
         <dd>Unsafe_chunk</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_unsafe_split.html">torch_unsafe_split()</a></code> 
+
+          <code><a href="torch_unsafe_split.html">torch_unsafe_split()</a></code>
+
         </dt>
         <dd>Unsafe_split</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code> 
+
+          <code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code>
+
         </dt>
         <dd>Unsqueeze</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_vander.html">torch_vander()</a></code> 
+
+          <code><a href="torch_vander.html">torch_vander()</a></code>
+
         </dt>
         <dd>Vander</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_var.html">torch_var()</a></code> 
+
+          <code><a href="torch_var.html">torch_var()</a></code>
+
         </dt>
         <dd>Var</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_var_mean.html">torch_var_mean()</a></code> 
+
+          <code><a href="torch_var_mean.html">torch_var_mean()</a></code>
+
         </dt>
         <dd>Var_mean</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_vdot.html">torch_vdot()</a></code> 
+
+          <code><a href="torch_vdot.html">torch_vdot()</a></code>
+
         </dt>
         <dd>Vdot</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_view_as_complex.html">torch_view_as_complex()</a></code> 
+
+          <code><a href="torch_view_as_complex.html">torch_view_as_complex()</a></code>
+
         </dt>
         <dd>View_as_complex</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_view_as_real.html">torch_view_as_real()</a></code> 
+
+          <code><a href="torch_view_as_real.html">torch_view_as_real()</a></code>
+
         </dt>
         <dd>View_as_real</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_vstack.html">torch_vstack()</a></code> 
+
+          <code><a href="torch_vstack.html">torch_vstack()</a></code>
+
         </dt>
         <dd>Vstack</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_where.html">torch_where()</a></code> 
+
+          <code><a href="torch_where.html">torch_where()</a></code>
+
         </dt>
         <dd>Where</dd>
       </dl><dl><dt>
-          
-          <code><a href="broadcast_all.html">broadcast_all()</a></code> 
+
+          <code><a href="broadcast_all.html">broadcast_all()</a></code>
+
         </dt>
         <dd>Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules:</dd>
       </dl></div><div class="section level2">
       <h2 id="neural-network-modules">Neural network modules<a class="anchor" aria-label="anchor" href="#neural-network-modules"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> 
+
+          <code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code>
+
         </dt>
         <dd>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> 
+
+          <code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code>
+
         </dt>
         <dd>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> 
+
+          <code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code>
+
         </dt>
         <dd>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> 
+
+          <code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code>
+
         </dt>
         <dd>AdaptiveLogSoftmaxWithLoss module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> 
+
+          <code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code>
+
         </dt>
         <dd>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> 
+
+          <code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code>
+
         </dt>
         <dd>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> 
+
+          <code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code>
+
         </dt>
         <dd>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> 
+
+          <code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code>
+
         </dt>
         <dd>Applies a 1D average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> 
+
+          <code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code>
+
         </dt>
         <dd>Applies a 2D average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> 
+
+          <code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code>
+
         </dt>
         <dd>Applies a 3D average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> 
+
+          <code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code>
+
         </dt>
         <dd>BatchNorm1D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> 
+
+          <code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code>
+
         </dt>
         <dd>BatchNorm2D</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_batch_norm3d.html">nn_batch_norm3d()</a></code> 
+
+          <code><a href="nn_batch_norm3d.html">nn_batch_norm3d()</a></code>
+
         </dt>
         <dd>BatchNorm3D</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> 
+
+          <code><a href="nn_bce_loss.html">nn_bce_loss()</a></code>
+
         </dt>
         <dd>Binary cross entropy loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_bce_with_logits_loss.html">nn_bce_with_logits_loss()</a></code> 
+
+          <code><a href="nn_bce_with_logits_loss.html">nn_bce_with_logits_loss()</a></code>
+
         </dt>
         <dd>BCE with logits loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_bilinear.html">nn_bilinear()</a></code> 
+
+          <code><a href="nn_bilinear.html">nn_bilinear()</a></code>
+
         </dt>
         <dd>Bilinear module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_buffer.html">nn_buffer()</a></code> 
+
+          <code><a href="nn_buffer.html">nn_buffer()</a></code>
+
         </dt>
         <dd>Creates a nn_buffer</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_celu.html">nn_celu()</a></code> 
+
+          <code><a href="nn_celu.html">nn_celu()</a></code>
+
         </dt>
         <dd>CELU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_contrib_sparsemax.html">nn_contrib_sparsemax()</a></code> 
+
+          <code><a href="nn_contrib_sparsemax.html">nn_contrib_sparsemax()</a></code>
+
         </dt>
         <dd>Sparsemax activation</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_conv1d.html">nn_conv1d()</a></code> 
+
+          <code><a href="nn_conv1d.html">nn_conv1d()</a></code>
+
         </dt>
         <dd>Conv1D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_conv2d.html">nn_conv2d()</a></code> 
+
+          <code><a href="nn_conv2d.html">nn_conv2d()</a></code>
+
         </dt>
         <dd>Conv2D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_conv3d.html">nn_conv3d()</a></code> 
+
+          <code><a href="nn_conv3d.html">nn_conv3d()</a></code>
+
         </dt>
         <dd>Conv3D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> 
+
+          <code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code>
+
         </dt>
         <dd>ConvTranspose1D</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> 
+
+          <code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code>
+
         </dt>
         <dd>ConvTranpose2D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> 
+
+          <code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code>
+
         </dt>
         <dd>ConvTranpose3D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_cosine_embedding_loss.html">nn_cosine_embedding_loss()</a></code> 
+
+          <code><a href="nn_cosine_embedding_loss.html">nn_cosine_embedding_loss()</a></code>
+
         </dt>
         <dd>Cosine embedding loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> 
+
+          <code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code>
+
         </dt>
         <dd>CrossEntropyLoss module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_ctc_loss.html">nn_ctc_loss()</a></code> 
+
+          <code><a href="nn_ctc_loss.html">nn_ctc_loss()</a></code>
+
         </dt>
         <dd>The Connectionist Temporal Classification loss.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_dropout.html">nn_dropout()</a></code> 
+
+          <code><a href="nn_dropout.html">nn_dropout()</a></code>
+
         </dt>
         <dd>Dropout module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> 
+
+          <code><a href="nn_dropout2d.html">nn_dropout2d()</a></code>
+
         </dt>
         <dd>Dropout2D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> 
+
+          <code><a href="nn_dropout3d.html">nn_dropout3d()</a></code>
+
         </dt>
         <dd>Dropout3D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_elu.html">nn_elu()</a></code> 
+
+          <code><a href="nn_elu.html">nn_elu()</a></code>
+
         </dt>
         <dd>ELU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_embedding.html">nn_embedding()</a></code> 
+
+          <code><a href="nn_embedding.html">nn_embedding()</a></code>
+
         </dt>
         <dd>Embedding module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_embedding_bag.html">nn_embedding_bag()</a></code> 
+
+          <code><a href="nn_embedding_bag.html">nn_embedding_bag()</a></code>
+
         </dt>
         <dd>Embedding bag module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_flatten.html">nn_flatten()</a></code> 
+
+          <code><a href="nn_flatten.html">nn_flatten()</a></code>
+
         </dt>
         <dd>Flattens a contiguous range of dims into a tensor.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> 
+
+          <code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code>
+
         </dt>
         <dd>Applies a 2D fractional max pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> 
+
+          <code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code>
+
         </dt>
         <dd>Applies a 3D fractional max pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_gelu.html">nn_gelu()</a></code> 
+
+          <code><a href="nn_gelu.html">nn_gelu()</a></code>
+
         </dt>
         <dd>GELU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_glu.html">nn_glu()</a></code> 
+
+          <code><a href="nn_glu.html">nn_glu()</a></code>
+
         </dt>
         <dd>GLU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_group_norm.html">nn_group_norm()</a></code> 
+
+          <code><a href="nn_group_norm.html">nn_group_norm()</a></code>
+
         </dt>
         <dd>Group normalization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_gru.html">nn_gru()</a></code> 
+
+          <code><a href="nn_gru.html">nn_gru()</a></code>
+
         </dt>
         <dd>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> 
+
+          <code><a href="nn_hardshrink.html">nn_hardshrink()</a></code>
+
         </dt>
         <dd>Hardshwink module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> 
+
+          <code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code>
+
         </dt>
         <dd>Hardsigmoid module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_hardswish.html">nn_hardswish()</a></code> 
+
+          <code><a href="nn_hardswish.html">nn_hardswish()</a></code>
+
         </dt>
         <dd>Hardswish module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> 
+
+          <code><a href="nn_hardtanh.html">nn_hardtanh()</a></code>
+
         </dt>
         <dd>Hardtanh module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_hinge_embedding_loss.html">nn_hinge_embedding_loss()</a></code> 
+
+          <code><a href="nn_hinge_embedding_loss.html">nn_hinge_embedding_loss()</a></code>
+
         </dt>
         <dd>Hinge embedding loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_identity.html">nn_identity()</a></code> 
+
+          <code><a href="nn_identity.html">nn_identity()</a></code>
+
         </dt>
         <dd>Identity module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> 
+
+          <code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code>
+
         </dt>
         <dd>Calculate gain</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> 
+
+          <code><a href="nn_init_constant_.html">nn_init_constant_()</a></code>
+
         </dt>
         <dd>Constant initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> 
+
+          <code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code>
+
         </dt>
         <dd>Dirac initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> 
+
+          <code><a href="nn_init_eye_.html">nn_init_eye_()</a></code>
+
         </dt>
         <dd>Eye initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> 
+
+          <code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code>
+
         </dt>
         <dd>Kaiming normal initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> 
+
+          <code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code>
+
         </dt>
         <dd>Kaiming uniform initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> 
+
+          <code><a href="nn_init_normal_.html">nn_init_normal_()</a></code>
+
         </dt>
         <dd>Normal initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> 
+
+          <code><a href="nn_init_ones_.html">nn_init_ones_()</a></code>
+
         </dt>
         <dd>Ones initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> 
+
+          <code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code>
+
         </dt>
         <dd>Orthogonal initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> 
+
+          <code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code>
+
         </dt>
         <dd>Sparse initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> 
+
+          <code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code>
+
         </dt>
         <dd>Truncated normal initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> 
+
+          <code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code>
+
         </dt>
         <dd>Uniform initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> 
+
+          <code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code>
+
         </dt>
         <dd>Xavier normal initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> 
+
+          <code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code>
+
         </dt>
         <dd>Xavier uniform initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> 
+
+          <code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code>
+
         </dt>
         <dd>Zeros initialization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_kl_div_loss.html">nn_kl_div_loss()</a></code> 
+
+          <code><a href="nn_kl_div_loss.html">nn_kl_div_loss()</a></code>
+
         </dt>
         <dd>Kullback-Leibler divergence loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_l1_loss.html">nn_l1_loss()</a></code> 
+
+          <code><a href="nn_l1_loss.html">nn_l1_loss()</a></code>
+
         </dt>
         <dd>L1 loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_layer_norm.html">nn_layer_norm()</a></code> 
+
+          <code><a href="nn_layer_norm.html">nn_layer_norm()</a></code>
+
         </dt>
         <dd>Layer normalization</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> 
+
+          <code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code>
+
         </dt>
         <dd>LeakyReLU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_linear.html">nn_linear()</a></code> 
+
+          <code><a href="nn_linear.html">nn_linear()</a></code>
+
         </dt>
         <dd>Linear module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> 
+
+          <code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code>
+
         </dt>
         <dd>LogSigmoid module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> 
+
+          <code><a href="nn_log_softmax.html">nn_log_softmax()</a></code>
+
         </dt>
         <dd>LogSoftmax module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> 
+
+          <code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code>
+
         </dt>
         <dd>Applies a 1D power-average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> 
+
+          <code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code>
+
         </dt>
         <dd>Applies a 2D power-average pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_lstm.html">nn_lstm()</a></code> 
+
+          <code><a href="nn_lstm.html">nn_lstm()</a></code>
+
         </dt>
         <dd>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_margin_ranking_loss.html">nn_margin_ranking_loss()</a></code> 
+
+          <code><a href="nn_margin_ranking_loss.html">nn_margin_ranking_loss()</a></code>
+
         </dt>
         <dd>Margin ranking loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> 
+
+          <code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code>
+
         </dt>
         <dd>MaxPool1D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> 
+
+          <code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code>
+
         </dt>
         <dd>MaxPool2D module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> 
+
+          <code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code>
+
         </dt>
         <dd>Applies a 3D max pooling over an input signal composed of several input planes.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> 
+
+          <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code>
+
         </dt>
         <dd>Computes a partial inverse of <code>MaxPool1d</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> 
+
+          <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code>
+
         </dt>
         <dd>Computes a partial inverse of <code>MaxPool2d</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> 
+
+          <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code>
+
         </dt>
         <dd>Computes a partial inverse of <code>MaxPool3d</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_module.html">nn_module()</a></code> 
+
+          <code><a href="nn_module.html">nn_module()</a></code>
+
         </dt>
         <dd>Base class for all neural network modules.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_module_dict.html">nn_module_dict()</a></code> 
+
+          <code><a href="nn_module_dict.html">nn_module_dict()</a></code>
+
         </dt>
         <dd>Container that allows named values</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_module_list.html">nn_module_list()</a></code> 
+
+          <code><a href="nn_module_list.html">nn_module_list()</a></code>
+
         </dt>
         <dd>Holds submodules in a list.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_mse_loss.html">nn_mse_loss()</a></code> 
+
+          <code><a href="nn_mse_loss.html">nn_mse_loss()</a></code>
+
         </dt>
         <dd>MSE loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_multi_margin_loss.html">nn_multi_margin_loss()</a></code> 
+
+          <code><a href="nn_multi_margin_loss.html">nn_multi_margin_loss()</a></code>
+
         </dt>
         <dd>Multi margin loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> 
+
+          <code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code>
+
         </dt>
         <dd>MultiHead attention</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_multilabel_margin_loss.html">nn_multilabel_margin_loss()</a></code> 
+
+          <code><a href="nn_multilabel_margin_loss.html">nn_multilabel_margin_loss()</a></code>
+
         </dt>
         <dd>Multilabel margin loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_multilabel_soft_margin_loss.html">nn_multilabel_soft_margin_loss()</a></code> 
+
+          <code><a href="nn_multilabel_soft_margin_loss.html">nn_multilabel_soft_margin_loss()</a></code>
+
         </dt>
         <dd>Multi label soft margin loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_nll_loss.html">nn_nll_loss()</a></code> 
+
+          <code><a href="nn_nll_loss.html">nn_nll_loss()</a></code>
+
         </dt>
         <dd>Nll loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> 
+
+          <code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code>
+
         </dt>
         <dd>Pairwise distance</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_parameter.html">nn_parameter()</a></code> 
+
+          <code><a href="nn_parameter.html">nn_parameter()</a></code>
+
         </dt>
         <dd>Creates an <code>nn_parameter</code></dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_poisson_nll_loss.html">nn_poisson_nll_loss()</a></code> 
+
+          <code><a href="nn_poisson_nll_loss.html">nn_poisson_nll_loss()</a></code>
+
         </dt>
         <dd>Poisson NLL loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_prelu.html">nn_prelu()</a></code> 
+
+          <code><a href="nn_prelu.html">nn_prelu()</a></code>
+
         </dt>
         <dd>PReLU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_prune_head.html">nn_prune_head()</a></code> 
+
+          <code><a href="nn_prune_head.html">nn_prune_head()</a></code>
+
         </dt>
         <dd>Prune top layer(s) of a network</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_relu.html">nn_relu()</a></code> 
+
+          <code><a href="nn_relu.html">nn_relu()</a></code>
+
         </dt>
         <dd>ReLU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_relu6.html">nn_relu6()</a></code> 
+
+          <code><a href="nn_relu6.html">nn_relu6()</a></code>
+
         </dt>
         <dd>ReLu6 module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_rnn.html">nn_rnn()</a></code> 
+
+          <code><a href="nn_rnn.html">nn_rnn()</a></code>
+
         </dt>
         <dd>RNN module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_rrelu.html">nn_rrelu()</a></code> 
+
+          <code><a href="nn_rrelu.html">nn_rrelu()</a></code>
+
         </dt>
         <dd>RReLU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_selu.html">nn_selu()</a></code> 
+
+          <code><a href="nn_selu.html">nn_selu()</a></code>
+
         </dt>
         <dd>SELU module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_sequential.html">nn_sequential()</a></code> 
+
+          <code><a href="nn_sequential.html">nn_sequential()</a></code>
+
         </dt>
         <dd>A sequential container</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> 
+
+          <code><a href="nn_sigmoid.html">nn_sigmoid()</a></code>
+
         </dt>
         <dd>Sigmoid module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_silu.html">nn_silu()</a></code> 
+
+          <code><a href="nn_silu.html">nn_silu()</a></code>
+
         </dt>
         <dd>Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_smooth_l1_loss.html">nn_smooth_l1_loss()</a></code> 
+
+          <code><a href="nn_smooth_l1_loss.html">nn_smooth_l1_loss()</a></code>
+
         </dt>
         <dd>Smooth L1 loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_soft_margin_loss.html">nn_soft_margin_loss()</a></code> 
+
+          <code><a href="nn_soft_margin_loss.html">nn_soft_margin_loss()</a></code>
+
         </dt>
         <dd>Soft margin loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_softmax.html">nn_softmax()</a></code> 
+
+          <code><a href="nn_softmax.html">nn_softmax()</a></code>
+
         </dt>
         <dd>Softmax module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> 
+
+          <code><a href="nn_softmax2d.html">nn_softmax2d()</a></code>
+
         </dt>
         <dd>Softmax2d module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_softmin.html">nn_softmin()</a></code> 
+
+          <code><a href="nn_softmin.html">nn_softmin()</a></code>
+
         </dt>
         <dd>Softmin</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_softplus.html">nn_softplus()</a></code> 
+
+          <code><a href="nn_softplus.html">nn_softplus()</a></code>
+
         </dt>
         <dd>Softplus module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_softshrink.html">nn_softshrink()</a></code> 
+
+          <code><a href="nn_softshrink.html">nn_softshrink()</a></code>
+
         </dt>
         <dd>Softshrink module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_softsign.html">nn_softsign()</a></code> 
+
+          <code><a href="nn_softsign.html">nn_softsign()</a></code>
+
         </dt>
         <dd>Softsign module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_tanh.html">nn_tanh()</a></code> 
+
+          <code><a href="nn_tanh.html">nn_tanh()</a></code>
+
         </dt>
         <dd>Tanh module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> 
+
+          <code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code>
+
         </dt>
         <dd>Tanhshrink module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_threshold.html">nn_threshold()</a></code> 
+
+          <code><a href="nn_threshold.html">nn_threshold()</a></code>
+
         </dt>
         <dd>Threshold module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_triplet_margin_loss.html">nn_triplet_margin_loss()</a></code> 
+
+          <code><a href="nn_triplet_margin_loss.html">nn_triplet_margin_loss()</a></code>
+
         </dt>
         <dd>Triplet margin loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code> 
+
+          <code><a href="nn_triplet_margin_with_distance_loss.html">nn_triplet_margin_with_distance_loss()</a></code>
+
         </dt>
         <dd>Triplet margin with distance loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_unflatten.html">nn_unflatten()</a></code> 
+
+          <code><a href="nn_unflatten.html">nn_unflatten()</a></code>
+
         </dt>
         <dd>Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_upsample.html">nn_upsample()</a></code> 
+
+          <code><a href="nn_upsample.html">nn_upsample()</a></code>
+
         </dt>
         <dd>Upsample module</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_clip_grad_norm_.html">nn_utils_clip_grad_norm_()</a></code> 
+
+          <code><a href="nn_utils_clip_grad_norm_.html">nn_utils_clip_grad_norm_()</a></code>
+
         </dt>
         <dd>Clips gradient norm of an iterable of parameters.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_clip_grad_value_.html">nn_utils_clip_grad_value_()</a></code> 
+
+          <code><a href="nn_utils_clip_grad_value_.html">nn_utils_clip_grad_value_()</a></code>
+
         </dt>
         <dd>Clips gradient of an iterable of parameters at specified value.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> 
+
+          <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code>
+
         </dt>
         <dd>Packs a Tensor containing padded sequences of variable length.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> 
+
+          <code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code>
+
         </dt>
         <dd>Packs a list of variable length Tensors</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> 
+
+          <code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code>
+
         </dt>
         <dd>Pads a packed batch of variable length sequences.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> 
+
+          <code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code>
+
         </dt>
         <dd>Pad a list of variable length Tensors with <code>padding_value</code></dd>
       </dl><dl><dt>
-          
-          <code><a href="nn_utils_weight_norm.html">nn_utils_weight_norm</a></code> 
+
+          <code><a href="nn_utils_weight_norm.html">nn_utils_weight_norm</a></code>
+
         </dt>
         <dd>nn_utils_weight_norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_nn_module.html">is_nn_module()</a></code> 
+
+          <code><a href="is_nn_module.html">is_nn_module()</a></code>
+
         </dt>
         <dd>Checks if the object is an nn_module</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_nn_parameter.html">is_nn_parameter()</a></code> 
+
+          <code><a href="is_nn_parameter.html">is_nn_parameter()</a></code>
+
         </dt>
         <dd>Checks if an object is a nn_parameter</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_nn_buffer.html">is_nn_buffer()</a></code> 
+
+          <code><a href="is_nn_buffer.html">is_nn_buffer()</a></code>
+
         </dt>
         <dd>Checks if the object is a nn_buffer</dd>
       </dl></div><div class="section level2">
       <h2 id="neural-networks-functional-module">Neural networks functional module<a class="anchor" aria-label="anchor" href="#neural-networks-functional-module"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> 
+
+          <code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code>
+
         </dt>
         <dd>Adaptive_avg_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> 
+
+          <code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code>
+
         </dt>
         <dd>Adaptive_avg_pool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> 
+
+          <code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code>
+
         </dt>
         <dd>Adaptive_avg_pool3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> 
+
+          <code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code>
+
         </dt>
         <dd>Adaptive_max_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> 
+
+          <code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code>
+
         </dt>
         <dd>Adaptive_max_pool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> 
+
+          <code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code>
+
         </dt>
         <dd>Adaptive_max_pool3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> 
+
+          <code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code>
+
         </dt>
         <dd>Affine_grid</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> 
+
+          <code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code>
+
         </dt>
         <dd>Alpha_dropout</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> 
+
+          <code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code>
+
         </dt>
         <dd>Avg_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> 
+
+          <code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code>
+
         </dt>
         <dd>Avg_pool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> 
+
+          <code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code>
+
         </dt>
         <dd>Avg_pool3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> 
+
+          <code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code>
+
         </dt>
         <dd>Batch_norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> 
+
+          <code><a href="nnf_bilinear.html">nnf_bilinear()</a></code>
+
         </dt>
         <dd>Bilinear</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> 
+
+          <code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code>
+
         </dt>
         <dd>Binary_cross_entropy</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> 
+
+          <code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code>
+
         </dt>
         <dd>Binary_cross_entropy_with_logits</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> 
+
+          <code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code>
+
         </dt>
         <dd>Celu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_contrib_sparsemax.html">nnf_contrib_sparsemax()</a></code> 
+
+          <code><a href="nnf_contrib_sparsemax.html">nnf_contrib_sparsemax()</a></code>
+
         </dt>
         <dd>Sparsemax</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> 
+
+          <code><a href="nnf_conv1d.html">nnf_conv1d()</a></code>
+
         </dt>
         <dd>Conv1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> 
+
+          <code><a href="nnf_conv2d.html">nnf_conv2d()</a></code>
+
         </dt>
         <dd>Conv2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> 
+
+          <code><a href="nnf_conv3d.html">nnf_conv3d()</a></code>
+
         </dt>
         <dd>Conv3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> 
+
+          <code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code>
+
         </dt>
         <dd>Conv_tbc</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> 
+
+          <code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code>
+
         </dt>
         <dd>Conv_transpose1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> 
+
+          <code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code>
+
         </dt>
         <dd>Conv_transpose2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> 
+
+          <code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code>
+
         </dt>
         <dd>Conv_transpose3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> 
+
+          <code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code>
+
         </dt>
         <dd>Cosine_embedding_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> 
+
+          <code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code>
+
         </dt>
         <dd>Cosine_similarity</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> 
+
+          <code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code>
+
         </dt>
         <dd>Cross_entropy</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> 
+
+          <code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code>
+
         </dt>
         <dd>Ctc_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_dropout.html">nnf_dropout()</a></code> 
+
+          <code><a href="nnf_dropout.html">nnf_dropout()</a></code>
+
         </dt>
         <dd>Dropout</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> 
+
+          <code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code>
+
         </dt>
         <dd>Dropout2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> 
+
+          <code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code>
+
         </dt>
         <dd>Dropout3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> 
+
+          <code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code>
+
         </dt>
         <dd>Elu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_embedding.html">nnf_embedding()</a></code> 
+
+          <code><a href="nnf_embedding.html">nnf_embedding()</a></code>
+
         </dt>
         <dd>Embedding</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> 
+
+          <code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code>
+
         </dt>
         <dd>Embedding_bag</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_fold.html">nnf_fold()</a></code> 
+
+          <code><a href="nnf_fold.html">nnf_fold()</a></code>
+
         </dt>
         <dd>Fold</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> 
+
+          <code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code>
+
         </dt>
         <dd>Fractional_max_pool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> 
+
+          <code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code>
+
         </dt>
         <dd>Fractional_max_pool3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_gelu.html">nnf_gelu()</a></code> 
+
+          <code><a href="nnf_gelu.html">nnf_gelu()</a></code>
+
         </dt>
         <dd>Gelu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_glu.html">nnf_glu()</a></code> 
+
+          <code><a href="nnf_glu.html">nnf_glu()</a></code>
+
         </dt>
         <dd>Glu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> 
+
+          <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code>
+
         </dt>
         <dd>Grid_sample</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> 
+
+          <code><a href="nnf_group_norm.html">nnf_group_norm()</a></code>
+
         </dt>
         <dd>Group_norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> 
+
+          <code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code>
+
         </dt>
         <dd>Gumbel_softmax</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> 
+
+          <code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code>
+
         </dt>
         <dd>Hardshrink</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> 
+
+          <code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code>
+
         </dt>
         <dd>Hardsigmoid</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> 
+
+          <code><a href="nnf_hardswish.html">nnf_hardswish()</a></code>
+
         </dt>
         <dd>Hardswish</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> 
+
+          <code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code>
+
         </dt>
         <dd>Hardtanh</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> 
+
+          <code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code>
+
         </dt>
         <dd>Hinge_embedding_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> 
+
+          <code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code>
+
         </dt>
         <dd>Instance_norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> 
+
+          <code><a href="nnf_interpolate.html">nnf_interpolate()</a></code>
+
         </dt>
         <dd>Interpolate</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> 
+
+          <code><a href="nnf_kl_div.html">nnf_kl_div()</a></code>
+
         </dt>
         <dd>Kl_div</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> 
+
+          <code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code>
+
         </dt>
         <dd>L1_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> 
+
+          <code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code>
+
         </dt>
         <dd>Layer_norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> 
+
+          <code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code>
+
         </dt>
         <dd>Leaky_relu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_linear.html">nnf_linear()</a></code> 
+
+          <code><a href="nnf_linear.html">nnf_linear()</a></code>
+
         </dt>
         <dd>Linear</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> 
+
+          <code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code>
+
         </dt>
         <dd>Local_response_norm</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> 
+
+          <code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code>
+
         </dt>
         <dd>Log_softmax</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> 
+
+          <code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code>
+
         </dt>
         <dd>Logsigmoid</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> 
+
+          <code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code>
+
         </dt>
         <dd>Lp_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> 
+
+          <code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code>
+
         </dt>
         <dd>Lp_pool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> 
+
+          <code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code>
+
         </dt>
         <dd>Margin_ranking_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> 
+
+          <code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code>
+
         </dt>
         <dd>Max_pool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> 
+
+          <code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code>
+
         </dt>
         <dd>Max_pool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> 
+
+          <code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code>
+
         </dt>
         <dd>Max_pool3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> 
+
+          <code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code>
+
         </dt>
         <dd>Max_unpool1d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> 
+
+          <code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code>
+
         </dt>
         <dd>Max_unpool2d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> 
+
+          <code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code>
+
         </dt>
         <dd>Max_unpool3d</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> 
+
+          <code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code>
+
         </dt>
         <dd>Mse_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> 
+
+          <code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code>
+
         </dt>
         <dd>Multi head attention forward</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> 
+
+          <code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code>
+
         </dt>
         <dd>Multi_margin_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> 
+
+          <code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code>
+
         </dt>
         <dd>Multilabel_margin_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> 
+
+          <code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code>
+
         </dt>
         <dd>Multilabel_soft_margin_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> 
+
+          <code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code>
+
         </dt>
         <dd>Nll_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_normalize.html">nnf_normalize()</a></code> 
+
+          <code><a href="nnf_normalize.html">nnf_normalize()</a></code>
+
         </dt>
         <dd>Normalize</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> 
+
+          <code><a href="nnf_one_hot.html">nnf_one_hot()</a></code>
+
         </dt>
         <dd>One_hot</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_pad.html">nnf_pad()</a></code> 
+
+          <code><a href="nnf_pad.html">nnf_pad()</a></code>
+
         </dt>
         <dd>Pad</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> 
+
+          <code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code>
+
         </dt>
         <dd>Pairwise_distance</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_pdist.html">nnf_pdist()</a></code> 
+
+          <code><a href="nnf_pdist.html">nnf_pdist()</a></code>
+
         </dt>
         <dd>Pdist</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> 
+
+          <code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code>
+
         </dt>
         <dd>Pixel_shuffle</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> 
+
+          <code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code>
+
         </dt>
         <dd>Poisson_nll_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_prelu.html">nnf_prelu()</a></code> 
+
+          <code><a href="nnf_prelu.html">nnf_prelu()</a></code>
+
         </dt>
         <dd>Prelu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> 
+
+          <code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code>
+
         </dt>
         <dd>Relu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_relu6.html">nnf_relu6()</a></code> 
+
+          <code><a href="nnf_relu6.html">nnf_relu6()</a></code>
+
         </dt>
         <dd>Relu6</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> 
+
+          <code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code>
+
         </dt>
         <dd>Rrelu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> 
+
+          <code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code>
+
         </dt>
         <dd>Selu</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> 
+
+          <code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code>
+
         </dt>
         <dd>Sigmoid</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_silu.html">nnf_silu()</a></code> 
+
+          <code><a href="nnf_silu.html">nnf_silu()</a></code>
+
         </dt>
         <dd>Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See <code><a href="../reference/nn_silu.html">nn_silu()</a></code> for more information.</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> 
+
+          <code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code>
+
         </dt>
         <dd>Smooth_l1_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> 
+
+          <code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code>
+
         </dt>
         <dd>Soft_margin_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_softmax.html">nnf_softmax()</a></code> 
+
+          <code><a href="nnf_softmax.html">nnf_softmax()</a></code>
+
         </dt>
         <dd>Softmax</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_softmin.html">nnf_softmin()</a></code> 
+
+          <code><a href="nnf_softmin.html">nnf_softmin()</a></code>
+
         </dt>
         <dd>Softmin</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_softplus.html">nnf_softplus()</a></code> 
+
+          <code><a href="nnf_softplus.html">nnf_softplus()</a></code>
+
         </dt>
         <dd>Softplus</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> 
+
+          <code><a href="nnf_softshrink.html">nnf_softshrink()</a></code>
+
         </dt>
         <dd>Softshrink</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_softsign.html">nnf_softsign()</a></code> 
+
+          <code><a href="nnf_softsign.html">nnf_softsign()</a></code>
+
         </dt>
         <dd>Softsign</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> 
+
+          <code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code>
+
         </dt>
         <dd>Tanhshrink</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> 
+
+          <code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code>
+
         </dt>
         <dd>Threshold</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> 
+
+          <code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code>
+
         </dt>
         <dd>Triplet_margin_loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_triplet_margin_with_distance_loss.html">nnf_triplet_margin_with_distance_loss()</a></code> 
+
+          <code><a href="nnf_triplet_margin_with_distance_loss.html">nnf_triplet_margin_with_distance_loss()</a></code>
+
         </dt>
         <dd>Triplet margin with distance loss</dd>
       </dl><dl><dt>
-          
-          <code><a href="nnf_unfold.html">nnf_unfold()</a></code> 
+
+          <code><a href="nnf_unfold.html">nnf_unfold()</a></code>
+
         </dt>
         <dd>Unfold</dd>
-      </dl></div><div id="" class="section level2">
-      
-      
-      
+      </dl></div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="torch_device.html">torch_device()</a></code> 
+
+          <code><a href="torch_device.html">torch_device()</a></code>
+
         </dt>
         <dd>Create a Device object</dd>
       </dl><dl><dt>
-          
-          <code><a href="local_device.html">local_device()</a></code> <code><a href="local_device.html">with_device()</a></code> 
+
+          <code><a href="local_device.html">local_device()</a></code> <code><a href="local_device.html">with_device()</a></code>
+
         </dt>
         <dd>Device contexts</dd>
       </dl></div><div class="section level2">
       <h2 id="optimizers">Optimizers<a class="anchor" aria-label="anchor" href="#optimizers"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="optimizer.html">optimizer()</a></code> 
+
+          <code><a href="optimizer.html">optimizer()</a></code>
+
         </dt>
         <dd>Creates a custom optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_adadelta.html">optim_adadelta()</a></code> 
+
+          <code><a href="optim_adadelta.html">optim_adadelta()</a></code>
+
         </dt>
         <dd>Adadelta optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_adagrad.html">optim_adagrad()</a></code> 
+
+          <code><a href="optim_adagrad.html">optim_adagrad()</a></code>
+
         </dt>
         <dd>Adagrad optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_adam.html">optim_adam()</a></code> 
+
+          <code><a href="optim_adam.html">optim_adam()</a></code>
+
         </dt>
         <dd>Implements Adam algorithm.</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_adamw.html">optim_adamw()</a></code> 
+
+          <code><a href="optim_adamw.html">optim_adamw()</a></code>
+
         </dt>
         <dd>Implements AdamW algorithm</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_asgd.html">optim_asgd()</a></code> 
+
+          <code><a href="optim_asgd.html">optim_asgd()</a></code>
+
         </dt>
         <dd>Averaged Stochastic Gradient Descent optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_lbfgs.html">optim_lbfgs()</a></code> 
+
+          <code><a href="optim_ignite_adagrad.html">optim_ignite_adagrad()</a></code>
+
+        </dt>
+        <dd>LibTorch implementation of Adagrad</dd>
+      </dl><dl><dt>
+
+          <code><a href="optim_ignite_adam.html">optim_ignite_adam()</a></code>
+
+        </dt>
+        <dd>LibTorch implementation of Adam</dd>
+      </dl><dl><dt>
+
+          <code><a href="optim_ignite_adamw.html">optim_ignite_adamw()</a></code>
+
+        </dt>
+        <dd>LibTorch implementation of AdamW</dd>
+      </dl><dl><dt>
+
+          <code><a href="optim_ignite_rmsprop.html">optim_ignite_rmsprop()</a></code>
+
+        </dt>
+        <dd>LibTorch implementation of RMSprop</dd>
+      </dl><dl><dt>
+
+          <code><a href="optim_ignite_sgd.html">optim_ignite_sgd()</a></code>
+
+        </dt>
+        <dd>LibTorch implementation of SGD</dd>
+      </dl><dl><dt>
+
+          <code><a href="optim_lbfgs.html">optim_lbfgs()</a></code>
+
         </dt>
         <dd>LBFGS optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_required.html">optim_required()</a></code> 
+
+          <code><a href="optim_required.html">optim_required()</a></code>
+
         </dt>
         <dd>Dummy value indicating a required value.</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_rmsprop.html">optim_rmsprop()</a></code> 
+
+          <code><a href="optim_rmsprop.html">optim_rmsprop()</a></code>
+
         </dt>
         <dd>RMSprop optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_rprop.html">optim_rprop()</a></code> 
+
+          <code><a href="optim_rprop.html">optim_rprop()</a></code>
+
         </dt>
         <dd>Implements the resilient backpropagation algorithm.</dd>
       </dl><dl><dt>
-          
-          <code><a href="optim_sgd.html">optim_sgd()</a></code> 
+
+          <code><a href="optim_sgd.html">optim_sgd()</a></code>
+
         </dt>
         <dd>SGD optimizer</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_optimizer.html">is_optimizer()</a></code> 
+
+          <code><a href="is_optimizer.html">is_optimizer()</a></code>
+
         </dt>
         <dd>Checks if the object is a torch optimizer</dd>
+      </dl><dl><dt>
+
+          <code><a href="optimizer_ignite.html">optimizer_ignite()</a></code>
+
+        </dt>
+        <dd>Abstract Base Class for LibTorch Optimizers</dd>
+      </dl><dl><dt>
+
+          <code><a href="OptimizerIgnite.html">OptimizerIgnite</a></code>
+
+        </dt>
+        <dd>Abstract Base Class for LibTorch Optimizers</dd>
       </dl></div><div class="section level2">
       <h2 id="learning-rate-schedulers">Learning rate schedulers<a class="anchor" aria-label="anchor" href="#learning-rate-schedulers"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="lr_cosine_annealing.html">lr_cosine_annealing()</a></code> 
+
+          <code><a href="lr_cosine_annealing.html">lr_cosine_annealing()</a></code>
+
         </dt>
         <dd>Set the learning rate of each parameter group using a cosine annealing schedule</dd>
       </dl><dl><dt>
-          
-          <code><a href="lr_lambda.html">lr_lambda()</a></code> 
+
+          <code><a href="lr_lambda.html">lr_lambda()</a></code>
+
         </dt>
         <dd>Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.</dd>
       </dl><dl><dt>
-          
-          <code><a href="lr_multiplicative.html">lr_multiplicative()</a></code> 
+
+          <code><a href="lr_multiplicative.html">lr_multiplicative()</a></code>
+
         </dt>
         <dd>Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr.</dd>
       </dl><dl><dt>
-          
-          <code><a href="lr_one_cycle.html">lr_one_cycle()</a></code> 
+
+          <code><a href="lr_one_cycle.html">lr_one_cycle()</a></code>
+
         </dt>
         <dd>Once cycle learning rate</dd>
       </dl><dl><dt>
-          
-          <code><a href="lr_reduce_on_plateau.html">lr_reduce_on_plateau()</a></code> 
+
+          <code><a href="lr_reduce_on_plateau.html">lr_reduce_on_plateau()</a></code>
+
         </dt>
         <dd>Reduce learning rate on plateau</dd>
       </dl><dl><dt>
-          
-          <code><a href="lr_scheduler.html">lr_scheduler()</a></code> 
+
+          <code><a href="lr_scheduler.html">lr_scheduler()</a></code>
+
         </dt>
         <dd>Creates learning rate schedulers</dd>
       </dl><dl><dt>
-          
-          <code><a href="lr_step.html">lr_step()</a></code> 
+
+          <code><a href="lr_step.html">lr_step()</a></code>
+
         </dt>
         <dd>Step learning rate decay</dd>
       </dl></div><div class="section level2">
       <h2 id="datasets">Datasets<a class="anchor" aria-label="anchor" href="#datasets"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="dataset.html">dataset()</a></code> 
+
+          <code><a href="dataset.html">dataset()</a></code>
+
         </dt>
         <dd>Helper function to create an function that generates R6 instances of class <code>dataset</code></dd>
       </dl><dl><dt>
-          
-          <code><a href="dataset_subset.html">dataset_subset()</a></code> 
+
+          <code><a href="dataset_subset.html">dataset_subset()</a></code>
+
         </dt>
         <dd>Dataset Subset</dd>
       </dl><dl><dt>
-          
-          <code><a href="iterable_dataset.html">iterable_dataset()</a></code> 
+
+          <code><a href="iterable_dataset.html">iterable_dataset()</a></code>
+
         </dt>
         <dd>Creates an iterable dataset</dd>
       </dl><dl><dt>
-          
-          <code><a href="dataloader.html">dataloader()</a></code> 
+
+          <code><a href="dataloader.html">dataloader()</a></code>
+
         </dt>
         <dd>Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset.</dd>
       </dl><dl><dt>
-          
-          <code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code> 
+
+          <code><a href="dataloader_make_iter.html">dataloader_make_iter()</a></code>
+
         </dt>
         <dd>Creates an iterator from a DataLoader</dd>
       </dl><dl><dt>
-          
-          <code><a href="dataloader_next.html">dataloader_next()</a></code> 
+
+          <code><a href="dataloader_next.html">dataloader_next()</a></code>
+
         </dt>
         <dd>Get the next element of a dataloader iterator</dd>
       </dl><dl><dt>
-          
-          <code><a href="enumerate.html">enumerate()</a></code> 
+
+          <code><a href="enumerate.html">enumerate()</a></code>
+
         </dt>
         <dd>Enumerate an iterator</dd>
       </dl><dl><dt>
-          
-          <code><a href="enumerate.dataloader.html">enumerate(<i>&lt;dataloader&gt;</i>)</a></code> 
+
+          <code><a href="enumerate.dataloader.html">enumerate(<i>&lt;dataloader&gt;</i>)</a></code>
+
         </dt>
         <dd>Enumerate an iterator</dd>
       </dl><dl><dt>
-          
-          <code><a href="tensor_dataset.html">tensor_dataset()</a></code> 
+
+          <code><a href="tensor_dataset.html">tensor_dataset()</a></code>
+
         </dt>
         <dd>Dataset wrapping tensors.</dd>
       </dl><dl><dt>
-          
-          <code><a href="is_dataloader.html">is_dataloader()</a></code> 
+
+          <code><a href="is_dataloader.html">is_dataloader()</a></code>
+
         </dt>
         <dd>Checks if the object is a dataloader</dd>
       </dl><dl><dt>
-          
-          <code><a href="sampler.html">sampler()</a></code> 
+
+          <code><a href="sampler.html">sampler()</a></code>
+
         </dt>
         <dd>Creates a new Sampler</dd>
       </dl></div><div class="section level2">
       <h2 id="distributions">Distributions<a class="anchor" aria-label="anchor" href="#distributions"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="Distribution.html">Distribution</a></code> 
+
+          <code><a href="Distribution.html">Distribution</a></code>
+
         </dt>
         <dd>Generic R6 class representing distributions</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_bernoulli.html">distr_bernoulli()</a></code> 
+
+          <code><a href="distr_bernoulli.html">distr_bernoulli()</a></code>
+
         </dt>
         <dd>Creates a Bernoulli distribution parameterized by <code>probs</code> or <code>logits</code> (but not both). Samples are binary (0 or 1). They take the value <code>1</code> with probability <code>p</code> and <code>0</code> with probability <code>1 - p</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_categorical.html">distr_categorical()</a></code> 
+
+          <code><a href="distr_categorical.html">distr_categorical()</a></code>
+
         </dt>
         <dd>Creates a categorical distribution parameterized by either <code>probs</code> or <code>logits</code> (but not both).</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_chi2.html">distr_chi2()</a></code> 
+
+          <code><a href="distr_chi2.html">distr_chi2()</a></code>
+
         </dt>
         <dd>Creates a Chi2 distribution parameterized by shape parameter <code>df</code>. This is exactly equivalent to <code>distr_gamma(alpha=0.5*df, beta=0.5)</code></dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_gamma.html">distr_gamma()</a></code> 
+
+          <code><a href="distr_gamma.html">distr_gamma()</a></code>
+
         </dt>
         <dd>Creates a Gamma distribution parameterized by shape <code>concentration</code> and <code>rate</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_mixture_same_family.html">distr_mixture_same_family()</a></code> 
+
+          <code><a href="distr_mixture_same_family.html">distr_mixture_same_family()</a></code>
+
         </dt>
         <dd>Mixture of components in the same family</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_multivariate_normal.html">distr_multivariate_normal()</a></code> 
+
+          <code><a href="distr_multivariate_normal.html">distr_multivariate_normal()</a></code>
+
         </dt>
         <dd>Gaussian distribution</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_normal.html">distr_normal()</a></code> 
+
+          <code><a href="distr_normal.html">distr_normal()</a></code>
+
         </dt>
         <dd>Creates a normal (also called Gaussian) distribution parameterized by <code>loc</code> and <code>scale</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="distr_poisson.html">distr_poisson()</a></code> 
+
+          <code><a href="distr_poisson.html">distr_poisson()</a></code>
+
         </dt>
         <dd>Creates a Poisson distribution parameterized by <code>rate</code>, the rate parameter.</dd>
       </dl><dl><dt>
-          
-          <code><a href="Constraint.html">Constraint</a></code> 
+
+          <code><a href="Constraint.html">Constraint</a></code>
+
         </dt>
         <dd>Abstract base class for constraints.</dd>
       </dl></div><div class="section level2">
       <h2 id="autograd">Autograd<a class="anchor" aria-label="anchor" href="#autograd"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="autograd_backward.html">autograd_backward()</a></code> 
+
+          <code><a href="autograd_backward.html">autograd_backward()</a></code>
+
         </dt>
         <dd>Computes the sum of gradients of given tensors w.r.t. graph leaves.</dd>
       </dl><dl><dt>
-          
-          <code><a href="autograd_function.html">autograd_function()</a></code> 
+
+          <code><a href="autograd_function.html">autograd_function()</a></code>
+
         </dt>
         <dd>Records operation history and defines formulas for differentiating ops.</dd>
       </dl><dl><dt>
-          
-          <code><a href="autograd_grad.html">autograd_grad()</a></code> 
+
+          <code><a href="autograd_grad.html">autograd_grad()</a></code>
+
         </dt>
         <dd>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</dd>
       </dl><dl><dt>
-          
-          <code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code> 
+
+          <code><a href="autograd_set_grad_mode.html">autograd_set_grad_mode()</a></code>
+
         </dt>
         <dd>Set grad mode</dd>
       </dl><dl><dt>
-          
-          <code><a href="with_no_grad.html">with_no_grad()</a></code> <code><a href="with_no_grad.html">local_no_grad()</a></code> 
+
+          <code><a href="with_no_grad.html">with_no_grad()</a></code> <code><a href="with_no_grad.html">local_no_grad()</a></code>
+
         </dt>
         <dd>Temporarily modify gradient recording.</dd>
       </dl><dl><dt>
-          
-          <code><a href="with_enable_grad.html">with_enable_grad()</a></code> <code><a href="with_enable_grad.html">local_enable_grad()</a></code> 
+
+          <code><a href="with_enable_grad.html">with_enable_grad()</a></code> <code><a href="with_enable_grad.html">local_enable_grad()</a></code>
+
         </dt>
         <dd>Enable grad</dd>
       </dl><dl><dt>
-          
-          <code><a href="with_detect_anomaly.html">with_detect_anomaly()</a></code> 
+
+          <code><a href="with_detect_anomaly.html">with_detect_anomaly()</a></code>
+
         </dt>
         <dd>Context-manager that enable anomaly detection for the autograd engine.</dd>
       </dl><dl><dt>
-          
-          <code><a href="AutogradContext.html">AutogradContext</a></code> 
+
+          <code><a href="AutogradContext.html">AutogradContext</a></code>
+
         </dt>
         <dd>Class representing the context.</dd>
       </dl></div><div class="section level2">
       <h2 id="mixed-precision">Mixed precision<a class="anchor" aria-label="anchor" href="#mixed-precision"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="local_autocast.html">local_autocast()</a></code> <code><a href="local_autocast.html">with_autocast()</a></code> <code><a href="local_autocast.html">set_autocast()</a></code> <code><a href="local_autocast.html">unset_autocast()</a></code> 
+
+          <code><a href="local_autocast.html">local_autocast()</a></code> <code><a href="local_autocast.html">with_autocast()</a></code> <code><a href="local_autocast.html">set_autocast()</a></code> <code><a href="local_autocast.html">unset_autocast()</a></code>
+
         </dt>
         <dd>Autocast context manager</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_amp_grad_scaler.html">cuda_amp_grad_scaler()</a></code> 
+
+          <code><a href="cuda_amp_grad_scaler.html">cuda_amp_grad_scaler()</a></code>
+
         </dt>
         <dd>Creates a gradient scaler</dd>
       </dl></div><div class="section level2">
       <h2 id="random-numbers">Random numbers<a class="anchor" aria-label="anchor" href="#random-numbers"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">local_torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">with_torch_manual_seed()</a></code> 
+
+          <code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">local_torch_manual_seed()</a></code> <code><a href="torch_manual_seed.html">with_torch_manual_seed()</a></code>
+
         </dt>
         <dd>Sets the seed for generating random numbers.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_get_rng_state.html">torch_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">torch_set_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_set_rng_state()</a></code> 
+
+          <code><a href="torch_get_rng_state.html">torch_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">torch_set_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_set_rng_state()</a></code>
+
         </dt>
         <dd>RNG state management</dd>
       </dl></div><div class="section level2">
       <h2 id="linear-algebra">Linear Algebra<a class="anchor" aria-label="anchor" href="#linear-algebra"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> 
+
+          <code><a href="linalg_cholesky.html">linalg_cholesky()</a></code>
+
         </dt>
         <dd>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex()</a></code> 
+
+          <code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex()</a></code>
+
         </dt>
         <dd>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_cond.html">linalg_cond()</a></code> 
+
+          <code><a href="linalg_cond.html">linalg_cond()</a></code>
+
         </dt>
         <dd>Computes the condition number of a matrix with respect to a matrix norm.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_det.html">linalg_det()</a></code> 
+
+          <code><a href="linalg_det.html">linalg_det()</a></code>
+
         </dt>
         <dd>Computes the determinant of a square matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_eig.html">linalg_eig()</a></code> 
+
+          <code><a href="linalg_eig.html">linalg_eig()</a></code>
+
         </dt>
         <dd>Computes the eigenvalue decomposition of a square matrix if it exists.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_eigh.html">linalg_eigh()</a></code> 
+
+          <code><a href="linalg_eigh.html">linalg_eigh()</a></code>
+
         </dt>
         <dd>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_eigvals.html">linalg_eigvals()</a></code> 
+
+          <code><a href="linalg_eigvals.html">linalg_eigvals()</a></code>
+
         </dt>
         <dd>Computes the eigenvalues of a square matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code> 
+
+          <code><a href="linalg_eigvalsh.html">linalg_eigvalsh()</a></code>
+
         </dt>
         <dd>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_householder_product.html">linalg_householder_product()</a></code> 
+
+          <code><a href="linalg_householder_product.html">linalg_householder_product()</a></code>
+
         </dt>
         <dd>Computes the first <code>n</code> columns of a product of Householder matrices.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_inv.html">linalg_inv()</a></code> 
+
+          <code><a href="linalg_inv.html">linalg_inv()</a></code>
+
         </dt>
         <dd>Computes the inverse of a square matrix if it exists.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_inv_ex.html">linalg_inv_ex()</a></code> 
+
+          <code><a href="linalg_inv_ex.html">linalg_inv_ex()</a></code>
+
         </dt>
         <dd>Computes the inverse of a square matrix if it is invertible.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> 
+
+          <code><a href="linalg_lstsq.html">linalg_lstsq()</a></code>
+
         </dt>
         <dd>Computes a solution to the least squares problem of a system of linear equations.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_matrix_norm.html">linalg_matrix_norm()</a></code> 
+
+          <code><a href="linalg_matrix_norm.html">linalg_matrix_norm()</a></code>
+
         </dt>
         <dd>Computes a matrix norm.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_matrix_power.html">linalg_matrix_power()</a></code> 
+
+          <code><a href="linalg_matrix_power.html">linalg_matrix_power()</a></code>
+
         </dt>
         <dd>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_matrix_rank.html">linalg_matrix_rank()</a></code> 
+
+          <code><a href="linalg_matrix_rank.html">linalg_matrix_rank()</a></code>
+
         </dt>
         <dd>Computes the numerical rank of a matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_multi_dot.html">linalg_multi_dot()</a></code> 
+
+          <code><a href="linalg_multi_dot.html">linalg_multi_dot()</a></code>
+
         </dt>
         <dd>Efficiently multiplies two or more matrices</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_norm.html">linalg_norm()</a></code> 
+
+          <code><a href="linalg_norm.html">linalg_norm()</a></code>
+
         </dt>
         <dd>Computes a vector or matrix norm.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_pinv.html">linalg_pinv()</a></code> 
+
+          <code><a href="linalg_pinv.html">linalg_pinv()</a></code>
+
         </dt>
         <dd>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_qr.html">linalg_qr()</a></code> 
+
+          <code><a href="linalg_qr.html">linalg_qr()</a></code>
+
         </dt>
         <dd>Computes the QR decomposition of a matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_slogdet.html">linalg_slogdet()</a></code> 
+
+          <code><a href="linalg_slogdet.html">linalg_slogdet()</a></code>
+
         </dt>
         <dd>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_solve.html">linalg_solve()</a></code> 
+
+          <code><a href="linalg_solve.html">linalg_solve()</a></code>
+
         </dt>
         <dd>Computes the solution of a square system of linear equations with a unique solution.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_solve_triangular.html">linalg_solve_triangular()</a></code> 
+
+          <code><a href="linalg_solve_triangular.html">linalg_solve_triangular()</a></code>
+
         </dt>
         <dd>Triangular solve</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_svd.html">linalg_svd()</a></code> 
+
+          <code><a href="linalg_svd.html">linalg_svd()</a></code>
+
         </dt>
         <dd>Computes the singular value decomposition (SVD) of a matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_svdvals.html">linalg_svdvals()</a></code> 
+
+          <code><a href="linalg_svdvals.html">linalg_svdvals()</a></code>
+
         </dt>
         <dd>Computes the singular values of a matrix.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code> 
+
+          <code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code>
+
         </dt>
         <dd>Computes the multiplicative inverse of <code><a href="../reference/torch_tensordot.html">torch_tensordot()</a></code></dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> 
+
+          <code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code>
+
         </dt>
         <dd>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="linalg_vector_norm.html">linalg_vector_norm()</a></code> 
+
+          <code><a href="linalg_vector_norm.html">linalg_vector_norm()</a></code>
+
         </dt>
         <dd>Computes a vector norm.</dd>
       </dl></div><div class="section level2">
       <h2 id="cuda-utilities">Cuda utilities<a class="anchor" aria-label="anchor" href="#cuda-utilities"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="cuda_amp_grad_scaler.html">cuda_amp_grad_scaler()</a></code> 
+
+          <code><a href="cuda_amp_grad_scaler.html">cuda_amp_grad_scaler()</a></code>
+
         </dt>
         <dd>Creates a gradient scaler</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_current_device.html">cuda_current_device()</a></code> 
+
+          <code><a href="cuda_current_device.html">cuda_current_device()</a></code>
+
         </dt>
         <dd>Returns the index of a currently selected device.</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_device_count.html">cuda_device_count()</a></code> 
+
+          <code><a href="cuda_device_count.html">cuda_device_count()</a></code>
+
         </dt>
         <dd>Returns the number of GPUs available.</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_empty_cache.html">cuda_empty_cache()</a></code> 
+
+          <code><a href="cuda_empty_cache.html">cuda_empty_cache()</a></code>
+
         </dt>
         <dd>Empty cache</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_get_device_capability.html">cuda_get_device_capability()</a></code> 
+
+          <code><a href="cuda_get_device_capability.html">cuda_get_device_capability()</a></code>
+
         </dt>
         <dd>Returns the major and minor CUDA capability of <code>device</code></dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_is_available.html">cuda_is_available()</a></code> 
+
+          <code><a href="cuda_is_available.html">cuda_is_available()</a></code>
+
         </dt>
         <dd>Returns a bool indicating if CUDA is currently available.</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_memory_stats.html">cuda_memory_stats()</a></code> <code><a href="cuda_memory_stats.html">cuda_memory_summary()</a></code> 
+
+          <code><a href="cuda_memory_stats.html">cuda_memory_stats()</a></code> <code><a href="cuda_memory_stats.html">cuda_memory_summary()</a></code>
+
         </dt>
         <dd>Returns a dictionary of CUDA memory allocator statistics for a given device.</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_runtime_version.html">cuda_runtime_version()</a></code> 
+
+          <code><a href="cuda_runtime_version.html">cuda_runtime_version()</a></code>
+
         </dt>
         <dd>Returns the CUDA runtime version</dd>
       </dl><dl><dt>
-          
-          <code><a href="cuda_synchronize.html">cuda_synchronize()</a></code> 
+
+          <code><a href="cuda_synchronize.html">cuda_synchronize()</a></code>
+
         </dt>
         <dd>Waits for all kernels in all streams on a CUDA device to complete.</dd>
       </dl><dl><dt>
-          
-          <code><a href="torch_get_rng_state.html">torch_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">torch_set_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_set_rng_state()</a></code> 
+
+          <code><a href="torch_get_rng_state.html">torch_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">torch_set_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_get_rng_state()</a></code> <code><a href="torch_get_rng_state.html">cuda_set_rng_state()</a></code>
+
         </dt>
         <dd>RNG state management</dd>
       </dl></div><div class="section level2">
       <h2 id="jit">JIT<a class="anchor" aria-label="anchor" href="#jit"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="jit_compile.html">jit_compile()</a></code> 
+
+          <code><a href="jit_compile.html">jit_compile()</a></code>
+
         </dt>
         <dd>Compile TorchScript code into a graph</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_load.html">jit_load()</a></code> 
+
+          <code><a href="jit_load.html">jit_load()</a></code>
+
         </dt>
         <dd>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_ops.html">jit_ops</a></code> 
+
+          <code><a href="jit_ops.html">jit_ops</a></code>
+
         </dt>
         <dd>Enable idiomatic access to JIT operators from R.</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_save.html">jit_save()</a></code> 
+
+          <code><a href="jit_save.html">jit_save()</a></code>
+
         </dt>
         <dd>Saves a <code>script_function</code> to a path</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_save_for_mobile.html">jit_save_for_mobile()</a></code> 
+
+          <code><a href="jit_save_for_mobile.html">jit_save_for_mobile()</a></code>
+
         </dt>
         <dd>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form, to be loaded on a mobile device</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_scalar.html">jit_scalar()</a></code> 
+
+          <code><a href="jit_scalar.html">jit_scalar()</a></code>
+
         </dt>
         <dd>Adds the 'jit_scalar' class to the input</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_trace.html">jit_trace()</a></code> 
+
+          <code><a href="jit_serialize.html">jit_serialize()</a></code>
+
+        </dt>
+        <dd>Serialize a Script Module</dd>
+      </dl><dl><dt>
+
+          <code><a href="jit_trace.html">jit_trace()</a></code>
+
         </dt>
         <dd>Trace a function and return an executable <code>script_function</code>.</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_trace_module.html">jit_trace_module()</a></code> 
+
+          <code><a href="jit_trace_module.html">jit_trace_module()</a></code>
+
         </dt>
         <dd>Trace a module</dd>
       </dl><dl><dt>
-          
-          <code><a href="jit_tuple.html">jit_tuple()</a></code> 
+
+          <code><a href="jit_tuple.html">jit_tuple()</a></code>
+
         </dt>
         <dd>Adds the 'jit_tuple' class to the input</dd>
+      </dl><dl><dt>
+
+          <code><a href="jit_unserialize.html">jit_unserialize()</a></code>
+
+        </dt>
+        <dd>Unserialize a Script Module</dd>
       </dl></div><div class="section level2">
       <h2 id="backends">Backends<a class="anchor" aria-label="anchor" href="#backends"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="backends_cudnn_is_available.html">backends_cudnn_is_available()</a></code> 
+
+          <code><a href="backends_cudnn_is_available.html">backends_cudnn_is_available()</a></code>
+
         </dt>
         <dd>CuDNN is available</dd>
       </dl><dl><dt>
-          
-          <code><a href="backends_cudnn_version.html">backends_cudnn_version()</a></code> 
+
+          <code><a href="backends_cudnn_version.html">backends_cudnn_version()</a></code>
+
         </dt>
         <dd>CuDNN version</dd>
       </dl><dl><dt>
-          
-          <code><a href="backends_mkl_is_available.html">backends_mkl_is_available()</a></code> 
+
+          <code><a href="backends_mkl_is_available.html">backends_mkl_is_available()</a></code>
+
         </dt>
         <dd>MKL is available</dd>
       </dl><dl><dt>
-          
-          <code><a href="backends_mkldnn_is_available.html">backends_mkldnn_is_available()</a></code> 
+
+          <code><a href="backends_mkldnn_is_available.html">backends_mkldnn_is_available()</a></code>
+
         </dt>
         <dd>MKLDNN is available</dd>
       </dl><dl><dt>
-          
-          <code><a href="backends_mps_is_available.html">backends_mps_is_available()</a></code> 
+
+          <code><a href="backends_mps_is_available.html">backends_mps_is_available()</a></code>
+
         </dt>
         <dd>MPS is available</dd>
       </dl><dl><dt>
-          
-          <code><a href="backends_openmp_is_available.html">backends_openmp_is_available()</a></code> 
+
+          <code><a href="backends_openmp_is_available.html">backends_openmp_is_available()</a></code>
+
         </dt>
         <dd>OpenMP is available</dd>
       </dl></div><div class="section level2">
       <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="install_torch.html">install_torch()</a></code> 
+
+          <code><a href="install_torch.html">install_torch()</a></code>
+
         </dt>
         <dd>Install Torch</dd>
       </dl><dl><dt>
-          
-          <code><a href="install_torch_from_file.html">get_install_libs_url()</a></code> <code><a href="install_torch_from_file.html">install_torch_from_file()</a></code> 
+
+          <code><a href="install_torch_from_file.html">get_install_libs_url()</a></code> <code><a href="install_torch_from_file.html">install_torch_from_file()</a></code>
+
         </dt>
         <dd>Install Torch from files</dd>
       </dl></div><div class="section level2">
       <h2 id="contrib">Contrib<a class="anchor" aria-label="anchor" href="#contrib"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="contrib_sort_vertices.html">contrib_sort_vertices()</a></code> 
+
+          <code><a href="contrib_sort_vertices.html">contrib_sort_vertices()</a></code>
+
         </dt>
         <dd>Contrib sort vertices</dd>
       </dl></div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -3721,14 +4446,14 @@ <h2 id="contrib">Contrib<a class="anchor" aria-label="anchor" href="#contrib"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/install_torch.html b/reference/install_torch.html
index be126cc526..2fba9afee4 100644
--- a/reference/install_torch.html
+++ b/reference/install_torch.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Installs Torch and its dependencies."><title>Install Torch — install_torch • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Install Torch — install_torch"><meta property="og:description" content="Installs Torch and its dependencies."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Install Torch — install_torch • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Install Torch — install_torch"><meta name="description" content="Installs Torch and its dependencies."><meta property="og:description" content="Installs Torch and its dependencies."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Install Torch</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+
+      <h1>Install Torch</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/install.R" class="external-link"><code>R/install.R</code></a></small>
       <div class="d-none name"><code>install_torch.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>reinstall</dt>
+
+
+<dl><dt id="arg-reinstall">reinstall<a class="anchor" aria-label="anchor" href="#arg-reinstall"></a></dt>
 <dd><p>Re-install Torch even if its already installed?</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently unused.</p></dd>
 
 
-<dt>.inform_restart</dt>
+<dt id="arg--inform-restart">.inform_restart<a class="anchor" aria-label="anchor" href="#arg--inform-restart"></a></dt>
 <dd><p>if <code>TRUE</code> and running in an <code><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive()</a></code> session, after
 installation it will print a message to inform the user that the session must
 be restarted for torch to work correctly.</p></dd>
@@ -140,7 +124,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 reported length, an increase of the <code>timeout</code> value should help.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
+
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diff --git a/reference/install_torch_from_file.html b/reference/install_torch_from_file.html
index 9417110fd1..b712add804 100644
--- a/reference/install_torch_from_file.html
+++ b/reference/install_torch_from_file.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="List the Torch and Lantern libraries URLs to download as local files in order to proceed with  install_torch_from_file().
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-Installs Torch and its dependencies from files."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Install Torch from files — get_install_libs_url • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Install Torch from files — get_install_libs_url"><meta name="description" content="List the Torch and Lantern libraries URLs to download as local files in order to proceed with  install_torch_from_file().
+Installs Torch and its dependencies from files."><meta property="og:description" content="List the Torch and Lantern libraries URLs to download as local files in order to proceed with  install_torch_from_file().
+Installs Torch and its dependencies from files."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -12,77 +9,62 @@
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Install Torch from files</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+
+      <h1>Install Torch from files</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/install.R" class="external-link"><code>R/install.R</code></a></small>
       <div class="d-none name"><code>install_torch_from_file.Rd</code></div>
     </div>
 
@@ -100,23 +82,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>version</dt>
+
+
+<dl><dt id="arg-version">version<a class="anchor" aria-label="anchor" href="#arg-version"></a></dt>
 <dd><p>Not used</p></dd>
 
 
-<dt>type</dt>
+<dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>Not used. This function is deprecated.</p></dd>
 
 
-<dt>libtorch</dt>
+<dt id="arg-libtorch">libtorch<a class="anchor" aria-label="anchor" href="#arg-libtorch"></a></dt>
 <dd><p>The installation archive file to use for Torch. Shall be a <code>"file://"</code> URL scheme.</p></dd>
 
 
-<dt>liblantern</dt>
+<dt id="arg-liblantern">liblantern<a class="anchor" aria-label="anchor" href="#arg-liblantern"></a></dt>
 <dd><p>The installation archive file to use for Lantern. Shall be a <code>"file://"</code> URL scheme.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>other parameters to be passed to <code>"install_torch()"</code></p></dd>
 
 </dl></div>
@@ -131,18 +115,18 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># on a linux CPU platform </span></span></span>
 <span class="r-in"><span><span class="fu">get_install_libs_url</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># then after making both files available into /tmp/</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.setenv.html" class="external-link">Sys.setenv</a></span><span class="op">(</span>TORCH_URL<span class="op">=</span><span class="st">"/tmp/libtorch-v1.13.1.zip"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.setenv.html" class="external-link">Sys.setenv</a></span><span class="op">(</span>LANTERN_URL<span class="op">=</span><span class="st">"/tmp/lantern-0.9.1.9001+cpu+arm64-Darwin.zip"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch</span><span class="fu">::</span><span class="fu"><a href="install_torch.html">install_torch</a></span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -151,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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+
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   </body></html>
 
diff --git a/reference/is_dataloader.html b/reference/is_dataloader.html
index 600f509501..a7e7e658ea 100644
--- a/reference/is_dataloader.html
+++ b/reference/is_dataloader.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Checks if the object is a dataloader</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
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       <div class="d-none name"><code>is_dataloader.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>x</dt>
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index 24a741f82c..ac4d92f673 100644
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+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>x</dt>
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+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>object to check</p></dd>
 
 </dl></div>
 
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-      <img src="" class="logo" alt=""><h1>Checks if the object is an nn_module</h1>
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diff --git a/reference/is_nn_parameter.html b/reference/is_nn_parameter.html
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-      <img src="" class="logo" alt=""><h1>Checks if an object is a nn_parameter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
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+      <h1>Checks if an object is a nn_parameter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>is_nn_parameter.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the object to check</p></dd>
 
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diff --git a/reference/is_optimizer.html b/reference/is_optimizer.html
index cf9509ff33..8b27181d40 100644
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-      <img src="" class="logo" alt=""><h1>Checks if the object is a torch optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
+
+      <h1>Checks if the object is a torch optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
       <div class="d-none name"><code>is_optimizer.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>object to check</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/is_torch_device.html b/reference/is_torch_device.html
index 26e1964c12..c134cd32f4 100644
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-      <img src="" class="logo" alt=""><h1>Checks if object is a device</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/device.R" class="external-link"><code>R/device.R</code></a></small>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/device.R" class="external-link"><code>R/device.R</code></a></small>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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index f6ef0b2ff5..d96d57b76e 100644
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-      <img src="" class="logo" alt=""><h1>Check if object is a torch data type</h1>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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index 7b00e2d770..7f6b8b9eb6 100644
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-      <img src="" class="logo" alt=""><h1>Check if an object is a torch layout.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/layout.R" class="external-link"><code>R/layout.R</code></a></small>
+
+      <h1>Check if an object is a torch layout.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/layout.R" class="external-link"><code>R/layout.R</code></a></small>
       <div class="d-none name"><code>is_torch_layout.Rd</code></div>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
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+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>object to check</p></dd>
 
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-    <dl><dt>x</dt>
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-      <img src="" class="logo" alt=""><h1>Checks if an object is a QScheme</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/qscheme.R" class="external-link"><code>R/qscheme.R</code></a></small>
+
+      <h1>Checks if an object is a QScheme</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/qscheme.R" class="external-link"><code>R/qscheme.R</code></a></small>
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@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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diff --git a/reference/is_undefined_tensor.html b/reference/is_undefined_tensor.html
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       <div class="d-none name"><code>iterable_dataset.Rd</code></div>
     </div>
 
@@ -102,29 +84,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>name</dt>
+
+
+<dl><dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>a name for the dataset. It it's also used as the class
 for it.</p></dd>
 
 
-<dt>inherit</dt>
+<dt id="arg-inherit">inherit<a class="anchor" aria-label="anchor" href="#arg-inherit"></a></dt>
 <dd><p>you can optionally inherit from a dataset when creating a
 new dataset.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>public methods for the dataset class</p></dd>
 
 
-<dt>private</dt>
+<dt id="arg-private">private<a class="anchor" aria-label="anchor" href="#arg-private"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 
-<dt>active</dt>
+<dt id="arg-active">active<a class="anchor" aria-label="anchor" href="#arg-active"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 
-<dt>parent_env</dt>
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
 <dd><p>An environment to use as the parent of newly-created
 objects.</p></dd>
 
@@ -144,14 +128,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>    <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>      <span class="va">i</span> <span class="op">&lt;&lt;-</span> <span class="va">i</span> <span class="op">+</span> <span class="fl">1</span></span></span>
 <span class="r-in"><span>      <span class="kw">if</span> <span class="op">(</span><span class="va">i</span> <span class="op">&gt;</span> <span class="va">self</span><span class="op">$</span><span class="va">n</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>        <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/iterator.html" class="external-link">exhausted</a></span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span>        <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://coro.r-lib.org/reference/iterator.html" class="external-link">exhausted</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span>      <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>        <span class="va">i</span></span></span>
 <span class="r-in"><span>      <span class="op">}</span></span></span>
 <span class="r-in"><span>    <span class="op">}</span></span></span>
 <span class="r-in"><span>  <span class="op">}</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">collect</a></span><span class="op">(</span><span class="fu">ids</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">.iter</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">collect</a></span><span class="op">(</span><span class="fu">ids</span><span class="op">(</span><span class="op">)</span><span class="op">$</span><span class="fu">.iter</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1</span>
@@ -170,7 +154,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -179,14 +163,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/jit_compile.html b/reference/jit_compile.html
index 137985ebfb..c32dd4eafc 100644
--- a/reference/jit_compile.html
+++ b/reference/jit_compile.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See the TorchScript language reference for
-documentation on how to write TorchScript code."><title>Compile TorchScript code into a graph — jit_compile • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Compile TorchScript code into a graph — jit_compile"><meta property="og:description" content="See the TorchScript language reference for
-documentation on how to write TorchScript code."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Compile TorchScript code into a graph — jit_compile • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Compile TorchScript code into a graph — jit_compile"><meta name="description" content="See the TorchScript language reference for
+documentation on how to write TorchScript code."><meta property="og:description" content="See the TorchScript language reference for
+documentation on how to write TorchScript code."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
   window.dataLayer = window.dataLayer || [];
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
@@ -12,77 +9,62 @@
   gtag('config', 'G-9ZJSKW3L0N');
 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
       <ul class="navbar-nav me-auto"><li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/installation.html">Installation</a></li>
+    <li><h6 class="dropdown-header" data-toc-skip>Tensors</h6></li>
+    <li><a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a></li>
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+    <li><a class="dropdown-item" href="../articles/torchscript.html">Jit Compilation</a></li>
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 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-examples">Examples</a>
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-    <a class="dropdown-item" href="../articles/examples/dataset.html">dataset</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-examples"><li><a class="dropdown-item" href="../articles/examples/basic-autograd.html">basic-autograd</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/basic-nn-module.html">basic-nn-module</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/dataset.html">dataset</a></li>
+  </ul></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-advanced">Advanced</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-advanced">
-    <a class="dropdown-item" href="../articles/memory-management.html">Memory management</a>
-    <a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a>
-    <a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a>
-  </div>
-</li>
-<li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
-<li class="nav-item">
-  <a class="nav-link" href="../news/index.html">Changelog</a>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-  <a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compile TorchScript code into a graph</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/jit-compile.R" class="external-link"><code>R/jit-compile.R</code></a></small>
+
+      <h1>Compile TorchScript code into a graph</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/jit-compile.R" class="external-link"><code>R/jit-compile.R</code></a></small>
       <div class="d-none name"><code>jit_compile.Rd</code></div>
     </div>
 
@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>source</dt>
+
+
+<dl><dt id="arg-source">source<a class="anchor" aria-label="anchor" href="#arg-source"></a></dt>
 <dd><p>valid TorchScript source code.</p></dd>
 
 </dl></div>
@@ -119,11 +103,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">comp</span><span class="op">$</span><span class="fu">foo</span><span class="op">(</span><span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.62729</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.60211</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/jit_load.html b/reference/jit_load.html
index 4ffc58fd4f..95a9ada0ee 100644
--- a/reference/jit_load.html
+++ b/reference/jit_load.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+
+      <h1>Loads a <code>script_function</code> or <code>script_module</code> previously saved with <code>jit_save</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
       <div class="d-none name"><code>jit_load.Rd</code></div>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>path</dt>
+
+
+<dl><dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>a path to a <code>script_function</code> or <code>script_module</code> serialized with
 <code><a href="jit_save.html">jit_save()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -114,14 +98,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/jit_ops.html b/reference/jit_ops.html
index 080d169ba6..dd3b5c9637 100644
--- a/reference/jit_ops.html
+++ b/reference/jit_ops.html
@@ -1,20 +1,17 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Call JIT operators directly from R, keeping the familiar argument types and argument order.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Enable idiomatic access to JIT operators from R. — jit_ops • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Enable idiomatic access to JIT operators from R. — jit_ops"><meta name="description" content="Call JIT operators directly from R, keeping the familiar argument types and argument order.
 Note, however, that:
 all arguments are required (no defaults)
 axis numbering (as well as position numbers overall) starts from 0
 scalars have to be wrapped in jit_scalar()
 
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+"><meta property="og:description" content="Call JIT operators directly from R, keeping the familiar argument types and argument order.
 Note, however, that:
 all arguments are required (no defaults)
 axis numbering (as well as position numbers overall) starts from 0
 scalars have to be wrapped in jit_scalar()
 
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-      <img src="" class="logo" alt=""><h1>Enable idiomatic access to JIT operators from R.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/jit-ops.R" class="external-link"><code>R/jit-ops.R</code></a></small>
+
+      <h1>Enable idiomatic access to JIT operators from R.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/jit-ops.R" class="external-link"><code>R/jit-ops.R</code></a></small>
       <div class="d-none name"><code>jit_ops.Rd</code></div>
     </div>
 
@@ -139,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
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@@ -148,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/jit_save.html b/reference/jit_save.html
index 232d1536f0..b546f0cc6d 100644
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-      <img src="" class="logo" alt=""><h1>Saves a <code>script_function</code> to a path</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+
+      <h1>Saves a <code>script_function</code> to a path</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
       <div class="d-none name"><code>jit_save.Rd</code></div>
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@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>obj</dt>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
 <dd><p>An <code>script_function</code> to save</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>The path to save the serialized function.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused</p></dd>
 
 </dl></div>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/jit_save_for_mobile.html b/reference/jit_save_for_mobile.html
index fb7e562133..4ad7c0f2d4 100644
--- a/reference/jit_save_for_mobile.html
+++ b/reference/jit_save_for_mobile.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Saves a script_function or script_module in bytecode form,
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile"><meta name="description" content="Saves a script_function or script_module in bytecode form,
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@@ -12,77 +9,62 @@
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form, to be loaded on a mobile device</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+
+      <h1>Saves a <code>script_function</code> or <code>script_module</code> in bytecode form, to be loaded on a mobile device</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
       <div class="d-none name"><code>jit_save_for_mobile.Rd</code></div>
     </div>
 
@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>obj</dt>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
 <dd><p>An <code>script_function</code> or <code>script_module</code> to save</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>The path to save the serialized function.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused</p></dd>
 
 </dl></div>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/jit_scalar.html b/reference/jit_scalar.html
index 9a9e91d019..775e361a70 100644
--- a/reference/jit_scalar.html
+++ b/reference/jit_scalar.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Allows disambiguating length 1 vectors from scalars when passing
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+them to the jit."><meta property="og:description" content="Allows disambiguating length 1 vectors from scalars when passing
+them to the jit."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Adds the 'jit_scalar' class to the input</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/ivalue.R" class="external-link"><code>R/ivalue.R</code></a></small>
+
+      <h1>Adds the 'jit_scalar' class to the input</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ivalue.R" class="external-link"><code>R/ivalue.R</code></a></small>
       <div class="d-none name"><code>jit_scalar.Rd</code></div>
     </div>
 
@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>a length 1 R vector.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -112,14 +96,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/jit_serialize.html b/reference/jit_serialize.html
new file mode 100644
index 0000000000..141dddf58b
--- /dev/null
+++ b/reference/jit_serialize.html
@@ -0,0 +1,122 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Serialize a Script Module — jit_serialize • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Serialize a Script Module — jit_serialize"><meta name="description" content="Serializes a script module and returns it as a raw vector.
+You can read the object again using jit_unserialize."><meta property="og:description" content="Serializes a script module and returns it as a raw vector.
+You can read the object again using jit_unserialize."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Serialize a Script Module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_serialize.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Serializes a script module and returns it as a raw vector.
+You can read the object again using <code><a href="jit_unserialize.html">jit_unserialize</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">jit_serialize</span><span class="op">(</span><span class="va">obj</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
+<dd><p>(<code>script_module</code>)<br>
+Model to be serialized.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p><code><a href="https://rdrr.io/r/base/raw.html" class="external-link">raw()</a></code></p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="jit_trace.html">jit_trace</a></span><span class="op">(</span><span class="fu"><a href="nn_linear.html">nn_linear</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">serialized</span> <span class="op">&lt;-</span> <span class="fu">jit_serialize</span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
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+
diff --git a/reference/jit_trace.html b/reference/jit_trace.html
index 4a2f654db6..057ec562bd 100644
--- a/reference/jit_trace.html
+++ b/reference/jit_trace.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Using jit_trace, you can turn an existing R function into a TorchScript
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Trace a function and return an executable script_function. — jit_trace • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Trace a function and return an executable script_function. — jit_trace"><meta name="description" content="Using jit_trace, you can turn an existing R function into a TorchScript
 script_function. You must provide example inputs, and we run the function,
-recording the operations performed on all the tensors."><title>Trace a function and return an executable script_function. — jit_trace • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Trace a function and return an executable script_function. — jit_trace"><meta property="og:description" content="Using jit_trace, you can turn an existing R function into a TorchScript
+recording the operations performed on all the tensors."><meta property="og:description" content="Using jit_trace, you can turn an existing R function into a TorchScript
 script_function. You must provide example inputs, and we run the function,
-recording the operations performed on all the tensors."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+recording the operations performed on all the tensors."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Trace a function and return an executable <code>script_function</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+
+      <h1>Trace a function and return an executable <code>script_function</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
       <div class="d-none name"><code>jit_trace.Rd</code></div>
     </div>
 
@@ -96,19 +78,21 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">jit_trace</span><span class="op">(</span><span class="va">func</span>, <span class="va">...</span>, strict <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">jit_trace</span><span class="op">(</span><span class="va">func</span>, <span class="va">...</span>, strict <span class="op">=</span> <span class="cn">TRUE</span>, respect_mode <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>func</dt>
+
+
+<dl><dt id="arg-func">func<a class="anchor" aria-label="anchor" href="#arg-func"></a></dt>
 <dd><p>An R function that will be run with <code>example_inputs</code>. func arguments
 and return values must be tensors or (possibly nested) lists that contain tensors.
 Can also be a <code><a href="nn_module.html">nn_module()</a></code>, in such case <code><a href="jit_trace_module.html">jit_trace_module()</a></code> is used to trace
 that module.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>example inputs that will be passed to the function while
 tracing. The resulting trace can be run with inputs of different types and
 shapes assuming the traced operations support those types and shapes.
@@ -117,33 +101,37 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 respected.</p></dd>
 
 
-<dt>strict</dt>
+<dt id="arg-strict">strict<a class="anchor" aria-label="anchor" href="#arg-strict"></a></dt>
 <dd><p>run the tracer in a strict mode or not (default: <code>TRUE</code>). Only
 turn this off when you want the tracer to record your mutable container types
 (currently list/dict) and you are sure that the container you are using in
 your problem is a constant structure and does not get used as control flow
 (<code>if</code>, <code>for</code>) conditions.</p></dd>
 
+
+<dt id="arg-respect-mode">respect_mode<a class="anchor" aria-label="anchor" href="#arg-respect-mode"></a></dt>
+<dd><p>(<code>logical(1)</code>)<br>
+Whether both modes ('train' or 'eval') should be traced. If <code>TRUE</code> (default),
+the underlying C++ ScriptModule will have two methods <code>trainforward()</code> and
+<code>evalforward()</code>.
+The <code>$forward()</code> method of the R torch module will then select either based
+on the mode.
+If <code>FALSE</code>, only the current mode of the module will be jitted and hence only
+one <code>forward()</code> method exists.</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>An <code>script_function</code> if <code>func</code> is a function and <code>script_module</code> if
+    <p>An <code>script_function</code> if <code>func</code> is a function and <code>script_module</code> if
 <code>func</code> is a <code><a href="nn_module.html">nn_module()</a></code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>The resulting recording of a standalone function produces a <code>script_function</code>.
-In the future we will also support tracing <code>nn_modules</code>.</p>
-    </div>
-    <div class="section level2">
-    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    <p>Scripting is not yet supported in R.</p>
+    <p>The resulting recording of a standalone function produces a <code>script_function</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 
 <p>Tracing only correctly records functions and modules which are not data dependent
@@ -164,7 +152,8 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 </ul><p>In cases like these, tracing would not be appropriate and scripting is a better
 choice. If you trace such models, you may silently get incorrect results on
 subsequent invocations of the model. The tracer will try to emit warnings when
-doing something that may cause an incorrect trace to be produced.</p>
+doing something that may cause an incorrect trace to be produced.
+For scripting, see <code><a href="jit_compile.html">jit_compile</a></code>.</p>
     </div>
 
     <div class="section level2">
@@ -184,7 +173,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -193,14 +182,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
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diff --git a/reference/jit_trace_module.html b/reference/jit_trace_module.html
index 2f648be750..f1e0de5fd2 100644
--- a/reference/jit_trace_module.html
+++ b/reference/jit_trace_module.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Trace a module and return an executable ScriptModule that will be optimized
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Trace a module — jit_trace_module • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Trace a module — jit_trace_module"><meta name="description" content="Trace a module and return an executable ScriptModule that will be optimized
 using just-in-time compilation. When a module is passed to jit_trace(), only
 the forward method is run and traced. With jit_trace_module(), you can specify
 a named list of method names to example inputs to trace (see the inputs)
-argument below."><title>Trace a module — jit_trace_module • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Trace a module — jit_trace_module"><meta property="og:description" content="Trace a module and return an executable ScriptModule that will be optimized
+argument below."><meta property="og:description" content="Trace a module and return an executable ScriptModule that will be optimized
 using just-in-time compilation. When a module is passed to jit_trace(), only
 the forward method is run and traced. With jit_trace_module(), you can specify
 a named list of method names to example inputs to trace (see the inputs)
-argument below."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Trace a module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+
+      <h1>Trace a module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
       <div class="d-none name"><code>jit_trace_module.Rd</code></div>
     </div>
 
@@ -102,29 +84,42 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">jit_trace_module</span><span class="op">(</span><span class="va">mod</span>, <span class="va">...</span>, strict <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">jit_trace_module</span><span class="op">(</span><span class="va">mod</span>, <span class="va">...</span>, strict <span class="op">=</span> <span class="cn">TRUE</span>, respect_mode <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mod</dt>
+
+
+<dl><dt id="arg-mod">mod<a class="anchor" aria-label="anchor" href="#arg-mod"></a></dt>
 <dd><p>A torch <code><a href="nn_module.html">nn_module()</a></code>  containing methods whose names are specified
 in inputs. The given methods will be compiled as a part of a single ScriptModule.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>A named list containing sample inputs indexed by method names
 in mod. The inputs will be passed to methods whose names correspond to inputs
 keys while tracing. <code>list('forward'=example_forward_input, 'method2'=example_method2_input)</code>.</p></dd>
 
 
-<dt>strict</dt>
+<dt id="arg-strict">strict<a class="anchor" aria-label="anchor" href="#arg-strict"></a></dt>
 <dd><p>run the tracer in a strict mode or not (default: <code>TRUE</code>). Only
 turn this off when you want the tracer to record your mutable container types
 (currently list/dict) and you are sure that the container you are using in
 your problem is a constant structure and does not get used as control flow
 (<code>if</code>, <code>for</code>) conditions.</p></dd>
 
+
+<dt id="arg-respect-mode">respect_mode<a class="anchor" aria-label="anchor" href="#arg-respect-mode"></a></dt>
+<dd><p>(<code>logical(1)</code>)<br>
+Whether both modes ('train' or 'eval') should be traced. If <code>TRUE</code> (default),
+the underlying C++ ScriptModule will have two methods <code>trainforward()</code> and
+<code>evalforward()</code>.
+The <code>$forward()</code> method of the R torch module will then select either based
+on the mode.
+If <code>FALSE</code>, only the current mode of the module will be jitted and hence only
+one <code>forward()</code> method exists.</p></dd>
+
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -143,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/jit_tuple.html b/reference/jit_tuple.html
index 87ffcaf205..6b1d27be48 100644
--- a/reference/jit_tuple.html
+++ b/reference/jit_tuple.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Allows specifying that an output or input must be considered a jit
-tuple and instead of a list or dictionary when tracing."><title>Adds the 'jit_tuple' class to the input — jit_tuple • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Adds the 'jit_tuple' class to the input — jit_tuple"><meta property="og:description" content="Allows specifying that an output or input must be considered a jit
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Adds the 'jit_tuple' class to the input — jit_tuple • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Adds the 'jit_tuple' class to the input — jit_tuple"><meta name="description" content="Allows specifying that an output or input must be considered a jit
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+      <h1>Adds the 'jit_tuple' class to the input</h1>
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@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
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diff --git a/reference/jit_unserialize.html b/reference/jit_unserialize.html
new file mode 100644
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Unserialize a Script Module — jit_unserialize • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Unserialize a Script Module — jit_unserialize"><meta name="description" content="Unserializes a script module from a raw vector (generated with jit_serialize`)."><meta property="og:description" content="Unserializes a script module from a raw vector (generated with jit_serialize`)."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/trace.R" class="external-link"><code>R/trace.R</code></a></small>
+      <div class="d-none name"><code>jit_unserialize.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Unserializes a script module from a raw vector (generated with <code><a href="jit_serialize.html">jit_serialize</a></code>`).</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">jit_unserialize</span><span class="op">(</span><span class="va">obj</span><span class="op">)</span></span></code></pre></div>
+    </div>
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+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
+<dd><p>(<code>raw</code>)<br>
+Serialized model.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p><code>script_module</code>
+model &lt;- jit_trace(nn_linear(1, 1), torch_randn(1))
+serialized &lt;- jit_serialize(model)
+model2 &lt;- jit_unserialize(serialized)</p>
+    </div>
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diff --git a/reference/linalg_cholesky.html b/reference/linalg_cholesky.html
index d149493ed3..849e8eb442 100644
--- a/reference/linalg_cholesky.html
+++ b/reference/linalg_cholesky.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Letting  be  or ,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky"><meta name="description" content="Letting K be R or C,
 the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix
- is defined as"><title>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky"><meta property="og:description" content="Letting  be  or ,
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 the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix
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-      <img src="" class="logo" alt=""><h1>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_cholesky.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
+    <p>Letting K be R or C,
 the <strong>Cholesky decomposition</strong> of a complex Hermitian or real symmetric positive-definite matrix
- is defined as</p>
+A  K^n  n is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,19 +83,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
 consisting of symmetric or Hermitian positive-definite matrices.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>L</mi><msup><mi>L</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>L</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>L</mi><msup><mi>L</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>L</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = LL^{H}\mathrlap{\qquad L \in \mathbb{K}^{n \times n}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9304em;vertical-align:-0.0391em;"></span><span class="mord mathnormal">L</span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.8604em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">L</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where  is a lower triangular matrix and
- is the conjugate transpose when  is complex, and the
-transpose when  is real-valued.</p>
+<p>where L is a lower triangular matrix and
+L^H is the conjugate transpose when L is complex, and the
+transpose when L is real-valued.</p>
 <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
@@ -128,16 +112,16 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_eigh.html">linalg_eigh()</a></code> for a different decomposition of a Hermitian matrix.
 The eigenvalue decomposition gives more information about the matrix but it
 slower to compute than the Cholesky decomposition.</p></li>
-</ul><p>Other linalg: 
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -147,10 +131,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -176,7 +160,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,10} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -185,14 +169,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
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+
+
 
   </body></html>
 
diff --git a/reference/linalg_cholesky_ex.html b/reference/linalg_cholesky_ex.html
index f64df2331b..0de64cff82 100644
--- a/reference/linalg_cholesky_ex.html
+++ b/reference/linalg_cholesky_ex.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function skips the (slow) error checking and error message construction
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex"><meta name="description" content="This function skips the (slow) error checking and error message construction
 of linalg_cholesky(), instead directly returning the LAPACK
 error codes as part of a named tuple (L, info). This makes this function
 a faster way to check if a matrix is positive-definite, and it provides an
@@ -14,7 +14,7 @@
 The positive integer indicates the order of the leading minor that is not positive-definite,
 and the decomposition could not be completed.
 info filled with zeros indicates that the decomposition was successful.
-If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown."><title>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex"><meta property="og:description" content="This function skips the (slow) error checking and error message construction
+If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown."><meta property="og:description" content="This function skips the (slow) error checking and error message construction
 of linalg_cholesky(), instead directly returning the LAPACK
 error codes as part of a named tuple (L, info). This makes this function
 a faster way to check if a matrix is positive-definite, and it provides an
@@ -29,10 +29,7 @@
 The positive integer indicates the order of the leading minor that is not positive-definite,
 and the decomposition could not be completed.
 info filled with zeros indicates that the decomposition was successful.
-If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_cholesky_ex.Rd</code></div>
     </div>
 
@@ -140,12 +122,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): the Hermitian <code>n \times n</code> matrix or the batch of such matrices of size
 <code>(*, n, n)</code> where <code>*</code> is one or more batch dimensions.</p></dd>
 
 
-<dt>check_errors</dt>
+<dt id="arg-check-errors">check_errors<a class="anchor" aria-label="anchor" href="#arg-check-errors"></a></dt>
 <dd><p>(bool, optional): controls whether to check the content of <code>infos</code>. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -157,16 +141,16 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="linalg_cholesky.html">linalg_cholesky()</a></code> is a NumPy compatible variant that always checks for errors.</p>
-<p>Other linalg: 
+<p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -176,10 +160,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -194,18 +178,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $L</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8449  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3567  1.3276</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9757  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5533 -1.2421</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $info</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUIntType{} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -214,14 +198,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/linalg_cond.html b/reference/linalg_cond.html
index becb5e83be..3b2dcceafa 100644
--- a/reference/linalg_cond.html
+++ b/reference/linalg_cond.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Letting  be  or ,
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 the condition number  of a matrix
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-      <img src="" class="logo" alt=""><h1>Computes the condition number of a matrix with respect to a matrix norm.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the condition number of a matrix with respect to a matrix norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_cond.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
+    <p>Letting K be R or C,
 the <strong>condition number</strong>  of a matrix
- is defined as</p>
+A  K^n  n is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,26 +83,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions
 for <code>p</code> in <code>(2, -2)</code>, and of shape <code>(*, n, n)</code> where every matrix
 is invertible for <code>p</code> in <code>('fro', 'nuc', inf, -inf, 1, -1)</code>.</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(int, inf, -inf, 'fro', 'nuc', optional):
 the type of the matrix norm to use in the computations (see above). Default: <code>NULL</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A real-valued tensor, even when <code>A</code> is complex.</p>
+    <p>A real-valued tensor, even when <code>A</code> is complex.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>κ</mi><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">∥</mi><mi>A</mi><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub><mi mathvariant="normal">∥</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">\kappa(A) = \|A\|_p\|A^{-1}\|_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">κ</span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1502em;vertical-align:-0.2861em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mord">∥</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span></p>
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>κ</mi><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">∥</mi><mi>A</mi><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub><mi mathvariant="normal">∥</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><msub><mi mathvariant="normal">∥</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">\kappa(A) = \|A\|_p\|A^{-1}\|_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">κ</span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1502em;vertical-align:-0.2861em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mord">∥</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span></p>
 <p>The condition number of <code>A</code> measures the numerical stability of the linear system <code>AX = B</code>
 with respect to a matrix norm.</p>
 <p>Supports input of float, double, cfloat and cdouble dtypes.
@@ -132,8 +114,9 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <code><a href="linalg_norm.html">linalg_norm()</a></code> and <code><a href="linalg_inv.html">linalg_inv()</a></code>.</p>
 <p>As such, in this case, the matrix (or every matrix in the batch) <code>A</code> has to be square
 and invertible.</p>
-<p>For <code>p</code> in <code>(2, -2)</code>, this function can be computed in terms of the singular values</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>κ</mi><mn>2</mn></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mn>1</mn></msub><msub><mi>σ</mi><mi>n</mi></msub></mfrac><mspace width="2em"></mspace><msub><mi>κ</mi><mrow><mo>−</mo><mn>2</mn></mrow></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mi>n</mi></msub><msub><mi>σ</mi><mn>1</mn></msub></mfrac></mrow><annotation encoding="application/x-tex">\kappa_2(A) = \frac{\sigma_1}{\sigma_n}\qquad \kappa_{-2}(A) = \frac{\sigma_n}{\sigma_1}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.9436em;vertical-align:-0.836em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1076em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.836em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">2</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2083em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.9436em;vertical-align:-0.836em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1076em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.836em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
+<p>For <code>p</code> in <code>(2, -2)</code>, this function can be computed in terms of the singular values
+_1  ...  _n</p>
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>κ</mi><mn>2</mn></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mn>1</mn></msub><msub><mi>σ</mi><mi>n</mi></msub></mfrac><mspace width="2em"></mspace><msub><mi>κ</mi><mrow><mo>−</mo><mn>2</mn></mrow></msub><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><msub><mi>σ</mi><mi>n</mi></msub><msub><mi>σ</mi><mn>1</mn></msub></mfrac></mrow><annotation encoding="application/x-tex">\kappa_2(A) = \frac{\sigma_1}{\sigma_n}\qquad \kappa_{-2}(A) = \frac{\sigma_n}{\sigma_1}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.9436em;vertical-align:-0.836em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1076em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.836em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal">κ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">2</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2083em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.9436em;vertical-align:-0.836em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1076em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.836em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>In these cases, it is computed using <code><a href="linalg_svd.html">linalg_svd()</a></code>. For these norms, the matrix
 (or every matrix in the batch) <code>A</code> may have any shape.</p>
 <table class="table table"><tr><td><code>p</code></td><td>matrix norm</td></tr><tr><td><code>NULL</code></td><td><code>2</code>-norm (largest singular value)</td></tr><tr><td><code>'fro'</code></td><td>Frobenius norm</td></tr><tr><td><code>'nuc'</code></td><td>nuclear norm</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td></tr><tr><td><code>2</code></td><td>largest singular value</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td></tr></table></div>
@@ -155,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>Computes the determinant of a square matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the determinant of a square matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_det.Rd</code></div>
     </div>
 
@@ -101,22 +83,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -126,10 +110,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -145,13 +129,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">linalg_det</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2190</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6716</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1249</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1696</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5191</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 1de072a916..22fc7ffa65 100644
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+++ b/reference/linalg_eig.html
@@ -1,12 +1,9 @@
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- (if it exists) is defined as"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the eigenvalue decomposition of a square matrix if it exists.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the eigenvalue decomposition of a square matrix if it exists.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_eig.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
+    <p>Letting K be R or C,
 the <strong>eigenvalue decomposition</strong> of a square matrix
- (if it exists) is defined as</p>
+A  K^n  n (if it exists) is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,25 +83,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
 consisting of diagonalizable matrices.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list <code>(eigenvalues, eigenvectors)</code> which corresponds to  and  above.
+    <p>A list <code>(eigenvalues, eigenvectors)</code> which corresponds to  and V above.
 <code>eigenvalues</code> and <code>eigenvectors</code> will always be complex-valued, even when <code>A</code> is real. The eigenvectors
 will be given by the columns of <code>eigenvectors</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>V</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>V</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">C</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = V \operatorname{diag}(\Lambda) V^{-1}\mathrlap{\qquad V \in \mathbb{C}^{n \times n}, \Lambda \in \mathbb{C}^n}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1141em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord">Λ</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">C</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">Λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">C</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7144em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>This decomposition exists if and only if  is <code>diagonalizable</code>_.
+<p>This decomposition exists if and only if A is <code>diagonalizable</code>_.
 This is the case when all its eigenvalues are different.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
@@ -131,11 +113,11 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <ul><li><p>This function assumes that <code>A</code> is <code>diagonalizable</code>_ (for example, when all the
 eigenvalues are different). If it is not diagonalizable, the returned
-eigenvalues will be correct but .</p></li>
+eigenvalues will be correct but A  V diag()V^-1.</p></li>
 <li><p>The eigenvectors of a matrix are not unique, nor are they continuous with respect to
 <code>A</code>. Due to this lack of uniqueness, different hardware and software may compute
 different eigenvectors.
@@ -146,8 +128,8 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 <li><p>Gradients computed using <code>V</code> will only be finite when <code>A</code> does not have repeated eigenvalues.
 Furthermore, if the distance between any two eigenvalues is close to zero,
 the gradient will be numerically unstable, as it depends on the eigenvalues
- through the computation of
-.</p></li>
+_i through the computation of
+1_i  j _i - _j.</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -160,16 +142,16 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 decomposition that works on matrices of any shape.</p></li>
 <li><p><code><a href="linalg_qr.html">linalg_qr()</a></code> for another (much faster) decomposition that works on matrices of
 any shape.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -179,10 +161,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -196,7 +178,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -205,14 +187,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
+
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index 7084c9180f..d8e2280b05 100644
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+++ b/reference/linalg_eigh.html
@@ -1,12 +1,9 @@
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 the eigenvalue decomposition of a complex Hermitian or real symmetric matrix
- is defined as"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_eigh.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
+    <p>Letting K be R or C,
 the <strong>eigenvalue decomposition</strong> of a complex Hermitian or real symmetric matrix
- is defined as</p>
+A  K^n  n is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,34 +83,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
 consisting of symmetric or Hermitian matrices.</p></dd>
 
 
-<dt>UPLO</dt>
+<dt id="arg-uplo">UPLO<a class="anchor" aria-label="anchor" href="#arg-uplo"></a></dt>
 <dd><p>('L', 'U', optional): controls whether to use the upper or lower triangular part
 of <code>A</code> in the computations. Default: <code>'L'</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list <code>(eigenvalues, eigenvectors)</code> which corresponds to  and  above.
+    <p>A list <code>(eigenvalues, eigenvectors)</code> which corresponds to  and Q above.
 <code>eigenvalues</code> will always be real-valued, even when <code>A</code> is complex.</p>
-
-
 <p>It will also be ordered in ascending order.
 <code>eigenvectors</code> will have the same dtype as <code>A</code> and will contain the eigenvectors as its columns.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>Q</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">Λ</mi><mo stretchy="false">)</mo><msup><mi>Q</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi mathvariant="normal">Λ</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>n</mi></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = Q \operatorname{diag}(\Lambda) Q^{H}\mathrlap{\qquad Q \in \mathbb{K}^{n \times n}, \Lambda \in \mathbb{R}^n}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1413em;vertical-align:-0.25em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord">Λ</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal">Q</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">Λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7144em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where  is the conjugate transpose when  is complex, and the transpose when  is real-valued.
- is orthogonal in the real case and unitary in the complex case.</p>
+<p>where Q^H is the conjugate transpose when Q is complex, and the transpose when Q is real-valued.
+Q is orthogonal in the real case and unitary in the complex case.</p>
 <p>Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
@@ -142,13 +122,13 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <ul><li><p>The eigenvectors of a symmetric matrix are not unique, nor are they continuous with
 respect to <code>A</code>. Due to this lack of uniqueness, different hardware and
 software may compute different eigenvectors.
 This non-uniqueness is caused by the fact that multiplying an eigenvector by
-<code>-1</code> in the real case or by  in the complex
+<code>-1</code> in the real case or by e^i ,   R in the complex
 case produces another set of valid eigenvectors of the matrix.
 This non-uniqueness problem is even worse when the matrix has repeated eigenvalues.
 In this case, one may multiply the associated eigenvectors spanning
@@ -158,8 +138,8 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 <code>A</code> has unique eigenvalues.
 Furthermore, if the distance between any two eigvalues is close to zero,
 the gradient will be numerically unstable, as it depends on the eigenvalues
- through the computation of
-.</p></li>
+_i through the computation of
+1_i  j _i - _j.</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -176,16 +156,16 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 decomposition of matrices of any shape.</p></li>
 <li><p><code><a href="linalg_qr.html">linalg_qr()</a></code> for another (much faster) decomposition that works on general
 matrices.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
-<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
 <code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -195,10 +175,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -212,19 +192,19 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6851</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1951</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.6945</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1325</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5609 -0.8279</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8279 -0.5609</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8597  0.5108</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5108 -0.8597</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -233,14 +213,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/linalg_eigvals.html b/reference/linalg_eigvals.html
index b07496a5ff..d0138ece55 100644
--- a/reference/linalg_eigvals.html
+++ b/reference/linalg_eigvals.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Letting  be  or ,
-the eigenvalues of a square matrix  are defined
-as the roots (counted with multiplicity) of the polynomial p of degree n given by"><title>Computes the eigenvalues of a square matrix. — linalg_eigvals • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the eigenvalues of a square matrix. — linalg_eigvals"><meta property="og:description" content="Letting  be  or ,
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+the eigenvalues of a square matrix A  K^n  n are defined
+as the roots (counted with multiplicity) of the polynomial p of degree n given by"><meta property="og:description" content="Letting K be R or C,
+the eigenvalues of a square matrix A  K^n  n are defined
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the eigenvalues of a square matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the eigenvalues of a square matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_eigvals.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
-the <strong>eigenvalues</strong> of a square matrix  are defined
+    <p>Letting K be R or C,
+the <strong>eigenvalues</strong> of a square matrix A  K^n  n are defined
 as the roots (counted with multiplicity) of the polynomial <code>p</code> of degree <code>n</code> given by</p>
     </div>
 
@@ -101,16 +83,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">C</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">C</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{C}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">λ</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop"><span class="mord mathrm">det</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">λ</span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.7335em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord mathbb">C</span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where  is the <code>n</code>-dimensional identity matrix.
+<p>where I_n is the <code>n</code>-dimensional identity matrix.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
@@ -123,16 +107,16 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="linalg_eig.html">linalg_eig()</a></code> computes the full eigenvalue decomposition.</p>
-<p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
 <code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -142,10 +126,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -159,7 +143,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -168,14 +152,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/linalg_eigvalsh.html b/reference/linalg_eigvalsh.html
index b29064da11..4eac65452b 100644
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@@ -1,12 +1,9 @@
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-the eigenvalues of a complex Hermitian or real symmetric  matrix 
-are defined as the roots (counted with multiplicity) of the polynomial p of degree n given by"><title>Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh"><meta property="og:description" content="Letting  be  or ,
-the eigenvalues of a complex Hermitian or real symmetric  matrix 
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+the eigenvalues of a complex Hermitian or real symmetric  matrix A  K^n  n
+are defined as the roots (counted with multiplicity) of the polynomial p of degree n given by"><meta property="og:description" content="Letting K be R or C,
+the eigenvalues of a complex Hermitian or real symmetric  matrix A  K^n  n
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the eigenvalues of a complex Hermitian or real symmetric matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_eigvalsh.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
-the <strong>eigenvalues</strong> of a complex Hermitian or real symmetric  matrix 
+    <p>Letting K be R or C,
+the <strong>eigenvalues</strong> of a complex Hermitian or real symmetric  matrix A  K^n  n
 are defined as the roots (counted with multiplicity) of the polynomial <code>p</code> of degree <code>n</code> given by</p>
     </div>
 
@@ -101,29 +83,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
 consisting of symmetric or Hermitian matrices.</p></dd>
 
 
-<dt>UPLO</dt>
+<dt id="arg-uplo">UPLO<a class="anchor" aria-label="anchor" href="#arg-uplo"></a></dt>
 <dd><p>('L', 'U', optional): controls whether to use the upper or lower triangular part
 of <code>A</code> in the computations. Default: <code>'L'</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A real-valued tensor cointaining the eigenvalues even when <code>A</code> is complex.
+    <p>A real-valued tensor cointaining the eigenvalues even when <code>A</code> is complex.
 The eigenvalues are returned in ascending order.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">R</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>λ</mi><mo stretchy="false">)</mo><mo>=</mo><mi mathvariant="normal">det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo>−</mo><mi>λ</mi><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub><mo stretchy="false">)</mo><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>λ</mi><mo>∈</mo><mi mathvariant="double-struck">R</mi></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 p(\lambda) = \operatorname{det}(A - \lambda \mathrm{I}_n)\mathrlap{\qquad \lambda \in \mathbb{R}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">λ</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop"><span class="mord mathrm">det</span></span><span class="mopen">(</span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">λ</span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:0.7335em;vertical-align:-0.0391em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">λ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord mathbb">R</span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where  is the <code>n</code>-dimensional identity matrix.</p>
+<p>where I_n is the <code>n</code>-dimensional identity matrix.</p>
 <p>The eigenvalues of a real symmetric or complex Hermitian matrix are always real.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
@@ -136,16 +118,16 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index">
 <ul><li><p><code><a href="linalg_eigh.html">linalg_eigh()</a></code> computes the full eigenvalue decomposition.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -155,10 +137,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -171,12 +153,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">linalg_eigvalsh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1600</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5052</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9836</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6534</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -185,14 +167,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
+
+
 
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diff --git a/reference/linalg_householder_product.html b/reference/linalg_householder_product.html
index ff16556980..498e35a7a2 100644
--- a/reference/linalg_householder_product.html
+++ b/reference/linalg_householder_product.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Letting  be  or ,
-for a matrix  with columns 
-with  and a vector  with ,
-this function computes the first  columns of the matrix"><title>Computes the first n columns of a product of Householder matrices. — linalg_householder_product • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the first n columns of a product of Householder matrices. — linalg_householder_product"><meta property="og:description" content="Letting  be  or ,
-for a matrix  with columns 
-with  and a vector  with ,
-this function computes the first  columns of the matrix"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the first n columns of a product of Householder matrices. — linalg_householder_product • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the first n columns of a product of Householder matrices. — linalg_householder_product"><meta name="description" content="Letting K be R or C,
+for a matrix V  K^m  n with columns v_i  K^m
+with m  n and a vector   K^k with k  n,
+this function computes the first n columns of the matrix"><meta property="og:description" content="Letting K be R or C,
+for a matrix V  K^m  n with columns v_i  K^m
+with m  n and a vector   K^k with k  n,
+this function computes the first n columns of the matrix"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -16,85 +13,70 @@
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the first <code>n</code> columns of a product of Householder matrices.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the first <code>n</code> columns of a product of Householder matrices.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_householder_product.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
-for a matrix  with columns 
-with  and a vector  with ,
-this function computes the first  columns of the matrix</p>
+    <p>Letting K be R or C,
+for a matrix V  K^m  n with columns v_i  K^m
+with m  n and a vector   K^k with k  n,
+this function computes the first n columns of the matrix</p>
     </div>
 
     <div class="section level2">
@@ -104,21 +86,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>tau</dt>
+<dt id="arg-tau">tau<a class="anchor" aria-label="anchor" href="#arg-tau"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, k)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>H</mi><mn>1</mn></msub><msub><mi>H</mi><mn>2</mn></msub><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><msub><mi>H</mi><mi>k</mi></msub><mspace width="2em"></mspace><mi>w</mi><mi>i</mi><mi>t</mi><mi>h</mi><mspace width="2em"></mspace><msub><mi>H</mi><mi>i</mi></msub><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>m</mi></msub><mo>−</mo><msub><mi>τ</mi><mi>i</mi></msub><msub><mi>v</mi><mi>i</mi></msub><msubsup><mi>v</mi><mi>i</mi><mi>H</mi></msubsup></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>H</mi><mn>1</mn></msub><msub><mi>H</mi><mn>2</mn></msub><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><msub><mi>H</mi><mi>k</mi></msub><mspace width="2em"></mspace><mi>w</mi><mi>i</mi><mi>t</mi><mi>h</mi><mspace width="2em"></mspace><msub><mi>H</mi><mi>i</mi></msub><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>m</mi></msub><mo>−</mo><msub><mi>τ</mi><mi>i</mi></msub><msub><mi>v</mi><mi>i</mi></msub><msubsup><mi>v</mi><mi>i</mi><mi>H</mi></msubsup></mrow><annotation encoding="application/x-tex">
 H_1H_2 ... H_k \qquad with \qquad H_i = \mathrm{I}_m - \tau_i v_i v_i^{H}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord">...</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.02691em;">w</span><span class="mord mathnormal">i</span><span class="mord mathnormal">t</span><span class="mord mathnormal">h</span><span class="mspace" style="margin-right:2em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">m</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.1383em;vertical-align:-0.247em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.1132em;">τ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.1132em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">v</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">v</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-2.453em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.247em;"><span></span></span></span></span></span></span></span></span></span></span></p>
-<p>where  is the <code>m</code>-dimensional identity matrix and
- is the conjugate transpose when  is complex, and the transpose when  is real-valued.
+<p>where I_m is the <code>m</code>-dimensional identity matrix and
+v^H is the conjugate transpose when v is complex, and the transpose when v is real-valued.
 See <a href="https://netlib.org/lapack/lug/node128.html" class="external-link">Representation of Orthogonal or Unitary Matrices</a> for
 further details.</p>
 <p>Supports inputs of float, double, cfloat and cdouble dtypes.
@@ -138,16 +122,16 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <li><p><code><a href="torch_ormqr.html">torch_ormqr()</a></code> is a related function that computes the matrix multiplication
 of a product of Householder matrices with another matrix.
 However, that function is not supported by autograd.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -157,10 +141,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -177,7 +161,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -186,14 +170,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/linalg_inv.html b/reference/linalg_inv.html
index 930a1100b5..506fa0394f 100644
--- a/reference/linalg_inv.html
+++ b/reference/linalg_inv.html
@@ -1,8 +1,5 @@
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the inverse of a square matrix if it exists.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the inverse of a square matrix if it exists.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_inv.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
 consisting of invertible matrices.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>Letting  be  or ,
-for a matrix ,
-its <strong>inverse matrix</strong>  (if it exists) is defined as</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mi>A</mi><mo>=</mo><mi>A</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub></mrow><annotation encoding="application/x-tex">
+    <p>Letting K be R or C,
+for a matrix A  K^n  n,
+its <strong>inverse matrix</strong> A^-1  K^n  n (if it exists) is defined as</p>
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mi>A</mi><mo>=</mo><mi>A</mi><msup><mi>A</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mo>=</mo><msub><mi mathvariant="normal">I</mi><mi>n</mi></msub></mrow><annotation encoding="application/x-tex">
 A^{-1}A = AA^{-1} = \mathrm{I}_n
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8641em;"></span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8641em;"></span><span class="mord mathnormal">A</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8641em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathrm">I</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">n</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>
-where  is the <code>n</code>-dimensional identity matrix.</p>
-<p>The inverse matrix exists if and only if  is invertible. In this case,
+where I_n is the <code>n</code>-dimensional identity matrix.</p>
+<p>The inverse matrix exists if and only if A is invertible. In this case,
 the inverse is unique.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if <code>A</code> is a batch of matrices
@@ -125,14 +109,14 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 of any shape.
 <code><a href="linalg_solve.html">linalg_solve()</a></code> computes <code>A$inv() %*% B</code> with a
 numerically stable algorithm.</p>
-<p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
 <code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
@@ -144,10 +128,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">linalg_inv</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2507 -0.4972 -0.1922 -0.9363</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2006 -1.1907  3.5489  0.3504</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1944 -1.1430  3.6438 -0.8388</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4556 -0.7984  0.8755 -0.3526</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2312 -0.7295 -0.1168 -0.8399</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3578  0.2195  0.5738  0.0203</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6391  0.8412  0.2621 -0.7401</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1649  0.0259 -0.4296 -0.0122</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -176,14 +160,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/linalg_inv_ex.html b/reference/linalg_inv_ex.html
index a7994db268..4db2ddadeb 100644
--- a/reference/linalg_inv_ex.html
+++ b/reference/linalg_inv_ex.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Returns a namedtuple (inverse, info). inverse contains the result of
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex"><meta name="description" content="Returns a namedtuple (inverse, info). inverse contains the result of
 inverting A and info stores the LAPACK error codes.
 If A is not an invertible matrix, or if it's a batch of matrices
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@@ -10,7 +10,7 @@
 If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if A is a batch of matrices then
-the output has the same batch dimensions."><title>Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex"><meta property="og:description" content="Returns a namedtuple (inverse, info). inverse contains the result of
+the output has the same batch dimensions."><meta property="og:description" content="Returns a namedtuple (inverse, info). inverse contains the result of
 inverting A and info stores the LAPACK error codes.
 If A is not an invertible matrix, or if it's a batch of matrices
 and one or more of them is not an invertible matrix,
@@ -21,10 +21,7 @@
 If check_errors=TRUE and info contains positive integers, then a RuntimeError is thrown.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if A is a batch of matrices then
-the output has the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+the output has the same batch dimensions."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the inverse of a square matrix if it is invertible.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the inverse of a square matrix if it is invertible.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_inv_ex.Rd</code></div>
     </div>
 
@@ -128,12 +110,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions
 consisting of square matrices.</p></dd>
 
 
-<dt>check_errors</dt>
+<dt id="arg-check-errors">check_errors<a class="anchor" aria-label="anchor" href="#arg-check-errors"></a></dt>
 <dd><p>(bool, optional): controls whether to check the content of <code>info</code>. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -146,14 +130,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="linalg_inv.html">linalg_inv()</a></code> is a NumPy compatible variant that always checks for errors.</p>
-<p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
@@ -165,10 +149,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -182,7 +166,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -191,14 +175,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/linalg_lstsq.html b/reference/linalg_lstsq.html
index 189097e700..220d75977f 100644
--- a/reference/linalg_lstsq.html
+++ b/reference/linalg_lstsq.html
@@ -1,12 +1,9 @@
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-the least squares problem for a linear system  with
- is defined as"><title>Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq"><meta property="og:description" content="Letting  be  or ,
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+the least squares problem for a linear system AX = B with
+A  K^m  n, B  K^m  k is defined as"><meta property="og:description" content="Letting K be R or C,
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-      <img src="" class="logo" alt=""><h1>Computes a solution to the least squares problem of a system of linear equations.</h1>
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+      <h1>Computes a solution to the least squares problem of a system of linear equations.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_lstsq.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
-the <strong>least squares problem</strong> for a linear system  with
- is defined as</p>
+    <p>Letting K be R or C,
+the <strong>least squares problem</strong> for a linear system AX = B with
+A  K^m  n, B  K^m  k is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,25 +83,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): lhs tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>B</dt>
+<dt id="arg-b">B<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>(Tensor): rhs tensor of shape <code>(*, m, k)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>rcond</dt>
+<dt id="arg-rcond">rcond<a class="anchor" aria-label="anchor" href="#arg-rcond"></a></dt>
 <dd><p>(float, optional): used to determine the effective rank of <code>A</code>.
 If <code>rcond = NULL</code>, <code>rcond</code> is set to the machine
 precision of the dtype of <code>A</code> times <code>max(m, n)</code>. Default: <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused.</p></dd>
 
 
-<dt>driver</dt>
+<dt id="arg-driver">driver<a class="anchor" aria-label="anchor" href="#arg-driver"></a></dt>
 <dd><p>(str, optional): name of the LAPACK/MAGMA method to be used.
 If <code>NULL</code>, <code>'gelsy'</code> is used for CPU inputs and <code>'gels'</code> for CUDA inputs.
 Default: <code>NULL</code>.</p></dd>
@@ -127,16 +111,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list <code>(solution, residuals, rank, singular_values)</code>.</p>
+    <p>A list <code>(solution, residuals, rank, singular_values)</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><munder><mrow><mi>min</mi><mo>⁡</mo></mrow><mrow><mi>X</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>k</mi></mrow></msup></mrow></munder><mi mathvariant="normal">∥</mi><mi>A</mi><mi>X</mi><mo>−</mo><mi>B</mi><msub><mi mathvariant="normal">∥</mi><mi>F</mi></msub></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><munder><mrow><mi>min</mi><mo>⁡</mo></mrow><mrow><mi>X</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>k</mi></mrow></msup></mrow></munder><mi mathvariant="normal">∥</mi><mi>A</mi><mi>X</mi><mo>−</mo><mi>B</mi><msub><mi mathvariant="normal">∥</mi><mi>F</mi></msub></mrow><annotation encoding="application/x-tex">
 \min_{X \in \mathbb{K}^{n \times k}} \|AX - B\|_F
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.5908em;vertical-align:-0.8408em;"></span><span class="mop op-limits"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.6679em;"><span style="top:-2.2866em;margin-left:0em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.07847em;">X</span><span class="mrel mtight">∈</span><span class="mord mtight"><span class="mord mathbb mtight">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.782em;"><span style="top:-2.786em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span></span></span></span></span><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span><span class="mop">min</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.8408em;"><span></span></span></span></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∥</span><span class="mord mathnormal">A</span><span class="mord mathnormal" style="margin-right:0.07847em;">X</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.05017em;">B</span><span class="mord"><span class="mord">∥</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3283em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">F</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span></p>
-<p>where  denotes the Frobenius norm.
+<p>where \|-\|_F denotes the Frobenius norm.
 Supports inputs of float, double, cfloat and cdouble dtypes.</p>
 <p>Also supports batches of matrices, and if the inputs are batches of matrices then
 the output has the same batch dimensions.
@@ -152,13 +134,13 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul><p>See also the <a href="https://netlib.org/lapack/lug/node27.html" class="external-link">full description of these drivers</a></p>
 <p><code>rcond</code> is used to determine the effective rank of the matrices in <code>A</code>
 when <code>driver</code> is one of (<code>'gelsy'</code>, <code>'gelsd'</code>, <code>'gelss'</code>).
-In this case, if  are the singular values of <code>A</code> in decreasing order,
- will be rounded down to zero if .
+In this case, if _i are the singular values of <code>A</code> in decreasing order,
+_i will be rounded down to zero if _i  rcond  _1.
 If <code>rcond = NULL</code> (default), <code>rcond</code> is set to the machine precision of the dtype of <code>A</code>.</p>
 <p>This function returns the solution to the problem and some extra information in a list of
 four tensors <code>(solution, residuals, rank, singular_values)</code>. For inputs <code>A</code>, <code>B</code>
 of shape <code>(*, m, n)</code>, <code>(*, m, k)</code> respectively, it cointains</p><ul><li><p><code>solution</code>: the least squares solution. It has shape <code>(*, n, k)</code>.</p></li>
-<li><p><code>residuals</code>: the squared residuals of the solutions, that is, .
+<li><p><code>residuals</code>: the squared residuals of the solutions, that is, \|AX - B\|_F^2.
 It has shape equal to the batch dimensions of <code>A</code>.
 It is computed when <code>m &gt; n</code> and every matrix in <code>A</code> is full-rank,
 otherwise, it is an empty tensor.
@@ -180,7 +162,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>The default value of <code>rcond</code> may change in a future PyTorch release.
 It is therefore recommended to use a fixed value to avoid potential
@@ -188,17 +170,17 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
 <code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
@@ -207,10 +189,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -228,7 +210,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -237,14 +219,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_matrix_norm.html b/reference/linalg_matrix_norm.html
index ce0af8b9b8..c26b0ec03d 100644
--- a/reference/linalg_matrix_norm.html
+++ b/reference/linalg_matrix_norm.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If A is complex valued, it computes the norm of A$abs()
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes a matrix norm. — linalg_matrix_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a matrix norm. — linalg_matrix_norm"><meta name="description" content="If A is complex valued, it computes the norm of A$abs()
 Support input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices: the norm will be computed over the
 dimensions specified by the 2-tuple dim and the other dimensions will
-be treated as batch dimensions. The output will have the same batch dimensions."><title>Computes a matrix norm. — linalg_matrix_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a matrix norm. — linalg_matrix_norm"><meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
+be treated as batch dimensions. The output will have the same batch dimensions."><meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
 Support input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices: the norm will be computed over the
 dimensions specified by the 2-tuple dim and the other dimensions will
-be treated as batch dimensions. The output will have the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -18,77 +15,62 @@
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes a matrix norm.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes a matrix norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_matrix_norm.Rd</code></div>
     </div>
 
@@ -113,28 +95,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor with two or more dimensions. By default its
 shape is interpreted as <code>(*, m, n)</code> where <code>*</code> is zero or more
 batch dimensions, but this behavior can be controlled using <code>dim</code>.</p></dd>
 
 
-<dt>ord</dt>
+<dt id="arg-ord">ord<a class="anchor" aria-label="anchor" href="#arg-ord"></a></dt>
 <dd><p>(int, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>'fro'</code></p></dd>
 
 
-<dt>dim</dt>
-<dd><p>(int, Tupleint, optional): dimensions over which to compute
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
+<dd><p>(int, <code>Tuple[int]</code>, optional): dimensions over which to compute
 the vector or matrix norm. See above for the behavior when <code>dim=NULL</code>.
 Default: <code>NULL</code></p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
 in the result as dimensions with size one. Default: <code>FALSE</code></p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
 <code>dtype</code> before performing the operation, and the returned tensor's type
 will be <code>dtype</code>. Default: <code>NULL</code></p></dd>
@@ -146,17 +130,17 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 supported:</p><table class="table table"><tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr><tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr><tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr><tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr><tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr><tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr></table></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
 <code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
@@ -165,10 +149,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -191,7 +175,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -200,14 +184,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_matrix_power.html b/reference/linalg_matrix_power.html
index 24a3ecdbcf..3dd14981ed 100644
--- a/reference/linalg_matrix_power.html
+++ b/reference/linalg_matrix_power.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Supports input of float, double, cfloat and cdouble dtypes.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power"><meta name="description" content="Supports input of float, double, cfloat and cdouble dtypes.
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+the output has the same batch dimensions."><meta property="og:description" content="Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if A is a batch of matrices then
-the output has the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the <code>n</code>-th power of a square matrix for an integer <code>n</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_matrix_power.Rd</code></div>
     </div>
 
@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, m)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int): the exponent.</p></dd>
 
 </dl></div>
@@ -119,17 +103,17 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="linalg_solve.html">linalg_solve()</a></code> computes <code>A$inverse() %*% B</code> with a
 numerically stable algorithm.</p>
-<p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_rank.html">linalg_matrix_rank</a>()</code>,
@@ -138,10 +122,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -160,7 +144,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -169,14 +153,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_matrix_rank.html b/reference/linalg_matrix_rank.html
index 47c4cdb0bd..557f2c2119 100644
--- a/reference/linalg_matrix_rank.html
+++ b/reference/linalg_matrix_rank.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
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-that are greater than the specified tol threshold."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the numerical rank of a matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the numerical rank of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_matrix_rank.Rd</code></div>
     </div>
 
@@ -108,29 +90,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more
 batch dimensions.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Not currently used.</p></dd>
 
 
-<dt>atol</dt>
+<dt id="arg-atol">atol<a class="anchor" aria-label="anchor" href="#arg-atol"></a></dt>
 <dd><p>the absolute tolerance value. When <code>NULL</code> it’s considered to be zero.</p></dd>
 
 
-<dt>rtol</dt>
+<dt id="arg-rtol">rtol<a class="anchor" aria-label="anchor" href="#arg-rtol"></a></dt>
 <dd><p>the relative tolerance value. See above for the value it takes when <code>NULL</code>.</p></dd>
 
 
-<dt>tol</dt>
+<dt id="arg-tol">tol<a class="anchor" aria-label="anchor" href="#arg-tol"></a></dt>
 <dd><p>(float, Tensor, optional): the tolerance value. See above for
 the value it takes when <code>NULL</code>. Default: <code>NULL</code>.</p></dd>
 
 
-<dt>hermitian</dt>
+<dt id="arg-hermitian">hermitian<a class="anchor" aria-label="anchor" href="#arg-hermitian"></a></dt>
 <dd><p>(bool, optional): indicates whether <code>A</code> is Hermitian if complex
 or symmetric if real. Default: <code>FALSE</code>.</p></dd>
 
@@ -145,10 +129,10 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 triangular part of the matrix is used in the computations.</p>
 <p>If <code>tol</code> is not specified and <code>A</code> is a matrix of dimensions <code>(m, n)</code>,
 the tolerance is set to be</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>t</mi><mi>o</mi><mi>l</mi><mo>=</mo><msub><mi>σ</mi><mn>1</mn></msub><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>m</mi><mo separator="true">,</mo><mi>n</mi><mo stretchy="false">)</mo><mi>ε</mi></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>t</mi><mi>o</mi><mi>l</mi><mo>=</mo><msub><mi>σ</mi><mn>1</mn></msub><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>m</mi><mo separator="true">,</mo><mi>n</mi><mo stretchy="false">)</mo><mi>ε</mi></mrow><annotation encoding="application/x-tex">
 tol = \sigma_1 \max(m, n) \varepsilon
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">t</span><span class="mord mathnormal">o</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord mathnormal">m</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">n</span><span class="mclose">)</span><span class="mord mathnormal">ε</span></span></span></span></span></p>
-<p>where  is the largest singular value
+<p>where _1 is the largest singular value
 (or eigenvalue in absolute value when <code>hermitian = TRUE</code>), and
  is the epsilon value for the dtype of <code>A</code> (see <code><a href="torch_finfo.html">torch_finfo()</a></code>).</p>
 <p>If <code>A</code> is a batch of matrices, <code>tol</code> is computed this way for every element of
@@ -156,17 +140,17 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -175,10 +159,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -195,7 +179,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -204,14 +188,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/linalg_multi_dot.html b/reference/linalg_multi_dot.html
index 601cfde37a..6f3c2a82bd 100644
--- a/reference/linalg_multi_dot.html
+++ b/reference/linalg_multi_dot.html
@@ -1,10 +1,7 @@
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Efficiently multiplies two or more matrices</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Efficiently multiplies two or more matrices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_multi_dot.Rd</code></div>
     </div>
 
@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(<code>Sequence[Tensor]</code>): two or more tensors to multiply. The first and last
 tensors may be 1D or 2D. Every other tensor must be 2D.</p></dd>
 
@@ -122,7 +106,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 <code>a * b * c</code>. Given matrices <code>A</code>, <code>B</code>, <code>C</code> with shapes <code>(10, 100)</code>,
 <code>(100, 5)</code>, <code>(5, 50)</code> respectively, we can calculate the cost of different
 multiplication orders as follows:</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left right" columnspacing="0em 1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo stretchy="false">(</mo><mi>A</mi><mi>B</mi><mo stretchy="false">)</mo><mi>C</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>+</mo><mn>10</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>7500</mn><mtext> </mtext><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">(</mo><mi>B</mi><mi>C</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>50</mn><mo>+</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>75000</mn></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left right" columnspacing="0em 1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo stretchy="false">(</mo><mi>A</mi><mi>B</mi><mo stretchy="false">)</mo><mi>C</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>+</mo><mn>10</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>7500</mn><mtext> </mtext><mi mathvariant="normal">cost</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>A</mi><mo stretchy="false">(</mo><mi>B</mi><mi>C</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mo>=</mo><mn>10</mn><mo>×</mo><mn>100</mn><mo>×</mo><mn>50</mn><mo>+</mo><mn>100</mn><mo>×</mo><mn>5</mn><mo>×</mo><mn>50</mn><mo>=</mo><mn>75000</mn></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">
 \begin{align*}
 \operatorname{cost}((AB)C) &amp;= 10 \times 100 \times 5 + 10 \times 5 \times 50 = 7500 \
 \operatorname{cost}(A(BC)) &amp;= 10 \times 100 \times 50 + 100 \times 5 \times 50 = 75000
@@ -132,17 +116,17 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -151,10 +135,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -171,7 +155,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -180,14 +164,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/linalg_norm.html b/reference/linalg_norm.html
index 1c90a3ec94..07f7f2349d 100644
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+++ b/reference/linalg_norm.html
@@ -1,12 +1,9 @@
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes a vector or matrix norm.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes a vector or matrix norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_norm.Rd</code></div>
     </div>
 
@@ -101,26 +83,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n)</code> or <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions</p></dd>
 
 
-<dt>ord</dt>
+<dt id="arg-ord">ord<a class="anchor" aria-label="anchor" href="#arg-ord"></a></dt>
 <dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>NULL</code></p></dd>
 
 
-<dt>dim</dt>
-<dd><p>(int, Tupleint, optional): dimensions over which to compute
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
+<dd><p>(int, <code>Tuple[int]</code>, optional): dimensions over which to compute
 the vector or matrix norm. See above for the behavior when <code>dim=NULL</code>.
 Default: <code>NULL</code></p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
 in the result as dimensions with size one. Default: <code>FALSE</code></p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
 <code>dtype</code> before performing the operation, and the returned tensor's type
 will be <code>dtype</code>. Default: <code>NULL</code></p></dd>
@@ -128,7 +112,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    
+
 <ul><li><p>If <code>dim</code> is an int, the vector norm will be computed.</p></li>
 <li><p>If <code>dim</code> is a 2-tuple, the matrix norm will be computed.</p></li>
 <li><p>If <code>dim=NULL</code> and <code>ord=NULL</code>, A will be flattened to 1D and the 2-norm of the resulting vector will be computed.</p></li>
@@ -137,17 +121,17 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 supported:</p><table class="table table"><tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr><tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr><tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr><tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr><tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr><tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr></table></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -156,10 +140,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -181,7 +165,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -190,14 +174,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_pinv.html b/reference/linalg_pinv.html
index 3fd5c60697..1857576ad3 100644
--- a/reference/linalg_pinv.html
+++ b/reference/linalg_pinv.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The pseudoinverse may be defined algebraically_
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 Supports input of float, double, cfloat and cdouble dtypes.
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+the output has the same batch dimensions."><meta property="og:description" content="The pseudoinverse may be defined algebraically_
 but it is more computationally convenient to understand it through the SVD_
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if A is a batch of matrices then
-the output has the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the pseudoinverse (Moore-Penrose inverse) of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_pinv.Rd</code></div>
     </div>
 
@@ -107,11 +89,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>rcond</dt>
+<dt id="arg-rcond">rcond<a class="anchor" aria-label="anchor" href="#arg-rcond"></a></dt>
 <dd><p>(float or Tensor, optional): the tolerance value to determine when is a singular value zero
 If it is a <code>torch_Tensor</code>, its shape must be
 broadcastable to that of the singular values of
@@ -120,16 +104,16 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 Default: <code>0</code>.</p></dd>
 
 
-<dt>hermitian</dt>
+<dt id="arg-hermitian">hermitian<a class="anchor" aria-label="anchor" href="#arg-hermitian"></a></dt>
 <dd><p>(bool, optional): indicates whether <code>A</code> is Hermitian if complex
 or symmetric if real. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>atol</dt>
+<dt id="arg-atol">atol<a class="anchor" aria-label="anchor" href="#arg-atol"></a></dt>
 <dd><p>the absolute tolerance value. When <code>NULL</code> it’s considered to be zero.</p></dd>
 
 
-<dt>rtol</dt>
+<dt id="arg-rtol">rtol<a class="anchor" aria-label="anchor" href="#arg-rtol"></a></dt>
 <dd><p>the relative tolerance value. See above for the value it takes when <code>NULL</code>.</p></dd>
 
 </dl></div>
@@ -158,17 +142,17 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <ul><li><p><code><a href="linalg_inv.html">linalg_inv()</a></code> computes the inverse of a square matrix.</p></li>
 <li><p><code><a href="linalg_lstsq.html">linalg_lstsq()</a></code> computes <code>A$pinv() %*% B</code> with a
 numerically stable algorithm.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -177,10 +161,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_norm.html">linalg_norm</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -193,15 +177,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">linalg_pinv</span><span class="op">(</span><span class="va">A</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6886  0.2109  0.0239</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0302  0.1808  0.1159</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3785  0.2301 -0.3357</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4896 -0.6582  0.2559</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1375 -0.4979  0.0205</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3203 -0.5515 -0.4246</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0289 -0.0081  0.2335</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3591 -0.0429 -0.9052</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4663 -1.0388  1.8590</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1553  0.1277  0.7656</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -210,14 +194,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
+
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index 6350750884..c2ed67d710 100644
--- a/reference/linalg_qr.html
+++ b/reference/linalg_qr.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
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 the full QR decomposition of a matrix
- is defined as"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Computes the QR decomposition of a matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
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+      <h1>Computes the QR decomposition of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
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     <div class="ref-description section level2">
-    <p>Letting  be  or ,
+    <p>Letting K be R or C,
 the <strong>full QR decomposition</strong> of a matrix
- is defined as</p>
+A  K^m  n is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,31 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(str, optional): one of <code>'reduced'</code>, <code>'complete'</code>, <code>'r'</code>.
 Controls the shape of the returned tensors. Default: <code>'reduced'</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list <code>(Q, R)</code>.</p>
+    <p>A list <code>(Q, R)</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times m}, R \in \mathbb{K}^{m \times n}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">QR</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">m</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where  is orthogonal in the real case and unitary in the complex case, and  is upper triangular.
+<p>where Q is orthogonal in the real case and unitary in the complex case, and R is upper triangular.
 When <code>m &gt; n</code> (tall matrix), as <code>R</code> is upper triangular, its last <code>m - n</code> rows are zero.
 In this case, we can drop the last <code>m - n</code> columns of <code>Q</code> to form the
 <strong>reduced QR decomposition</strong>:</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>Q</mi><mi>R</mi><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup><mo separator="true">,</mo><mi>R</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = QR\mathrlap{\qquad Q \in \mathbb{K}^{m \times n}, R \in \mathbb{K}^{n \times n}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">QR</span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0158em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.00773em;">R</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
 <p>The reduced QR decomposition agrees with the full QR decomposition when <code>n &gt;= m</code> (wide matrix).
@@ -139,17 +121,17 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -158,10 +140,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_norm.html">linalg_norm</a>()</code>,
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -177,13 +159,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">qr</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span>, <span class="va">qr</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span><span class="op">$</span><span class="fu">round</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1 -0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0  1  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -192,14 +174,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/linalg_slogdet.html b/reference/linalg_slogdet.html
index 6a3f6f9bf1..49526d7b07 100644
--- a/reference/linalg_slogdet.html
+++ b/reference/linalg_slogdet.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="For complex A, it returns the angle and the natural logarithm of the modulus of the
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 determinant, that is, a logarithmic polar decomposition of the determinant.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if A is a batch of matrices then
-the output has the same batch dimensions."><title>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet"><meta property="og:description" content="For complex A, it returns the angle and the natural logarithm of the modulus of the
+the output has the same batch dimensions."><meta property="og:description" content="For complex A, it returns the angle and the natural logarithm of the modulus of the
 determinant, that is, a logarithmic polar decomposition of the determinant.
 Supports input of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if A is a batch of matrices then
-the output has the same batch dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -18,77 +15,62 @@
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_slogdet.Rd</code></div>
     </div>
 
@@ -107,38 +89,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list <code>(sign, logabsdet)</code>.
+    <p>A list <code>(sign, logabsdet)</code>.
 <code>logabsdet</code> will always be real-valued, even when <code>A</code> is complex.
 <code>sign</code> will have the same dtype as <code>A</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="notes">Notes<a class="anchor" aria-label="anchor" href="#notes"></a></h2>
-    
+
 
 <ul><li><p>The determinant can be recovered as <code>sign * exp(logabsdet)</code>.</p></li>
 <li><p>When a matrix has a determinant of zero, it returns <code>(0, -Inf)</code>.</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -147,10 +129,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_norm.html">linalg_norm</a>()</code>,
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -169,12 +151,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.643368</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.649284</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -183,14 +165,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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index ba3ef19263..07b2e42aab 100644
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+++ b/reference/linalg_solve.html
@@ -1,12 +1,9 @@
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-this function computes the solution  of the linear system associated to
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the solution of a square system of linear equations with a unique solution. — linalg_solve • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the solution of a square system of linear equations with a unique solution. — linalg_solve"><meta name="description" content="Letting K be R or C,
+this function computes the solution X  K^n  k of the linear system associated to
+A  K^n  n, B  K^m  k, which is defined as"><meta property="og:description" content="Letting K be R or C,
+this function computes the solution X  K^n  k of the linear system associated to
+A  K^n  n, B  K^m  k, which is defined as"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the solution of a square system of linear equations with a unique solution.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the solution of a square system of linear equations with a unique solution.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_solve.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
-this function computes the solution  of the <strong>linear system</strong> associated to
-, which is defined as</p>
+    <p>Letting K be R or C,
+this function computes the solution X  K^n  k of the <strong>linear system</strong> associated to
+A  K^n  n, B  K^m  k, which is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, n, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>B</dt>
+<dt id="arg-b">B<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>(Tensor): right-hand side tensor of shape <code>(*, n)</code> or  <code>(*, n, k)</code> or <code>(n,)</code> or <code>(n, k)</code>
 according to the rules described above</p></dd>
 
@@ -115,8 +99,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <p>$$
   AX = B
 $$</p>
-<p>This system of linear equations has one solution if and only if  is <code>invertible</code>_.
-This function assumes that  is invertible.
+<p>This system of linear equations has one solution if and only if A is <code>invertible</code>_.
+This function assumes that A is invertible.
 Supports inputs of float, double, cfloat and cdouble dtypes.
 Also supports batches of matrices, and if the inputs are batches of matrices then
 the output has the same batch dimensions.</p>
@@ -134,17 +118,17 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -155,8 +139,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
 <code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
@@ -170,10 +154,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu">linalg_solve</span><span class="op">(</span><span class="va">A</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">x</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -182,14 +166,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_solve_triangular.html b/reference/linalg_solve_triangular.html
index a185892472..a157ed0ed3 100644
--- a/reference/linalg_solve_triangular.html
+++ b/reference/linalg_solve_triangular.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Triangular solve</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Triangular solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_solve_triangular.Rd</code></div>
     </div>
 
@@ -95,43 +77,45 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>tensor of shape <code>(*, n, n)</code> or <code>(*, k, k)</code> if <code>left=TRUE</code>) where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>B</dt>
+<dt id="arg-b">B<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>right-hand side tensor of shape <code>(*, n, k)</code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Currently ignored.</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>whether A is an upper or lower triangular matrix.</p></dd>
 
 
-<dt>left</dt>
+<dt id="arg-left">left<a class="anchor" aria-label="anchor" href="#arg-left"></a></dt>
 <dd><p>wheter to solve the system AX=B or XA=B</p></dd>
 
 
-<dt>unitriangular</dt>
+<dt id="arg-unitriangular">unitriangular<a class="anchor" aria-label="anchor" href="#arg-unitriangular"></a></dt>
 <dd><p>if <code>TRUE</code>, the diagonal elements of A are assumed to be all equal to 1.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -142,14 +126,14 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +142,14 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_svd.html b/reference/linalg_svd.html
index 639e8ad6c5..43cc6f7c08 100644
--- a/reference/linalg_svd.html
+++ b/reference/linalg_svd.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
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 the full SVD of a matrix
-, if k = min(m,n), is defined as"><title>Computes the singular value decomposition (SVD) of a matrix. — linalg_svd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the singular value decomposition (SVD) of a matrix. — linalg_svd"><meta property="og:description" content="Letting  be  or ,
+A  K^m  n, if k = min(m,n), is defined as"><meta property="og:description" content="Letting K be R or C,
 the full SVD of a matrix
-, if k = min(m,n), is defined as"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Computes the singular value decomposition (SVD) of a matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the singular value decomposition (SVD) of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_svd.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Letting  be  or ,
+    <p>Letting K be R or C,
 the <strong>full SVD</strong> of a matrix
-, if <code>k = min(m,n)</code>, is defined as</p>
+A  K^m  n, if <code>k = min(m,n)</code>, is defined as</p>
     </div>
 
     <div class="section level2">
@@ -101,20 +83,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 
-<dt>full_matrices</dt>
+<dt id="arg-full-matrices">full_matrices<a class="anchor" aria-label="anchor" href="#arg-full-matrices"></a></dt>
 <dd><p>(bool, optional): controls whether to compute the full or reduced
 SVD, and consequently, the shape of the returned tensors <code>U</code> and <code>V</code>. Default: <code>TRUE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A list <code>(U, S, V)</code> which corresponds to , ,  above.
+    <p>A list <code>(U, S, V)</code> which corresponds to U, S, V^H above.
 <code>S</code> will always be real-valued, even when <code>A</code> is complex.
 It will also be ordered in descending order.
 <code>U</code> and <code>V</code> will have the same dtype as <code>A</code>. The left / right singular vectors will be given by
@@ -122,23 +104,23 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+    <p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>m</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = U \operatorname{diag}(S) V^{H} \mathrlap{\qquad U \in \mathbb{K}^{m \times m}, S \in \mathbb{R}^k, V \in \mathbb{K}^{n \times n}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1491em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0935em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">m</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8213em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">n</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where ,
- is the conjugate transpose when  is complex, and the transpose when  is real-valued.</p>
-<p>The matrices  ,  (and thus ) are orthogonal in the real case, and unitary in the complex case.
+<p>where diag(S)  K^m  n,
+V^H is the conjugate transpose when V is complex, and the transpose when V is real-valued.</p>
+<p>The matrices  U, V (and thus V^H) are orthogonal in the real case, and unitary in the complex case.
 When <code>m &gt; n</code> (resp. <code>m &lt; n</code>) we can drop the last <code>m - n</code> (resp. <code>n - m</code>) columns of <code>U</code> (resp. <code>V</code>) to form the <strong>reduced SVD</strong>:</p>
-<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.3/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>k</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>k</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
+<p><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/katex@0.16.9/dist/katex.min.css" data-external="1"><span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>A</mi><mo>=</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mi>H</mi></msup><mpadded width="0px"><mrow><mspace width="2em"></mspace><mi>U</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>k</mi></mrow></msup><mo separator="true">,</mo><mi>S</mi><mo>∈</mo><msup><mi mathvariant="double-struck">R</mi><mi>k</mi></msup><mo separator="true">,</mo><mi>V</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>k</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow></mpadded></mrow><annotation encoding="application/x-tex">
 A = U \operatorname{diag}(S) V^{H} \mathrlap{\qquad U \in \mathbb{K}^{m \times k}, S \in \mathbb{R}^k, V \in \mathbb{K}^{k \times n}}
 </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.1491em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8913em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.08125em;">H</span></span></span></span></span></span></span></span></span><span class="mord vbox"><span class="thinbox"><span class="rlap"><span class="strut" style="height:1.0935em;vertical-align:-0.1944em;"></span><span class="inner"><span class="mord"><span class="mspace" style="margin-right:2em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">R</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8991em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span><span class="fix"></span></span></span></span></span></span></span></span></p>
-<p>where .</p>
-<p>In this case,  and  also have orthonormal columns.
+<p>where diag(S)  K^k  k.</p>
+<p>In this case, U and V also have orthonormal columns.
 Supports input of float, double, cfloat and cdouble dtypes.</p>
 <p>Also supports batches of matrices, and if <code>A</code> is a batch of matrices then
 the output has the same batch dimensions.</p>
 <p>The returned decomposition is a named tuple <code>(U, S, V)</code>
-which corresponds to , ,  above.</p>
+which corresponds to U, S, V^H above.</p>
 <p>The singular values are returned in descending order.
 The parameter <code>full_matrices</code> chooses between the full (default) and reduced SVD.</p>
     </div>
@@ -150,15 +132,15 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="warnings">Warnings<a class="anchor" aria-label="anchor" href="#warnings"></a></h2>
-    
+
 
 <p>The returned tensors <code>U</code> and <code>V</code> are not unique, nor are they continuous with
 respect to <code>A</code>.
 Due to this lack of uniqueness, different hardware and software may compute
 different singular vectors.
 This non-uniqueness is caused by the fact that multiplying any pair of singular
-vectors  by <code>-1</code> in the real case or by
- in the complex case produces another two
+vectors u_k, v_k by <code>-1</code> in the real case or by
+e^i ,   R in the complex case produces another two
 valid singular vectors of the matrix.
 This non-uniqueness problem is even worse when the matrix has repeated singular values.
 In this case, one may multiply the associated singular vectors of <code>U</code> and <code>V</code> spanning
@@ -167,10 +149,10 @@ <h2 id="warnings">Warnings<a class="anchor" aria-label="anchor" href="#warnings"
 <code>A</code> does not have zero as a singular value or repeated singular values.
 Furthermore, if the distance between any two singular values is close to zero,
 the gradient will be numerically unstable, as it depends on the singular values
- through the computation of
-.
+_i through the computation of
+1_i  j _i^2 - _j^2.
 The gradient will also be numerically unstable when <code>A</code> has small singular
-values, as it also depends on the computaiton of .</p>
+values, as it also depends on the computaiton of 1_i.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -184,17 +166,17 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 for Hermitian and symmetric matrices.</p></li>
 <li><p><code><a href="linalg_qr.html">linalg_qr()</a></code> for another (much faster) decomposition that works on general
 matrices.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -204,8 +186,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
@@ -221,30 +203,30 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0445  0.3368 -0.7746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8789 -0.1809  0.1178</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0135  0.8168  0.1631</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4666  0.3359 -0.0405</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0876  0.2717  0.5983</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6009  0.1289  0.0304</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0997 -0.1008  0.6480</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4880  0.0750 -0.6420</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6010 -0.4268  0.3059</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1721  0.8863  0.2709</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,3} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.7608</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9651</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9611</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.0966</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.3732</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8830</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[3]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0630 -0.9964  0.0561</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9145 -0.0351  0.4031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3997 -0.0767 -0.9134</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0631 -0.9846 -0.1630</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2267 -0.1449  0.9631</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9719  0.0977 -0.2141</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
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diff --git a/reference/linalg_svdvals.html b/reference/linalg_svdvals.html
index ac04adc4a5..8749623f85 100644
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+++ b/reference/linalg_svdvals.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
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+The singular values are returned in descending order."><meta property="og:description" content="Supports input of float, double, cfloat and cdouble dtypes.
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 the output has the same batch dimensions.
-The singular values are returned in descending order."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Computes the singular values of a matrix.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the singular values of a matrix.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_svdvals.Rd</code></div>
     </div>
 
@@ -104,30 +86,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor of shape <code>(*, m, n)</code> where <code>*</code> is zero or more batch dimensions.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A real-valued tensor, even when <code>A</code> is complex.</p>
+    <p>A real-valued tensor, even when <code>A</code> is complex.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p><code><a href="linalg_svd.html">linalg_svd()</a></code> computes the full singular value decomposition.</p>
-<p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+<p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -137,8 +119,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
@@ -153,13 +135,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">S</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3.1020</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3424</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9048</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.8025</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.6571</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4364</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -168,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/linalg_tensorinv.html b/reference/linalg_tensorinv.html
index d112df9518..5ab7b716cf 100644
--- a/reference/linalg_tensorinv.html
+++ b/reference/linalg_tensorinv.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If m is the product of the first ind dimensions of A and n is the product of
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv"><meta name="description" content="If m is the product of the first ind dimensions of A and n is the product of
 the rest of the dimensions, this function expects m and n to be equal.
 If this is the case, it computes a tensor X such that
-tensordot(A, X, ind) is the identity matrix in dimension m."><title>Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv"><meta property="og:description" content="If m is the product of the first ind dimensions of A and n is the product of
+tensordot(A, X, ind) is the identity matrix in dimension m."><meta property="og:description" content="If m is the product of the first ind dimensions of A and n is the product of
 the rest of the dimensions, this function expects m and n to be equal.
 If this is the case, it computes a tensor X such that
-tensordot(A, X, ind) is the identity matrix in dimension m."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Computes the multiplicative inverse of <code><a href="torch_tensordot.html">torch_tensordot()</a></code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the multiplicative inverse of <code><a href="torch_tensordot.html">torch_tensordot()</a></code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_tensorinv.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor to invert.</p></dd>
 
 
-<dt>ind</dt>
+<dt id="arg-ind">ind<a class="anchor" aria-label="anchor" href="#arg-ind"></a></dt>
 <dd><p>(int): index at which to compute the inverse of <code><a href="torch_tensordot.html">torch_tensordot()</a></code>. Default: <code>3</code>.</p></dd>
 
 </dl></div>
@@ -127,17 +111,17 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index">
 <ul><li><p><code><a href="linalg_tensorsolve.html">linalg_tensorsolve()</a></code> computes <code>torch_tensordot(linalg_tensorinv(A), B))</code>.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -147,10 +131,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
     </div>
@@ -172,7 +156,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -181,14 +165,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/linalg_tensorsolve.html b/reference/linalg_tensorsolve.html
index 1b57a190cf..9f06c9a3f6 100644
--- a/reference/linalg_tensorsolve.html
+++ b/reference/linalg_tensorsolve.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If m is the product of the first B\ .ndim  dimensions of A and
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve"><meta name="description" content="If m is the product of the first B\ .ndim  dimensions of A and
 n is the product of the rest of the dimensions, this function expects m and n to be equal.
 The returned tensor x satisfies
-tensordot(A, x, dims=x$ndim) == B."><title>Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve"><meta property="og:description" content="If m is the product of the first B\ .ndim  dimensions of A and
+tensordot(A, x, dims=x$ndim) == B."><meta property="og:description" content="If m is the product of the first B\ .ndim  dimensions of A and
 n is the product of the rest of the dimensions, this function expects m and n to be equal.
 The returned tensor x satisfies
-tensordot(A, x, dims=x$ndim) == B."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes the solution <code>X</code> to the system <code>torch_tensordot(A, X) = B</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_tensorsolve.Rd</code></div>
     </div>
 
@@ -104,16 +86,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor to solve for.</p></dd>
 
 
-<dt>B</dt>
+<dt id="arg-b">B<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>(Tensor): the solution</p></dd>
 
 
-<dt>dims</dt>
-<dd><p>(Tupleint, optional): dimensions of <code>A</code> to be moved.
+<dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
+<dd><p>(<code>Tuple[int]</code>, optional): dimensions of <code>A</code> to be moved.
 If <code>NULL</code>, no dimensions are moved. Default: <code>NULL</code>.</p></dd>
 
 </dl></div>
@@ -128,17 +112,17 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index">
 <ul><li><p><code><a href="linalg_tensorinv.html">linalg_tensorinv()</a></code> computes the multiplicative inverse of
 <code><a href="torch_tensordot.html">torch_tensordot()</a></code>.</p></li>
-</ul><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+</ul><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -148,10 +132,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_vector_norm.html">linalg_vector_norm</a>()</code></p></div>
     </div>
@@ -171,10 +155,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">A</span> <span class="op">&lt;-</span> <span class="va">A</span><span class="op">$</span><span class="fu">permute</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">1</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="torch_allclose.html">torch_allclose</a></span><span class="op">(</span><span class="fu"><a href="torch_tensordot.html">torch_tensordot</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">X</span>, dims <span class="op">=</span> <span class="va">X</span><span class="op">$</span><span class="va">ndim</span><span class="op">)</span>, <span class="va">B</span>, atol <span class="op">=</span> <span class="fl">1e-6</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -183,14 +167,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/linalg_vector_norm.html b/reference/linalg_vector_norm.html
index f0af2aa211..f7beafb440 100644
--- a/reference/linalg_vector_norm.html
+++ b/reference/linalg_vector_norm.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="If A is complex valued, it computes the norm of A$abs()
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes a vector norm. — linalg_vector_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a vector norm. — linalg_vector_norm"><meta name="description" content="If A is complex valued, it computes the norm of A$abs()
 Supports input of float, double, cfloat and cdouble dtypes.
 This function does not necessarily treat multidimensonal A as a batch of
-vectors, instead:"><title>Computes a vector norm. — linalg_vector_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a vector norm. — linalg_vector_norm"><meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
+vectors, instead:"><meta property="og:description" content="If A is complex valued, it computes the norm of A$abs()
 Supports input of float, double, cfloat and cdouble dtypes.
 This function does not necessarily treat multidimensonal A as a batch of
-vectors, instead:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes a vector norm.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
+
+      <h1>Computes a vector norm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/linalg.R" class="external-link"><code>R/linalg.R</code></a></small>
       <div class="d-none name"><code>linalg_vector_norm.Rd</code></div>
     </div>
 
@@ -104,27 +86,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor): tensor, flattened by default, but this behavior can be
 controlled using <code>dim</code>.</p></dd>
 
 
-<dt>ord</dt>
+<dt id="arg-ord">ord<a class="anchor" aria-label="anchor" href="#arg-ord"></a></dt>
 <dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional): order of norm. Default: <code>2</code></p></dd>
 
 
-<dt>dim</dt>
-<dd><p>(int, Tupleint, optional): dimensions over which to compute
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
+<dd><p>(int, <code>Tuple[int]</code>, optional): dimensions over which to compute
 the vector or matrix norm. See above for the behavior when <code>dim=NULL</code>.
 Default: <code>NULL</code></p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool, optional): If set to <code>TRUE</code>, the reduced dimensions are retained
 in the result as dimensions with size one. Default: <code>FALSE</code></p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>dtype (<code>torch_dtype</code>, optional): If specified, the input tensor is cast to
 <code>dtype</code> before performing the operation, and the returned tensor's type
 will be <code>dtype</code>. Default: <code>NULL</code></p></dd>
@@ -132,7 +116,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    
+
 <ul><li><p>If <code>dim=NULL</code>, <code>A</code> will be flattened before the norm is computed.</p></li>
 <li><p>If <code>dim</code> is an <code>int</code> or a <code>tuple</code>, the norm will be computed over these dimensions
 and the other dimensions will be treated as batch dimensions.</p></li>
@@ -141,17 +125,17 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 supported:</p><table class="table table"><tr><td><code>ord</code></td><td>norm for matrices</td><td>norm for vectors</td></tr><tr><td><code>NULL</code> (default)</td><td>Frobenius norm</td><td><code>2</code>-norm (see below)</td></tr><tr><td><code>"fro"</code></td><td>Frobenius norm</td><td>– not supported –</td></tr><tr><td><code>"nuc"</code></td><td>nuclear norm</td><td>– not supported –</td></tr><tr><td><code>Inf</code></td><td><code>max(sum(abs(x), dim=2))</code></td><td><code>max(abs(x))</code></td></tr><tr><td><code>-Inf</code></td><td><code>min(sum(abs(x), dim=2))</code></td><td><code>min(abs(x))</code></td></tr><tr><td><code>0</code></td><td>– not supported –</td><td><code>sum(x != 0)</code></td></tr><tr><td><code>1</code></td><td><code>max(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>-1</code></td><td><code>min(sum(abs(x), dim=1))</code></td><td>as below</td></tr><tr><td><code>2</code></td><td>largest singular value</td><td>as below</td></tr><tr><td><code>-2</code></td><td>smallest singular value</td><td>as below</td></tr><tr><td>other <code>int</code> or <code>float</code></td><td>– not supported –</td><td><code>sum(abs(x)^{ord})^{(1 / ord)}</code></td></tr></table></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other linalg: 
-<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
+    <div class="dont-index"><p>Other linalg:
 <code><a href="linalg_cholesky.html">linalg_cholesky</a>()</code>,
+<code><a href="linalg_cholesky_ex.html">linalg_cholesky_ex</a>()</code>,
 <code><a href="linalg_det.html">linalg_det</a>()</code>,
+<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
 <code><a href="linalg_eigh.html">linalg_eigh</a>()</code>,
-<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_eigvals.html">linalg_eigvals</a>()</code>,
-<code><a href="linalg_eig.html">linalg_eig</a>()</code>,
+<code><a href="linalg_eigvalsh.html">linalg_eigvalsh</a>()</code>,
 <code><a href="linalg_householder_product.html">linalg_householder_product</a>()</code>,
-<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_inv.html">linalg_inv</a>()</code>,
+<code><a href="linalg_inv_ex.html">linalg_inv_ex</a>()</code>,
 <code><a href="linalg_lstsq.html">linalg_lstsq</a>()</code>,
 <code><a href="linalg_matrix_norm.html">linalg_matrix_norm</a>()</code>,
 <code><a href="linalg_matrix_power.html">linalg_matrix_power</a>()</code>,
@@ -161,10 +145,10 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="linalg_pinv.html">linalg_pinv</a>()</code>,
 <code><a href="linalg_qr.html">linalg_qr</a>()</code>,
 <code><a href="linalg_slogdet.html">linalg_slogdet</a>()</code>,
-<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_solve.html">linalg_solve</a>()</code>,
-<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
+<code><a href="linalg_solve_triangular.html">linalg_solve_triangular</a>()</code>,
 <code><a href="linalg_svd.html">linalg_svd</a>()</code>,
+<code><a href="linalg_svdvals.html">linalg_svdvals</a>()</code>,
 <code><a href="linalg_tensorinv.html">linalg_tensorinv</a>()</code>,
 <code><a href="linalg_tensorsolve.html">linalg_tensorsolve</a>()</code></p></div>
     </div>
@@ -185,7 +169,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -194,14 +178,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/load_state_dict.html b/reference/load_state_dict.html
index bb3d139825..64e46bfcf1 100644
--- a/reference/load_state_dict.html
+++ b/reference/load_state_dict.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function should only be used to load models saved in python.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Load a state dict file — load_state_dict • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Load a state dict file — load_state_dict"><meta name="description" content="This function should only be used to load models saved in python.
 For it to work correctly you need to use torch.save with the flag:
 _use_new_zipfile_serialization=True and also remove all nn.Parameter
-classes from the tensors in the dict."><title>Load a state dict file — load_state_dict • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Load a state dict file — load_state_dict"><meta property="og:description" content="This function should only be used to load models saved in python.
+classes from the tensors in the dict."><meta property="og:description" content="This function should only be used to load models saved in python.
 For it to work correctly you need to use torch.save with the flag:
 _use_new_zipfile_serialization=True and also remove all nn.Parameter
-classes from the tensors in the dict."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Load a state dict file</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+
+      <h1>Load a state dict file</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/save.R" class="external-link"><code>R/save.R</code></a></small>
       <div class="d-none name"><code>load_state_dict.Rd</code></div>
     </div>
 
@@ -104,24 +86,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>path</dt>
+
+
+<dl><dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>to the state dict file</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>additional arguments that are currently not used.</p></dd>
 
 
-<dt>legacy_stream</dt>
+<dt id="arg-legacy-stream">legacy_stream<a class="anchor" aria-label="anchor" href="#arg-legacy-stream"></a></dt>
 <dd><p>if <code>TRUE</code> then the state dict is loaded using a
 a legacy way of handling streams.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a named list of tensors.</p>
+    <p>a named list of tensors.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -129,7 +111,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 in pytorch's C++ api.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +120,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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+
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diff --git a/reference/local_autocast.html b/reference/local_autocast.html
index b1094936b8..009106ace6 100644
--- a/reference/local_autocast.html
+++ b/reference/local_autocast.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Allow regions of your code to run in mixed precision.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Autocast context manager — local_autocast • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Autocast context manager — local_autocast"><meta name="description" content="Allow regions of your code to run in mixed precision.
 In these regions, ops run in an op-specific dtype chosen by autocast
-to improve performance while maintaining accuracy."><title>Autocast context manager — local_autocast • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Autocast context manager — local_autocast"><meta property="og:description" content="Allow regions of your code to run in mixed precision.
+to improve performance while maintaining accuracy."><meta property="og:description" content="Allow regions of your code to run in mixed precision.
 In these regions, ops run in an op-specific dtype chosen by autocast
-to improve performance while maintaining accuracy."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -14,77 +11,62 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Autocast context manager</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autocast.R" class="external-link"><code>R/autocast.R</code></a></small>
+
+      <h1>Autocast context manager</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autocast.R" class="external-link"><code>R/autocast.R</code></a></small>
       <div class="d-none name"><code>local_autocast.Rd</code></div>
     </div>
 
@@ -121,35 +103,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>device_type</dt>
+
+
+<dl><dt id="arg-device-type">device_type<a class="anchor" aria-label="anchor" href="#arg-device-type"></a></dt>
 <dd><p>a character string indicating whether to use 'cuda' or 'cpu' device</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>a torch data type indicating whether to use <code><a href="torch_dtype.html">torch_float16()</a></code> or <code>torch_bfloat16()</code>.</p></dd>
 
 
-<dt>enabled</dt>
+<dt id="arg-enabled">enabled<a class="anchor" aria-label="anchor" href="#arg-enabled"></a></dt>
 <dd><p>a logical value indicating whether autocasting should be enabled in the region. Default: TRUE</p></dd>
 
 
-<dt>cache_enabled</dt>
+<dt id="arg-cache-enabled">cache_enabled<a class="anchor" aria-label="anchor" href="#arg-cache-enabled"></a></dt>
 <dd><p>a logical value indicating whether the weight cache inside autocast should be enabled.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused.</p></dd>
 
 
-<dt>.env</dt>
+<dt id="arg--env">.env<a class="anchor" aria-label="anchor" href="#arg--env"></a></dt>
 <dd><p>The environment to use for scoping.</p></dd>
 
 
-<dt>code</dt>
+<dt id="arg-code">code<a class="anchor" aria-label="anchor" href="#arg-code"></a></dt>
 <dd><p>code to be executed with no gradient recording.</p></dd>
 
 
-<dt>context</dt>
+<dt id="arg-context">context<a class="anchor" aria-label="anchor" href="#arg-context"></a></dt>
 <dd><p>Returned by <code>set_autocast</code> and should be passed when unsetting it.</p></dd>
 
 </dl></div>
@@ -165,7 +149,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>with_autocast()</code>: A with context for automatic mixed precision.</p></li>
 <li><p><code>set_autocast()</code>: Set the autocast context. For advanced users only.</p></li>
 <li><p><code>unset_autocast()</code>: Unset the autocast context.</p></li>
@@ -192,7 +176,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -201,14 +185,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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index 57bf4b9410..c446a263ea 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+
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@@ -97,29 +79,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-    <dl><dt>device</dt>
+
+
+<dl><dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>A torch device to be used by default when creating new tensors.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused.</p></dd>
 
 
-<dt>.env</dt>
+<dt id="arg--env">.env<a class="anchor" aria-label="anchor" href="#arg--env"></a></dt>
 <dd><p>The environment to use for scoping.</p></dd>
 
 
-<dt>code</dt>
+<dt id="arg-code">code<a class="anchor" aria-label="anchor" href="#arg-code"></a></dt>
 <dd><p>The code to be evaluated in the modified environment.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>with_device()</code>: Modifies the default device for the selected context.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -128,14 +112,14 @@ <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functio
 </div>
 
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000000..3228d5a6c0
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index 816bd36e43..48112699a6 100644
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-      <img src="" class="logo" alt=""><h1>Set the learning rate of each parameter group using a cosine annealing schedule</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Set the learning rate of each parameter group using a cosine annealing schedule</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_cosine_annealing.Rd</code></div>
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@@ -101,29 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>optimizer</dt>
+
+
+<dl><dt id="arg-optimizer">optimizer<a class="anchor" aria-label="anchor" href="#arg-optimizer"></a></dt>
 <dd><p>(Optimizer): Wrapped optimizer.</p></dd>
 
 
-<dt>T_max</dt>
+<dt id="arg-t-max">T_max<a class="anchor" aria-label="anchor" href="#arg-t-max"></a></dt>
 <dd><p>Maximum number of iterations</p></dd>
 
 
-<dt>eta_min</dt>
+<dt id="arg-eta-min">eta_min<a class="anchor" aria-label="anchor" href="#arg-eta-min"></a></dt>
 <dd><p>Minimum learning rate. Default: 0.</p></dd>
 
 
-<dt>last_epoch</dt>
+<dt id="arg-last-epoch">last_epoch<a class="anchor" aria-label="anchor" href="#arg-last-epoch"></a></dt>
 <dd><p>The index of the last epoch</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
 each update. Default: <code>FALSE</code>.</p></dd>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_lambda.Rd</code></div>
     </div>
 
@@ -98,21 +80,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>optimizer</dt>
+
+
+<dl><dt id="arg-optimizer">optimizer<a class="anchor" aria-label="anchor" href="#arg-optimizer"></a></dt>
 <dd><p>(Optimizer): Wrapped optimizer.</p></dd>
 
 
-<dt>lr_lambda</dt>
+<dt id="arg-lr-lambda">lr_lambda<a class="anchor" aria-label="anchor" href="#arg-lr-lambda"></a></dt>
 <dd><p>(function or list): A function which computes a multiplicative
 factor given an integer parameter epoch, or a list of such
 functions, one for each group in optimizer.param_groups.</p></dd>
 
 
-<dt>last_epoch</dt>
+<dt id="arg-last-epoch">last_epoch<a class="anchor" aria-label="anchor" href="#arg-last-epoch"></a></dt>
 <dd><p>(int): The index of last epoch. Default: -1.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
 each update. Default: <code>FALSE</code>.</p></dd>
 
@@ -124,19 +108,19 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># Assuming optimizer has two groups.</span></span></span>
 <span class="r-in"><span><span class="va">lambda1</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">epoch</span><span class="op">)</span> <span class="va">epoch</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%/%</a></span> <span class="fl">30</span></span></span>
 <span class="r-in"><span><span class="va">lambda2</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">epoch</span><span class="op">)</span> <span class="fl">0.95</span><span class="op">^</span><span class="va">epoch</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_lambda</span><span class="op">(</span><span class="va">optimizer</span>, lr_lambda <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">lambda1</span>, <span class="va">lambda2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="fu">train</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="fu">validate</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/lr_multiplicative.html b/reference/lr_multiplicative.html
index 0c03839714..a5069dc137 100644
--- a/reference/lr_multiplicative.html
+++ b/reference/lr_multiplicative.html
@@ -1,10 +1,7 @@
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-in the specified function. When last_epoch=-1, sets initial lr as lr."><title>Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative"><meta property="og:description" content="Multiply the learning rate of each parameter group by the factor given
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+in the specified function. When last_epoch=-1, sets initial lr as lr."><meta property="og:description" content="Multiply the learning rate of each parameter group by the factor given
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_multiplicative.Rd</code></div>
     </div>
 
@@ -98,21 +80,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>optimizer</dt>
+
+
+<dl><dt id="arg-optimizer">optimizer<a class="anchor" aria-label="anchor" href="#arg-optimizer"></a></dt>
 <dd><p>(Optimizer): Wrapped optimizer.</p></dd>
 
 
-<dt>lr_lambda</dt>
+<dt id="arg-lr-lambda">lr_lambda<a class="anchor" aria-label="anchor" href="#arg-lr-lambda"></a></dt>
 <dd><p>(function or list): A function which computes a multiplicative
 factor given an integer parameter epoch, or a list of such
 functions, one for each group in optimizer.param_groups.</p></dd>
 
 
-<dt>last_epoch</dt>
+<dt id="arg-last-epoch">last_epoch<a class="anchor" aria-label="anchor" href="#arg-last-epoch"></a></dt>
 <dd><p>(int): The index of last epoch. Default: -1.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
 each update. Default: <code>FALSE</code>.</p></dd>
 
@@ -121,7 +105,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">lmbda</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">epoch</span><span class="op">)</span> <span class="fl">0.95</span></span></span>
 <span class="r-in"><span><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_multiplicative</span><span class="op">(</span><span class="va">optimizer</span>, lr_lambda <span class="op">=</span> <span class="va">lmbda</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">100</span><span class="op">)</span> <span class="op">{</span></span></span>
@@ -129,12 +113,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="fu">validate</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
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diff --git a/reference/lr_one_cycle.html b/reference/lr_one_cycle.html
index 5e5363c59c..411862a4ee 100644
--- a/reference/lr_one_cycle.html
+++ b/reference/lr_one_cycle.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Sets the learning rate of each parameter group according to the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Once cycle learning rate — lr_one_cycle • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Once cycle learning rate — lr_one_cycle"><meta name="description" content="Sets the learning rate of each parameter group according to the
 1cycle learning rate policy. The 1cycle policy anneals the learning
 rate from an initial learning rate to some maximum learning rate and then
 from that maximum learning rate to some minimum learning rate much lower
-than the initial learning rate."><title>Once cycle learning rate — lr_one_cycle • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Once cycle learning rate — lr_one_cycle"><meta property="og:description" content="Sets the learning rate of each parameter group according to the
+than the initial learning rate."><meta property="og:description" content="Sets the learning rate of each parameter group according to the
 1cycle learning rate policy. The 1cycle policy anneals the learning
 rate from an initial learning rate to some maximum learning rate and then
 from that maximum learning rate to some minimum learning rate much lower
-than the initial learning rate."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Once cycle learning rate</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Once cycle learning rate</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_one_cycle.Rd</code></div>
     </div>
 
@@ -122,56 +104,58 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>optimizer</dt>
+
+
+<dl><dt id="arg-optimizer">optimizer<a class="anchor" aria-label="anchor" href="#arg-optimizer"></a></dt>
 <dd><p>(Optimizer): Wrapped optimizer.</p></dd>
 
 
-<dt>max_lr</dt>
+<dt id="arg-max-lr">max_lr<a class="anchor" aria-label="anchor" href="#arg-max-lr"></a></dt>
 <dd><p>(float or list): Upper learning rate boundaries in the cycle
 for each parameter group.</p></dd>
 
 
-<dt>total_steps</dt>
+<dt id="arg-total-steps">total_steps<a class="anchor" aria-label="anchor" href="#arg-total-steps"></a></dt>
 <dd><p>(int): The total number of steps in the cycle. Note that
 if a value is not provided here, then it must be inferred by providing
 a value for epochs and steps_per_epoch.
 Default: NULL</p></dd>
 
 
-<dt>epochs</dt>
+<dt id="arg-epochs">epochs<a class="anchor" aria-label="anchor" href="#arg-epochs"></a></dt>
 <dd><p>(int): The number of epochs to train for. This is used along
 with steps_per_epoch in order to infer the total number of steps in the cycle
 if a value for total_steps is not provided.
 Default: NULL</p></dd>
 
 
-<dt>steps_per_epoch</dt>
+<dt id="arg-steps-per-epoch">steps_per_epoch<a class="anchor" aria-label="anchor" href="#arg-steps-per-epoch"></a></dt>
 <dd><p>(int): The number of steps per epoch to train for. This is
 used along with epochs in order to infer the total number of steps in the
 cycle if a value for total_steps is not provided.
 Default: NULL</p></dd>
 
 
-<dt>pct_start</dt>
+<dt id="arg-pct-start">pct_start<a class="anchor" aria-label="anchor" href="#arg-pct-start"></a></dt>
 <dd><p>(float): The percentage of the cycle (in number of steps) spent
 increasing the learning rate.
 Default: 0.3</p></dd>
 
 
-<dt>anneal_strategy</dt>
+<dt id="arg-anneal-strategy">anneal_strategy<a class="anchor" aria-label="anchor" href="#arg-anneal-strategy"></a></dt>
 <dd><p>(str): {'cos', 'linear'}
 Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
 linear annealing.
 Default: 'cos'</p></dd>
 
 
-<dt>cycle_momentum</dt>
+<dt id="arg-cycle-momentum">cycle_momentum<a class="anchor" aria-label="anchor" href="#arg-cycle-momentum"></a></dt>
 <dd><p>(bool): If <code>TRUE</code>, momentum is cycled inversely
 to learning rate between 'base_momentum' and 'max_momentum'.
 Default: TRUE</p></dd>
 
 
-<dt>base_momentum</dt>
+<dt id="arg-base-momentum">base_momentum<a class="anchor" aria-label="anchor" href="#arg-base-momentum"></a></dt>
 <dd><p>(float or list): Lower momentum boundaries in the cycle
 for each parameter group. Note that momentum is cycled inversely
 to learning rate; at the peak of a cycle, momentum is
@@ -179,7 +163,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 Default: 0.85</p></dd>
 
 
-<dt>max_momentum</dt>
+<dt id="arg-max-momentum">max_momentum<a class="anchor" aria-label="anchor" href="#arg-max-momentum"></a></dt>
 <dd><p>(float or list): Upper momentum boundaries in the cycle
 for each parameter group. Functionally,
 it defines the cycle amplitude (max_momentum - base_momentum).
@@ -189,19 +173,19 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 Default: 0.95</p></dd>
 
 
-<dt>div_factor</dt>
+<dt id="arg-div-factor">div_factor<a class="anchor" aria-label="anchor" href="#arg-div-factor"></a></dt>
 <dd><p>(float): Determines the initial learning rate via
 initial_lr = max_lr/div_factor
 Default: 25</p></dd>
 
 
-<dt>final_div_factor</dt>
+<dt id="arg-final-div-factor">final_div_factor<a class="anchor" aria-label="anchor" href="#arg-final-div-factor"></a></dt>
 <dd><p>(float): Determines the minimum learning rate via
 min_lr = initial_lr/final_div_factor
 Default: 1e4</p></dd>
 
 
-<dt>last_epoch</dt>
+<dt id="arg-last-epoch">last_epoch<a class="anchor" aria-label="anchor" href="#arg-last-epoch"></a></dt>
 <dd><p>(int): The index of the last batch. This parameter is used when
 resuming a training job. Since <code><a href="https://rdrr.io/r/stats/step.html" class="external-link">step()</a></code> should be invoked after each
 batch instead of after each epoch, this number represents the total
@@ -210,7 +194,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 Default: -1</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
 each update. Default: <code>FALSE</code>.</p></dd>
 
@@ -235,7 +219,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">data_loader</span> <span class="op">&lt;-</span> <span class="fu"><a href="dataloader.html">dataloader</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.1</span>, momentum <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_one_cycle</span><span class="op">(</span><span class="va">optimizer</span>,</span></span>
@@ -244,17 +228,17 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="va">epochs</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span>  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">batch</span> <span class="kw">in</span> <span class="va">data_loader</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span>  <span class="fu">coro</span><span class="fu">::</span><span class="fu"><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">loop</a></span><span class="op">(</span><span class="kw">for</span> <span class="op">(</span><span class="va">batch</span> <span class="kw">in</span> <span class="va">data_loader</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>    <span class="fu">train_batch</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></span>
 <span class="r-in"><span>    <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="op">}</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -263,14 +247,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/lr_reduce_on_plateau.html b/reference/lr_reduce_on_plateau.html
index 7c02c1bca1..1ad336e8fe 100644
--- a/reference/lr_reduce_on_plateau.html
+++ b/reference/lr_reduce_on_plateau.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Reduce learning rate when a metric has stopped improving.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Reduce learning rate on plateau — lr_reduce_on_plateau • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Reduce learning rate on plateau — lr_reduce_on_plateau"><meta name="description" content="Reduce learning rate when a metric has stopped improving.
 Models often benefit from reducing the learning rate by a factor
 of 2-10 once learning stagnates. This scheduler reads a metrics
 quantity and if no improvement is seen for a 'patience' number
-of epochs, the learning rate is reduced."><title>Reduce learning rate on plateau — lr_reduce_on_plateau • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Reduce learning rate on plateau — lr_reduce_on_plateau"><meta property="og:description" content="Reduce learning rate when a metric has stopped improving.
+of epochs, the learning rate is reduced."><meta property="og:description" content="Reduce learning rate when a metric has stopped improving.
 Models often benefit from reducing the learning rate by a factor
 of 2-10 once learning stagnates. This scheduler reads a metrics
 quantity and if no improvement is seen for a 'patience' number
-of epochs, the learning rate is reduced."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Reduce learning rate on plateau</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Reduce learning rate on plateau</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_reduce_on_plateau.Rd</code></div>
     </div>
 
@@ -118,34 +100,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>optimizer</dt>
+
+
+<dl><dt id="arg-optimizer">optimizer<a class="anchor" aria-label="anchor" href="#arg-optimizer"></a></dt>
 <dd><p>(Optimizer): Wrapped optimizer.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(str): One of <code>min</code>, <code>max</code>. In <code>min</code> mode, lr will be reduced
 when the quantity monitored has stopped decreasing; in <code>max</code> mode it will be
 reduced when the quantity monitored has stopped increasing. Default: 'min'.</p></dd>
 
 
-<dt>factor</dt>
+<dt id="arg-factor">factor<a class="anchor" aria-label="anchor" href="#arg-factor"></a></dt>
 <dd><p>(float): Factor by which the learning rate will be reduced.
 new_lr &lt;- lr * factor. Default: 0.1.</p></dd>
 
 
-<dt>patience</dt>
+<dt id="arg-patience">patience<a class="anchor" aria-label="anchor" href="#arg-patience"></a></dt>
 <dd><p>(int): Number of epochs with no improvement after which
 learning rate will be reduced. For example, if <code>patience = 2</code>, then we will
 ignore the first 2 epochs with no improvement, and will only decrease the LR
 after the 3rd epoch if the loss still hasn't improved then. Default: 10.</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(float):Threshold for measuring the new optimum, to only
 focus on significant changes. Default: 1e-4.</p></dd>
 
 
-<dt>threshold_mode</dt>
+<dt id="arg-threshold-mode">threshold_mode<a class="anchor" aria-label="anchor" href="#arg-threshold-mode"></a></dt>
 <dd><p>(str): One of <code>rel</code>, <code>abs</code>. In <code>rel</code> mode,
 dynamic_threshold &lt;- best * ( 1 + threshold ) in 'max' mode
 or best * ( 1 - threshold ) in <code>min</code> mode. In <code>abs</code> mode,
@@ -153,22 +137,22 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 best - threshold in <code>min</code> mode. Default: 'rel'.</p></dd>
 
 
-<dt>cooldown</dt>
+<dt id="arg-cooldown">cooldown<a class="anchor" aria-label="anchor" href="#arg-cooldown"></a></dt>
 <dd><p>(int): Number of epochs to wait before resuming normal
 operation after lr has been reduced. Default: 0.</p></dd>
 
 
-<dt>min_lr</dt>
+<dt id="arg-min-lr">min_lr<a class="anchor" aria-label="anchor" href="#arg-min-lr"></a></dt>
 <dd><p>(float or list): A scalar or a list of scalars. A lower bound
 on the learning rate of all param groups or each group respectively. Default: 0.</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float): Minimal decay applied to lr. If the difference between
 new and old lr is smaller than eps, the update is ignored. Default: 1e-8.</p></dd>
 
 
-<dt>verbose</dt>
+<dt id="arg-verbose">verbose<a class="anchor" aria-label="anchor" href="#arg-verbose"></a></dt>
 <dd><p>(bool): If <code>TRUE</code>, prints a message to stdout for
 each update. Default: <code>FALSE</code>.</p></dd>
 
@@ -177,7 +161,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> </span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{ </span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="optim_sgd.html">optim_sgd</a></span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr<span class="op">=</span><span class="fl">0.1</span>, momentum<span class="op">=</span><span class="fl">0.9</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">scheduler</span> <span class="op">&lt;-</span> <span class="fu">lr_reduce_on_plateau</span><span class="op">(</span><span class="va">optimizer</span>, <span class="st">'min'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">epoch</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span> <span class="op">{</span></span></span>
@@ -186,11 +170,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span> <span class="co"># note that step should be called after validate</span></span></span>
 <span class="r-in"><span> <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="va">val_loss</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -199,14 +183,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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   </body></html>
 
diff --git a/reference/lr_scheduler.html b/reference/lr_scheduler.html
index e194385a7e..aefd3fb273 100644
--- a/reference/lr_scheduler.html
+++ b/reference/lr_scheduler.html
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-      <img src="" class="logo" alt=""><h1>Creates learning rate schedulers</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Creates learning rate schedulers</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_scheduler.Rd</code></div>
     </div>
 
@@ -100,26 +82,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>classname</dt>
+
+
+<dl><dt id="arg-classname">classname<a class="anchor" aria-label="anchor" href="#arg-classname"></a></dt>
 <dd><p>optional name for the learning rate scheduler</p></dd>
 
 
-<dt>inherit</dt>
+<dt id="arg-inherit">inherit<a class="anchor" aria-label="anchor" href="#arg-inherit"></a></dt>
 <dd><p>an optional learning rate scheduler to inherit from</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>named list of methods. You must implement the <code>get_lr()</code>
 method that doesn't take any argument and returns learning rates
 for each <code>param_group</code> in the optimizer.</p></dd>
 
 
-<dt>parent_env</dt>
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -128,14 +112,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/lr_step.html b/reference/lr_step.html
index c34346a00a..a7bc349a4f 100644
--- a/reference/lr_step.html
+++ b/reference/lr_step.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Decays the learning rate of each parameter group by gamma every
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Step learning rate decay — lr_step • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Step learning rate decay — lr_step"><meta name="description" content="Decays the learning rate of each parameter group by gamma every
 step_size epochs. Notice that such decay can happen simultaneously with
 other changes to the learning rate from outside this scheduler. When
-last_epoch=-1, sets initial lr as lr."><title>Step learning rate decay — lr_step • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Step learning rate decay — lr_step"><meta property="og:description" content="Decays the learning rate of each parameter group by gamma every
+last_epoch=-1, sets initial lr as lr."><meta property="og:description" content="Decays the learning rate of each parameter group by gamma every
 step_size epochs. Notice that such decay can happen simultaneously with
 other changes to the learning rate from outside this scheduler. When
-last_epoch=-1, sets initial lr as lr."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Step learning rate decay</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
+
+      <h1>Step learning rate decay</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lr_scheduler.R" class="external-link"><code>R/optim-lr_scheduler.R</code></a></small>
       <div class="d-none name"><code>lr_step.Rd</code></div>
     </div>
 
@@ -104,20 +86,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>optimizer</dt>
+
+
+<dl><dt id="arg-optimizer">optimizer<a class="anchor" aria-label="anchor" href="#arg-optimizer"></a></dt>
 <dd><p>(Optimizer): Wrapped optimizer.</p></dd>
 
 
-<dt>step_size</dt>
+<dt id="arg-step-size">step_size<a class="anchor" aria-label="anchor" href="#arg-step-size"></a></dt>
 <dd><p>(int): Period of learning rate decay.</p></dd>
 
 
-<dt>gamma</dt>
+<dt id="arg-gamma">gamma<a class="anchor" aria-label="anchor" href="#arg-gamma"></a></dt>
 <dd><p>(float): Multiplicative factor of learning rate decay.
 Default: 0.1.</p></dd>
 
 
-<dt>last_epoch</dt>
+<dt id="arg-last-epoch">last_epoch<a class="anchor" aria-label="anchor" href="#arg-last-epoch"></a></dt>
 <dd><p>(int): The index of last epoch. Default: -1.</p></dd>
 
 </dl></div>
@@ -125,7 +109,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># Assuming optimizer uses lr = 0.05 for all groups</span></span></span>
 <span class="r-in"><span><span class="co"># lr = 0.05     if epoch &lt; 30</span></span></span>
 <span class="r-in"><span><span class="co"># lr = 0.005    if 30 &lt;= epoch &lt; 60</span></span></span>
@@ -137,12 +121,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="fu">validate</span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="va">scheduler</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -151,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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diff --git a/reference/nn_adaptive_avg_pool1d.html b/reference/nn_adaptive_avg_pool1d.html
index e4523fac0f..24ddc2f443 100644
--- a/reference/nn_adaptive_avg_pool1d.html
+++ b/reference/nn_adaptive_avg_pool1d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The output size is H, for any input size.
-The number of output features is equal to the number of input planes."><title>Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d"><meta property="og:description" content="The output size is H, for any input size.
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d"><meta name="description" content="The output size is H, for any input size.
+The number of output features is equal to the number of input planes."><meta property="og:description" content="The output size is H, for any input size.
+The number of output features is equal to the number of input planes."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -12,77 +9,62 @@
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
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@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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@@ -113,7 +97,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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-      <img src="" class="logo" alt=""><h1>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
     </div>
 
@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>output_size</dt>
+
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+<dl><dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the image of the form H x W.
 Can be a tuple (H, W) or a single H for a square image H x H.
 H and W can be either a <code>int</code>, or <code>NULL</code> which means the size will
@@ -120,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
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@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-    <dl><dt>output_size</dt>
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 <dd><p>the target output size of the form D x H x W.
 Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
 D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
@@ -120,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_adaptive_log_softmax_with_loss.html b/reference/nn_adaptive_log_softmax_with_loss.html
index 16f7c816eb..a543241737 100644
--- a/reference/nn_adaptive_log_softmax_with_loss.html
+++ b/reference/nn_adaptive_log_softmax_with_loss.html
@@ -1,10 +1,7 @@
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-Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou"><title>AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss"><meta property="og:description" content="Efficient softmax approximation as described in
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>AdaptiveLogSoftmaxWithLoss module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn_adaptive.R" class="external-link"><code>R/nn_adaptive.R</code></a></small>
+
+      <h1>AdaptiveLogSoftmaxWithLoss module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn_adaptive.R" class="external-link"><code>R/nn_adaptive.R</code></a></small>
       <div class="d-none name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
     </div>
 
@@ -104,33 +86,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_features</dt>
+
+
+<dl><dt id="arg-in-features">in_features<a class="anchor" aria-label="anchor" href="#arg-in-features"></a></dt>
 <dd><p>(int): Number of features in the input tensor</p></dd>
 
 
-<dt>n_classes</dt>
+<dt id="arg-n-classes">n_classes<a class="anchor" aria-label="anchor" href="#arg-n-classes"></a></dt>
 <dd><p>(int): Number of classes in the dataset</p></dd>
 
 
-<dt>cutoffs</dt>
+<dt id="arg-cutoffs">cutoffs<a class="anchor" aria-label="anchor" href="#arg-cutoffs"></a></dt>
 <dd><p>(Sequence): Cutoffs used to assign targets to their buckets</p></dd>
 
 
-<dt>div_value</dt>
+<dt id="arg-div-value">div_value<a class="anchor" aria-label="anchor" href="#arg-div-value"></a></dt>
 <dd><p>(float, optional): value used as an exponent to compute sizes
 of the clusters. Default: 4.0</p></dd>
 
 
-<dt>head_bias</dt>
+<dt id="arg-head-bias">head_bias<a class="anchor" aria-label="anchor" href="#arg-head-bias"></a></dt>
 <dd><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
 adaptive softmax. Default: <code>False</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul><li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
+    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul><li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
 log probabilities for each example</p></li>
 <li><p><strong>loss</strong> is a Scalar representing the computed negative
 log likelihood loss</p></li>
@@ -150,7 +132,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 present are evaluated.</p>
 <p>The idea is that the clusters which are accessed frequently
 (like the first one, containing most frequent labels), should also be cheap
-to compute -- that is, contain a small number of assigned labels.
+to compute – that is, contain a small number of assigned labels.
 We highly recommend taking a look at the original paper for more details.</p><ul><li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
 in the increasing order.
 It controls number of clusters and the partitioning of targets into
@@ -180,7 +162,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>Labels passed as inputs to this module should be sorted according to
 their frequency. This means that the most frequent label should be
@@ -189,7 +171,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>input: \((N, \mbox{in\_features})\)</p></li>
 <li><p>target: \((N)\) where each value satisfies \(0 &lt;= \mbox{target[i]} &lt;= \mbox{n\_classes}\)</p></li>
@@ -197,7 +179,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 <li><p>output2: <code>Scalar</code></p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -206,14 +188,14 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/nn_adaptive_max_pool1d.html b/reference/nn_adaptive_max_pool1d.html
index 6c08d128b7..763da08de1 100644
--- a/reference/nn_adaptive_max_pool1d.html
+++ b/reference/nn_adaptive_max_pool1d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d"><meta name="description" content="The output size is H, for any input size.
+The number of output features is equal to the number of input planes."><meta property="og:description" content="The output size is H, for any input size.
+The number of output features is equal to the number of input planes."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_adaptive_max_pool1d.Rd</code></div>
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@@ -98,11 +80,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>output_size</dt>
+
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+<dl><dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size H</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
 Useful to pass to <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></dd>
 
@@ -118,7 +102,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
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-      <img src="" class="logo" alt=""><h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_adaptive_max_pool2d.Rd</code></div>
     </div>
 
@@ -98,14 +80,16 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>output_size</dt>
+
+
+<dl><dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the image of the form H x W.
 Can be a tuple <code>(H, W)</code> or a single H for a square image H x H.
 H and W can be either a <code>int</code>, or <code>None</code> which means the size will
 be the same as that of the input.</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
 Useful to pass to <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></dd>
 
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_adaptive_max_pool3d.html b/reference/nn_adaptive_max_pool3d.html
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-      <img src="" class="logo" alt=""><h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_adaptive_max_pool3d.Rd</code></div>
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@@ -98,14 +80,16 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>output_size</dt>
+
+
+<dl><dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the image of the form D x H x W.
 Can be a tuple (D, H, W) or a single D for a cube D x D x D.
 D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
 be the same as that of the input.</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
 Useful to pass to <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></dd>
 
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_avg_pool1d.html b/reference/nn_avg_pool1d.html
index 495a694b09..087abf55b2 100644
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@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, L)\),
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, L)\),
 output \((N, C, L_{out})\) and kernel_size \(k\)
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a 1D average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 1D average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_avg_pool1d.Rd</code></div>
     </div>
 
@@ -107,23 +89,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on both sides</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when TRUE, will include the zero-padding in the averaging calculation</p></dd>
 
 </dl></div>
@@ -140,7 +124,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, L_{in})\)</p></li>
 <li><p>Output: \((N, C, L_{out})\), where</p></li>
@@ -160,11 +144,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4693 -0.1130 -0.5694</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7407  0.0182 -0.5100</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -173,14 +157,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nn_avg_pool2d.html b/reference/nn_avg_pool2d.html
index aaeb56bca8..3cd34bdc28 100644
--- a/reference/nn_avg_pool2d.html
+++ b/reference/nn_avg_pool2d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
 output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
-can be precisely described as:"><title>Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+can be precisely described as:"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
 output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
-can be precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a 2D average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 2D average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_avg_pool2d.Rd</code></div>
     </div>
 
@@ -108,27 +90,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on both sides</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when TRUE, will include the zero-padding in the averaging calculation</p></dd>
 
 
-<dt>divisor_override</dt>
+<dt id="arg-divisor-override">divisor_override<a class="anchor" aria-label="anchor" href="#arg-divisor-override"></a></dt>
 <dd><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></dd>
 
 </dl></div>
@@ -140,13 +124,13 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 $$</p>
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints – in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
@@ -173,7 +157,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -182,14 +166,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_avg_pool3d.html b/reference/nn_avg_pool3d.html
index d8dedb8ec5..dec907cbed 100644
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+++ b/reference/nn_avg_pool3d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
 output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
-can be precisely described as:"><title>Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+can be precisely described as:"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
 output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
-can be precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a 3D average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 3D average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_avg_pool3d.Rd</code></div>
     </div>
 
@@ -108,27 +90,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on all three sides</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when TRUE, will include the zero-padding in the averaging calculation</p></dd>
 
 
-<dt>divisor_override</dt>
+<dt id="arg-divisor-override">divisor_override<a class="anchor" aria-label="anchor" href="#arg-divisor-override"></a></dt>
 <dd><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></dd>
 
 </dl></div>
@@ -142,13 +126,13 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 $$</p>
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
 for <code>padding</code> number of points.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints – in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
@@ -179,7 +163,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -188,14 +172,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
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+
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diff --git a/reference/nn_batch_norm1d.html b/reference/nn_batch_norm1d.html
index fc13fcb3fd..900e48d032 100644
--- a/reference/nn_batch_norm1d.html
+++ b/reference/nn_batch_norm1d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>BatchNorm1D module — nn_batch_norm1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="BatchNorm1D module — nn_batch_norm1d"><meta name="description" content="Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
 inputs with optional additional channel dimension) as described in the paper
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+Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"><meta property="og:description" content="Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
 inputs with optional additional channel dimension) as described in the paper
-Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>BatchNorm1D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
+
+      <h1>BatchNorm1D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
       <div class="d-none name"><code>nn_batch_norm1d.Rd</code></div>
     </div>
 
@@ -107,28 +89,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_features</dt>
+
+
+<dl><dt id="arg-num-features">num_features<a class="anchor" aria-label="anchor" href="#arg-num-features"></a></dt>
 <dd><p>\(C\) from an expected input of size
 \((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability.
 Default: 1e-5</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>the value used for the running_mean and running_var
 computation. Can be set to <code>NULL</code> for cumulative moving average
 (i.e. simple average). Default: 0.1</p></dd>
 
 
-<dt>affine</dt>
+<dt id="arg-affine">affine<a class="anchor" aria-label="anchor" href="#arg-affine"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this module has
 learnable affine parameters. Default: <code>TRUE</code></p></dd>
 
 
-<dt>track_running_stats</dt>
+<dt id="arg-track-running-stats">track_running_stats<a class="anchor" aria-label="anchor" href="#arg-track-running-stats"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this
 module tracks the running mean and variance, and when set to <code>FALSE</code>,
 this module does not track such statistics and always uses batch
@@ -154,7 +138,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>This <code>momentum</code> argument is different from one used in optimizer
@@ -168,7 +152,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
 <li><p>Output: \((N, C)\) or \((N, C, L)\) (same shape as input)</p></li>
@@ -186,7 +170,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -195,14 +179,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_batch_norm2d.html b/reference/nn_batch_norm2d.html
index c056da42c3..d4ab819f52 100644
--- a/reference/nn_batch_norm2d.html
+++ b/reference/nn_batch_norm2d.html
@@ -1,12 +1,9 @@
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 additional channel dimension) as described in the paper
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-      <img src="" class="logo" alt=""><h1>BatchNorm2D</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
+
+      <h1>BatchNorm2D</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
       <div class="d-none name"><code>nn_batch_norm2d.Rd</code></div>
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@@ -107,28 +89,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_features</dt>
+
+
+<dl><dt id="arg-num-features">num_features<a class="anchor" aria-label="anchor" href="#arg-num-features"></a></dt>
 <dd><p>\(C\) from an expected input of size
 \((N, C, H, W)\)</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability.
 Default: 1e-5</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>the value used for the running_mean and running_var
 computation. Can be set to <code>None</code> for cumulative moving average
 (i.e. simple average). Default: 0.1</p></dd>
 
 
-<dt>affine</dt>
+<dt id="arg-affine">affine<a class="anchor" aria-label="anchor" href="#arg-affine"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this module has
 learnable affine parameters. Default: <code>TRUE</code></p></dd>
 
 
-<dt>track_running_stats</dt>
+<dt id="arg-track-running-stats">track_running_stats<a class="anchor" aria-label="anchor" href="#arg-track-running-stats"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this
 module tracks the running mean and variance, and when set to <code>FALSE</code>,
 this module does not track such statistics and uses batch statistics instead
@@ -167,7 +151,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H, W)\)</p></li>
 <li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
@@ -185,7 +169,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -194,14 +178,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_batch_norm3d.html b/reference/nn_batch_norm3d.html
index dcf3522aa9..95ecbd6bb1 100644
--- a/reference/nn_batch_norm3d.html
+++ b/reference/nn_batch_norm3d.html
@@ -1,12 +1,9 @@
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+Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift."><meta property="og:description" content="Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs
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-      <img src="" class="logo" alt=""><h1>BatchNorm3D</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
+
+      <h1>BatchNorm3D</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-batchnorm.R" class="external-link"><code>R/nn-batchnorm.R</code></a></small>
       <div class="d-none name"><code>nn_batch_norm3d.Rd</code></div>
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@@ -107,28 +89,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_features</dt>
+
+
+<dl><dt id="arg-num-features">num_features<a class="anchor" aria-label="anchor" href="#arg-num-features"></a></dt>
 <dd><p>\(C\) from an expected input of size
 \((N, C, D, H, W)\)</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability.
 Default: 1e-5</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>the value used for the running_mean and running_var
 computation. Can be set to <code>None</code> for cumulative moving average
 (i.e. simple average). Default: 0.1</p></dd>
 
 
-<dt>affine</dt>
+<dt id="arg-affine">affine<a class="anchor" aria-label="anchor" href="#arg-affine"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this module has
 learnable affine parameters. Default: <code>TRUE</code></p></dd>
 
 
-<dt>track_running_stats</dt>
+<dt id="arg-track-running-stats">track_running_stats<a class="anchor" aria-label="anchor" href="#arg-track-running-stats"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this
 module tracks the running mean and variance, and when set to <code>FALSE</code>,
 this module does not track such statistics and uses batch statistics instead
@@ -169,7 +153,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, D, H, W)\)</p></li>
 <li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
@@ -187,7 +171,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -196,14 +180,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_bce_loss.html b/reference/nn_bce_loss.html
index 8e0602c951..e6112687e9 100644
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@@ -1,10 +1,7 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Binary cross entropy loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Binary cross entropy loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_bce_loss.Rd</code></div>
     </div>
 
@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>weight</dt>
+
+
+<dl><dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional): a manual rescaling weight given to the loss
 of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -145,7 +129,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
@@ -166,7 +150,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -175,14 +159,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_bce_with_logits_loss.html b/reference/nn_bce_with_logits_loss.html
index 6277fdad9e..e61b819383 100644
--- a/reference/nn_bce_with_logits_loss.html
+++ b/reference/nn_bce_with_logits_loss.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This loss combines a Sigmoid layer and the BCELoss in one single
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>BCE with logits loss — nn_bce_with_logits_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="BCE with logits loss — nn_bce_with_logits_loss"><meta name="description" content="This loss combines a Sigmoid layer and the BCELoss in one single
 class. This version is more numerically stable than using a plain Sigmoid
 followed by a BCELoss as, by combining the operations into one layer,
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+we take advantage of the log-sum-exp trick for numerical stability."><meta property="og:description" content="This loss combines a Sigmoid layer and the BCELoss in one single
 class. This version is more numerically stable than using a plain Sigmoid
 followed by a BCELoss as, by combining the operations into one layer,
-we take advantage of the log-sum-exp trick for numerical stability."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>BCE with logits loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>BCE with logits loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_bce_with_logits_loss.Rd</code></div>
     </div>
 
@@ -104,19 +86,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>weight</dt>
+
+
+<dl><dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional): a manual rescaling weight given to the loss
 of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
 elements in the output, <code>'sum'</code>: the output will be summed.</p></dd>
 
 
-<dt>pos_weight</dt>
+<dt id="arg-pos-weight">pos_weight<a class="anchor" aria-label="anchor" href="#arg-pos-weight"></a></dt>
 <dd><p>(Tensor, optional): a weight of positive examples.
 Must be a vector with length equal to the number of classes.</p></dd>
 
@@ -159,7 +143,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional dimensions</p></li>
 <li><p>Target: \((N, *)\), same shape as the input</p></li>
@@ -187,7 +171,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
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@@ -196,14 +180,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-      <img src="" class="logo" alt=""><h1>Bilinear module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
+
+      <h1>Bilinear module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
       <div class="d-none name"><code>nn_bilinear.Rd</code></div>
     </div>
 
@@ -98,26 +80,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in1_features</dt>
+
+
+<dl><dt id="arg-in-features">in1_features<a class="anchor" aria-label="anchor" href="#arg-in-features"></a></dt>
 <dd><p>size of each first input sample</p></dd>
 
 
-<dt>in2_features</dt>
+<dt id="arg-in-features">in2_features<a class="anchor" aria-label="anchor" href="#arg-in-features"></a></dt>
 <dd><p>size of each second input sample</p></dd>
 
 
-<dt>out_features</dt>
+<dt id="arg-out-features">out_features<a class="anchor" aria-label="anchor" href="#arg-out-features"></a></dt>
 <dd><p>size of each output sample</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
 Default: <code>TRUE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
 \(*\) means any number of additional dimensions. All but the last
@@ -128,7 +112,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight: the learnable weights of the module of shape
 \((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\).
@@ -152,7 +136,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 128  50</span>
 </code></pre></div>
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diff --git a/reference/nn_buffer.html b/reference/nn_buffer.html
index 51827a15ec..51f24608c8 100644
--- a/reference/nn_buffer.html
+++ b/reference/nn_buffer.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Creates a nn_buffer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Creates a nn_buffer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>nn_buffer.Rd</code></div>
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@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the tensor that will be converted to nn_buffer</p></dd>
 
 
-<dt>persistent</dt>
+<dt id="arg-persistent">persistent<a class="anchor" aria-label="anchor" href="#arg-persistent"></a></dt>
 <dd><p>whether the buffer should be persistent or not.</p></dd>
 
 </dl></div>
 
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index 6ae0f9d781..d87e8a8f45 100644
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-      <img src="" class="logo" alt=""><h1>CELU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>CELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_celu.Rd</code></div>
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@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
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@@ -113,7 +97,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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-    
+
 
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@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>Sparsemax activation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Sparsemax activation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_contrib_sparsemax.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dim</dt>
+
+
+<dl><dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>The dimension over which to apply the sparsemax function. (-1)</p></dd>
 
 </dl></div>
@@ -106,7 +90,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 The implementation is based on <a href="https://github.com/aced125/sparsemax/tree/master/sparsemax" class="external-link">aced125/sparsemax</a></p>
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diff --git a/reference/nn_conv1d.html b/reference/nn_conv1d.html
index 51dbe9ff6e..9f669b2e9d 100644
--- a/reference/nn_conv1d.html
+++ b/reference/nn_conv1d.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 1D convolution over an input signal composed of several input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Conv1D module — nn_conv1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv1D module — nn_conv1d"><meta name="description" content="Applies a 1D convolution over an input signal composed of several input
 planes.
 In the simplest case, the output value of the layer with input size
 \((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
-precisely described as:"><title>Conv1D module — nn_conv1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv1D module — nn_conv1d"><meta property="og:description" content="Applies a 1D convolution over an input signal composed of several input
+precisely described as:"><meta property="og:description" content="Applies a 1D convolution over an input signal composed of several input
 planes.
 In the simplest case, the output value of the layer with input size
 \((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
-precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv1D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+
+      <h1>Conv1D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
       <div class="d-none name"><code>nn_conv1d.Rd</code></div>
     </div>
 
@@ -117,43 +99,45 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_channels</dt>
+
+
+<dl><dt id="arg-in-channels">in_channels<a class="anchor" aria-label="anchor" href="#arg-in-channels"></a></dt>
 <dd><p>(int): Number of channels in the input image</p></dd>
 
 
-<dt>out_channels</dt>
+<dt id="arg-out-channels">out_channels<a class="anchor" aria-label="anchor" href="#arg-out-channels"></a></dt>
 <dd><p>(int): Number of channels produced by the convolution</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the convolving kernel</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int, tuple or str, optional) – Padding added to both sides of
 the input. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>(int or tuple, optional): Spacing between kernel
 elements. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int, optional): Number of blocked connections from input
 channels to output channels. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
 output. Default: <code>TRUE</code></p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
 <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
 
@@ -190,7 +174,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>Depending of the size of your kernel, several (of the last)
@@ -206,7 +190,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, L_{out})\) where</p></li>
@@ -217,7 +201,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
     </div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\).
@@ -239,7 +223,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -248,14 +232,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
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+
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diff --git a/reference/nn_conv2d.html b/reference/nn_conv2d.html
index 2df1cf10ae..b1b607e22a 100644
--- a/reference/nn_conv2d.html
+++ b/reference/nn_conv2d.html
@@ -1,10 +1,7 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv2D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+
+      <h1>Conv2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
       <div class="d-none name"><code>nn_conv2d.Rd</code></div>
     </div>
 
@@ -108,44 +90,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_channels</dt>
+
+
+<dl><dt id="arg-in-channels">in_channels<a class="anchor" aria-label="anchor" href="#arg-in-channels"></a></dt>
 <dd><p>(int): Number of channels in the input image</p></dd>
 
 
-<dt>out_channels</dt>
+<dt id="arg-out-channels">out_channels<a class="anchor" aria-label="anchor" href="#arg-out-channels"></a></dt>
 <dd><p>(int): Number of channels produced by the convolution</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the convolving kernel</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple or string, optional): Zero-padding added to both sides of
 the input. controls the amount of padding applied to the input. It
 can be either a string <code>'valid'</code>, <code>'same'</code> or a tuple of ints giving the
 amount of implicit padding applied on both sides. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int, optional): Number of blocked connections from input
 channels to output channels. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
 output. Default: <code>TRUE</code></p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
 <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
 
@@ -180,14 +164,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 its own set of filters, of size:
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
 </ul></li>
-</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the height and
 width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+<li><p>a <code>tuple</code> of two ints – in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>Depending of the size of your kernel, several (of the last)
@@ -207,7 +191,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
@@ -222,7 +206,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\),
@@ -252,7 +236,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -261,14 +245,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_conv3d.html b/reference/nn_conv3d.html
index 1df02a4f5d..29a4876cb9 100644
--- a/reference/nn_conv3d.html
+++ b/reference/nn_conv3d.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 3D convolution over an input signal composed of several input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Conv3D module — nn_conv3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv3D module — nn_conv3d"><meta name="description" content="Applies a 3D convolution over an input signal composed of several input
 planes.
 In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
-and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:"><title>Conv3D module — nn_conv3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv3D module — nn_conv3d"><meta property="og:description" content="Applies a 3D convolution over an input signal composed of several input
+and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:"><meta property="og:description" content="Applies a 3D convolution over an input signal composed of several input
 planes.
 In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
-and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv3D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+
+      <h1>Conv3D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
       <div class="d-none name"><code>nn_conv3d.Rd</code></div>
     </div>
 
@@ -114,39 +96,41 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_channels</dt>
+
+
+<dl><dt id="arg-in-channels">in_channels<a class="anchor" aria-label="anchor" href="#arg-in-channels"></a></dt>
 <dd><p>(int): Number of channels in the input image</p></dd>
 
 
-<dt>out_channels</dt>
+<dt id="arg-out-channels">out_channels<a class="anchor" aria-label="anchor" href="#arg-out-channels"></a></dt>
 <dd><p>(int): Number of channels produced by the convolution</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the convolving kernel</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int, tuple or str, optional): padding added to all six sides of the input. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
 
 </dl></div>
@@ -172,8 +156,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <li><p>At groups= <code>in_channels</code>, each input channel is convolved with
 its own set of filters, of size
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
-</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints – in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
@@ -196,7 +180,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
@@ -215,7 +199,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\)
@@ -241,7 +225,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -250,14 +234,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/nn_conv_transpose1d.html b/reference/nn_conv_transpose1d.html
index 768c9994fb..d407a229a2 100644
--- a/reference/nn_conv_transpose1d.html
+++ b/reference/nn_conv_transpose1d.html
@@ -1,10 +1,7 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 1D transposed convolution operator over an input image
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+composed of several input planes."><meta property="og:description" content="Applies a 1D transposed convolution operator over an input image
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-      <img src="" class="logo" alt=""><h1>ConvTranspose1D</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+
+      <h1>ConvTranspose1D</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
       <div class="d-none name"><code>nn_conv_transpose1d.Rd</code></div>
     </div>
 
@@ -109,45 +91,47 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_channels</dt>
+
+
+<dl><dt id="arg-in-channels">in_channels<a class="anchor" aria-label="anchor" href="#arg-in-channels"></a></dt>
 <dd><p>(int): Number of channels in the input image</p></dd>
 
 
-<dt>out_channels</dt>
+<dt id="arg-out-channels">out_channels<a class="anchor" aria-label="anchor" href="#arg-out-channels"></a></dt>
 <dd><p>(int): Number of channels produced by the convolution</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the convolving kernel</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
 will be added to both sides of the input. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>(int or tuple, optional): Additional size added to one side
 of the output shape. Default: 0</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
 <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
 
@@ -200,7 +184,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, L_{out})\) where
@@ -211,7 +195,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
@@ -234,7 +218,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -243,14 +227,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
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+
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diff --git a/reference/nn_conv_transpose2d.html b/reference/nn_conv_transpose2d.html
index 562ac04196..c79cbc0b50 100644
--- a/reference/nn_conv_transpose2d.html
+++ b/reference/nn_conv_transpose2d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 2D transposed convolution operator over an input image
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+composed of several input planes."><meta property="og:description" content="Applies a 2D transposed convolution operator over an input image
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-      <img src="" class="logo" alt=""><h1>ConvTranpose2D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+
+      <h1>ConvTranpose2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
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@@ -109,45 +91,47 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>in_channels</dt>
+
+
+<dl><dt id="arg-in-channels">in_channels<a class="anchor" aria-label="anchor" href="#arg-in-channels"></a></dt>
 <dd><p>(int): Number of channels in the input image</p></dd>
 
 
-<dt>out_channels</dt>
+<dt id="arg-out-channels">out_channels<a class="anchor" aria-label="anchor" href="#arg-out-channels"></a></dt>
 <dd><p>(int): Number of channels produced by the convolution</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the convolving kernel</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
 will be added to both sides of each dimension in the input. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>(int or tuple, optional): Additional size added to one side
 of each dimension in the output shape. Default: 0</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
 <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
 
@@ -176,8 +160,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
 </ul></li>
 </ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the height and width dimensions</p></li>
+<li><p>a <code>tuple</code> of two ints – in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
@@ -202,7 +186,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
@@ -217,7 +201,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
@@ -252,7 +236,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1]  1 16 12 12</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -261,14 +245,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
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index cb47e09707..d6d6cd0a00 100644
--- a/reference/nn_conv_transpose3d.html
+++ b/reference/nn_conv_transpose3d.html
@@ -1,10 +1,7 @@
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-planes."><title>ConvTranpose3D module — nn_conv_transpose3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="ConvTranpose3D module — nn_conv_transpose3d"><meta property="og:description" content="Applies a 3D transposed convolution operator over an input image composed of several input
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+planes."><meta property="og:description" content="Applies a 3D transposed convolution operator over an input image composed of several input
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>ConvTranpose3D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
+
+      <h1>ConvTranpose3D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-conv.R" class="external-link"><code>R/nn-conv.R</code></a></small>
       <div class="d-none name"><code>nn_conv_transpose3d.Rd</code></div>
     </div>
 
@@ -109,47 +91,49 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_channels</dt>
+
+
+<dl><dt id="arg-in-channels">in_channels<a class="anchor" aria-label="anchor" href="#arg-in-channels"></a></dt>
 <dd><p>(int): Number of channels in the input image</p></dd>
 
 
-<dt>out_channels</dt>
+<dt id="arg-out-channels">out_channels<a class="anchor" aria-label="anchor" href="#arg-out-channels"></a></dt>
 <dd><p>(int): Number of channels produced by the convolution</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the convolving kernel</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
 will be added to both sides of each dimension in the input. Default: 0
 output_padding (int or tuple, optional): Additional size added to one side
 of each dimension in the output shape. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>(int or tuple, optional): Additional size added to one side
 of each dimension in the output shape. Default: 0</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></dd>
 
 </dl></div>
@@ -179,8 +163,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
 </ul></li>
 </ul><p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
-can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the depth, height and width dimensions</p></li>
+<li><p>a <code>tuple</code> of three ints – in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
@@ -206,7 +190,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
@@ -225,7 +209,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape
 \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
@@ -242,18 +226,18 @@ <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attri
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="co"># With square kernels and equal stride</span></span></span>
 <span class="r-in"><span><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose3d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, stride <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># non-square kernels and unequal stride and with padding</span></span></span>
 <span class="r-in"><span><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_conv_transpose3d</span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">2</span><span class="op">)</span>, stride <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, padding <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">4</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">10</span>, <span class="fl">50</span>, <span class="fl">100</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -262,14 +246,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nn_cosine_embedding_loss.html b/reference/nn_cosine_embedding_loss.html
index 1da6982fa7..1ac230df8c 100644
--- a/reference/nn_cosine_embedding_loss.html
+++ b/reference/nn_cosine_embedding_loss.html
@@ -1,18 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the loss given input tensors
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cosine embedding loss — nn_cosine_embedding_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cosine embedding loss — nn_cosine_embedding_loss"><meta name="description" content="Creates a criterion that measures the loss given input tensors
 \(x_1\), \(x_2\) and a Tensor label \(y\) with values 1 or -1.
 This is used for measuring whether two inputs are similar or dissimilar,
 using the cosine distance, and is typically used for learning nonlinear
 embeddings or semi-supervised learning.
-The loss function for each sample is:"><title>Cosine embedding loss — nn_cosine_embedding_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cosine embedding loss — nn_cosine_embedding_loss"><meta property="og:description" content="Creates a criterion that measures the loss given input tensors
+The loss function for each sample is:"><meta property="og:description" content="Creates a criterion that measures the loss given input tensors
 \(x_1\), \(x_2\) and a Tensor label \(y\) with values 1 or -1.
 This is used for measuring whether two inputs are similar or dissimilar,
 using the cosine distance, and is typically used for learning nonlinear
 embeddings or semi-supervised learning.
-The loss function for each sample is:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+The loss function for each sample is:"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Cosine embedding loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Cosine embedding loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_cosine_embedding_loss.Rd</code></div>
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@@ -110,13 +92,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>margin</dt>
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+<dl><dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
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-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
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 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -134,7 +118,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 $$</p>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nn_cross_entropy_loss.html b/reference/nn_cross_entropy_loss.html
index 21355f94b7..574e4aa2ed 100644
--- a/reference/nn_cross_entropy_loss.html
+++ b/reference/nn_cross_entropy_loss.html
@@ -1,10 +1,7 @@
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+It is useful when training a classification problem with C classes."><meta property="og:description" content="This criterion combines nn_log_softmax() and nn_nll_loss() in one single class.
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-      <img src="" class="logo" alt=""><h1>CrossEntropyLoss module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>CrossEntropyLoss module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_cross_entropy_loss.Rd</code></div>
     </div>
 
@@ -98,18 +80,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>weight</dt>
+
+
+<dl><dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional): a manual rescaling weight given to each class.
 If given, has to be a Tensor of size <code>C</code></p></dd>
 
 
-<dt>ignore_index</dt>
+<dt id="arg-ignore-index">ignore_index<a class="anchor" aria-label="anchor" href="#arg-ignore-index"></a></dt>
 <dd><p>(int, optional): Specifies a target value that is ignored
 and does not contribute to the input gradient. When <code>size_average</code> is
 <code>TRUE</code>, the loss is averaged over non-ignored targets.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -146,7 +130,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
 \((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
@@ -172,7 +156,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -181,14 +165,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_ctc_loss.html b/reference/nn_ctc_loss.html
index a4b2129539..34925a0ef9 100644
--- a/reference/nn_ctc_loss.html
+++ b/reference/nn_ctc_loss.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>The Connectionist Temporal Classification loss. — nn_ctc_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="The Connectionist Temporal Classification loss. — nn_ctc_loss"><meta name="description" content='Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
 probability of possible alignments of input to target, producing a loss value which is differentiable
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-limits the length of the target sequence such that it must be \(\leq\) the input length.'><title>The Connectionist Temporal Classification loss. — nn_ctc_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="The Connectionist Temporal Classification loss. — nn_ctc_loss"><meta property="og:description" content='Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
+limits the length of the target sequence such that it must be \(\leq\) the input length.'><meta property="og:description" content='Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
 probability of possible alignments of input to target, producing a loss value which is differentiable
 with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which
-limits the length of the target sequence such that it must be \(\leq\) the input length.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>The Connectionist Temporal Classification loss.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>The Connectionist Temporal Classification loss.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_ctc_loss.Rd</code></div>
     </div>
 
@@ -104,18 +86,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>blank</dt>
+
+
+<dl><dt id="arg-blank">blank<a class="anchor" aria-label="anchor" href="#arg-blank"></a></dt>
 <dd><p>(int, optional): blank label. Default \(0\).</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the output losses will be divided by the target lengths and
 then the mean over the batch is taken. Default: <code>'mean'</code></p></dd>
 
 
-<dt>zero_infinity</dt>
+<dt id="arg-zero-infinity">zero_infinity<a class="anchor" aria-label="anchor" href="#arg-zero-infinity"></a></dt>
 <dd><p>(bool, optional):
 Whether to zero infinite losses and the associated gradients.
 Default: <code>FALSE</code>
@@ -137,7 +121,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Log_probs: Tensor of size \((T, N, C)\),
 where \(T = \mbox{input length}\),
@@ -226,7 +210,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -235,14 +219,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/nn_dropout.html b/reference/nn_dropout.html
index aa72708b51..b4f8cb0cde 100644
--- a/reference/nn_dropout.html
+++ b/reference/nn_dropout.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="During training, randomly zeroes some of the elements of the input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dropout module — nn_dropout • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout module — nn_dropout"><meta name="description" content="During training, randomly zeroes some of the elements of the input
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+call."><meta property="og:description" content="During training, randomly zeroes some of the elements of the input
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-      <img src="" class="logo" alt=""><h1>Dropout module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
+
+      <h1>Dropout module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
       <div class="d-none name"><code>nn_dropout.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>p</dt>
+
+
+<dl><dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>probability of an element to be zeroed. Default: 0.5</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -123,7 +107,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\). Input can be of any shape</p></li>
 <li><p>Output: \((*)\). Output is of the same shape as input</p></li>
@@ -138,7 +122,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -147,14 +131,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_dropout2d.html b/reference/nn_dropout2d.html
index c973c4e146..07bb13fa65 100644
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+++ b/reference/nn_dropout2d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Randomly zero out entire channels (a channel is a 2D feature map,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dropout2D module — nn_dropout2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout2D module — nn_dropout2d"><meta name="description" content="Randomly zero out entire channels (a channel is a 2D feature map,
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+batched input is a 2D tensor \(\mbox{input}[i, j]\))."><meta property="og:description" content="Randomly zero out entire channels (a channel is a 2D feature map,
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-      <img src="" class="logo" alt=""><h1>Dropout2D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
+
+      <h1>Dropout2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
       <div class="d-none name"><code>nn_dropout2d.Rd</code></div>
     </div>
 
@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>p</dt>
+
+
+<dl><dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(float, optional): probability of an element to be zero-ed.</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
 in-place</p></dd>
 
@@ -126,7 +110,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H, W)\)</p></li>
 <li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_dropout3d.html b/reference/nn_dropout3d.html
index 75fe3a09a3..265a360683 100644
--- a/reference/nn_dropout3d.html
+++ b/reference/nn_dropout3d.html
@@ -1,12 +1,9 @@
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-      <img src="" class="logo" alt=""><h1>Dropout3D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
+
+      <h1>Dropout3D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-dropout.R" class="external-link"><code>R/nn-dropout.R</code></a></small>
       <div class="d-none name"><code>nn_dropout3d.Rd</code></div>
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@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>p</dt>
+
+
+<dl><dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(float, optional): probability of an element to be zeroed.</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
 in-place</p></dd>
 
@@ -126,7 +110,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, D, H, W)\)</p></li>
 <li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_elu.html b/reference/nn_elu.html
index 6be8237dc0..86494b42d2 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>ELU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>ELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_elu.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -111,7 +95,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nn_embedding.html b/reference/nn_embedding.html
index 3d5423286a..8a0fc8a5d6 100644
--- a/reference/nn_embedding.html
+++ b/reference/nn_embedding.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A simple lookup table that stores embeddings of a fixed dictionary and size.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Embedding module — nn_embedding • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Embedding module — nn_embedding"><meta name="description" content="A simple lookup table that stores embeddings of a fixed dictionary and size.
 This module is often used to store word embeddings and retrieve them using indices.
 The input to the module is a list of indices, and the output is the corresponding
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+word embeddings."><meta property="og:description" content="A simple lookup table that stores embeddings of a fixed dictionary and size.
 This module is often used to store word embeddings and retrieve them using indices.
 The input to the module is a list of indices, and the output is the corresponding
-word embeddings."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Embedding module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-sparse.R" class="external-link"><code>R/nn-sparse.R</code></a></small>
+
+      <h1>Embedding module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-sparse.R" class="external-link"><code>R/nn-sparse.R</code></a></small>
       <div class="d-none name"><code>nn_embedding.Rd</code></div>
     </div>
 
@@ -113,38 +95,40 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_embeddings</dt>
+
+
+<dl><dt id="arg-num-embeddings">num_embeddings<a class="anchor" aria-label="anchor" href="#arg-num-embeddings"></a></dt>
 <dd><p>(int): size of the dictionary of embeddings</p></dd>
 
 
-<dt>embedding_dim</dt>
+<dt id="arg-embedding-dim">embedding_dim<a class="anchor" aria-label="anchor" href="#arg-embedding-dim"></a></dt>
 <dd><p>(int): the size of each embedding vector</p></dd>
 
 
-<dt>padding_idx</dt>
+<dt id="arg-padding-idx">padding_idx<a class="anchor" aria-label="anchor" href="#arg-padding-idx"></a></dt>
 <dd><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
 (initialized to zeros) whenever it encounters the index.</p></dd>
 
 
-<dt>max_norm</dt>
+<dt id="arg-max-norm">max_norm<a class="anchor" aria-label="anchor" href="#arg-max-norm"></a></dt>
 <dd><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
 is renormalized to have norm <code>max_norm</code>.</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></dd>
 
 
-<dt>scale_grad_by_freq</dt>
+<dt id="arg-scale-grad-by-freq">scale_grad_by_freq<a class="anchor" aria-label="anchor" href="#arg-scale-grad-by-freq"></a></dt>
 <dd><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
 the words in the mini-batch. Default <code>False</code>.</p></dd>
 
 
-<dt>sparse</dt>
+<dt id="arg-sparse">sparse<a class="anchor" aria-label="anchor" href="#arg-sparse"></a></dt>
 <dd><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></dd>
 
 
-<dt>.weight</dt>
+<dt id="arg--weight">.weight<a class="anchor" aria-label="anchor" href="#arg--weight"></a></dt>
 <dd><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
 <p>See Notes for more details regarding sparse gradients.</p></dd>
 
@@ -163,14 +147,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
 initialized from \(\mathcal{N}(0, 1)\)</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
 <li><p>Output: \((*, H)\), where <code>*</code> is the input shape and \(H=\mbox{embedding\_dim}\)</p></li>
@@ -192,13 +176,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0913  0.6573  2.3777</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.7786  0.6836  0.6713</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3899 -0.0915 -1.3749</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0914 -0.3328 -0.6087</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4,3} ][ grad_fn = &lt;EmbeddingBackward0&gt; ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -207,14 +191,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_embedding_bag.html b/reference/nn_embedding_bag.html
index 599cfcbbbb..c5edd6e94f 100644
--- a/reference/nn_embedding_bag.html
+++ b/reference/nn_embedding_bag.html
@@ -1,10 +1,7 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Embedding bag module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-sparse.R" class="external-link"><code>R/nn-sparse.R</code></a></small>
+
+      <h1>Embedding bag module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-sparse.R" class="external-link"><code>R/nn-sparse.R</code></a></small>
       <div class="d-none name"><code>nn_embedding_bag.Rd</code></div>
     </div>
 
@@ -109,56 +91,58 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_embeddings</dt>
+
+
+<dl><dt id="arg-num-embeddings">num_embeddings<a class="anchor" aria-label="anchor" href="#arg-num-embeddings"></a></dt>
 <dd><p>(int): size of the dictionary of embeddings</p></dd>
 
 
-<dt>embedding_dim</dt>
+<dt id="arg-embedding-dim">embedding_dim<a class="anchor" aria-label="anchor" href="#arg-embedding-dim"></a></dt>
 <dd><p>(int): the size of each embedding vector</p></dd>
 
 
-<dt>max_norm</dt>
+<dt id="arg-max-norm">max_norm<a class="anchor" aria-label="anchor" href="#arg-max-norm"></a></dt>
 <dd><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
 is renormalized to have norm <code>max_norm</code>.</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code></p></dd>
 
 
-<dt>scale_grad_by_freq</dt>
+<dt id="arg-scale-grad-by-freq">scale_grad_by_freq<a class="anchor" aria-label="anchor" href="#arg-scale-grad-by-freq"></a></dt>
 <dd><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
 the words in the mini-batch. Default <code>False</code>.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(string, optional): <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies the way to reduce the bag.
 <code>"sum"</code> computes the weighted sum, taking <code>per_sample_weights</code>  into consideration. <code>"mean"</code> computes
 the average of the values in the bag, <code>"max"</code> computes the max value over each bag.</p></dd>
 
 
-<dt>sparse</dt>
+<dt id="arg-sparse">sparse<a class="anchor" aria-label="anchor" href="#arg-sparse"></a></dt>
 <dd><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.
 See Notes for more details regarding sparse gradients.</p></dd>
 
 
-<dt>include_last_offset</dt>
+<dt id="arg-include-last-offset">include_last_offset<a class="anchor" aria-label="anchor" href="#arg-include-last-offset"></a></dt>
 <dd><p>(bool, optional): if <code>True</code>, <code>offsets</code> has one additional element, where the last element
 is equivalent to the size of <code>indices</code>. This matches the CSR format.</p></dd>
 
 
-<dt>padding_idx</dt>
+<dt id="arg-padding-idx">padding_idx<a class="anchor" aria-label="anchor" href="#arg-padding-idx"></a></dt>
 <dd><p>(int, optional):  If given, pads the output with the embedding vector at <code>padding_idx</code>
 (initialized to zeros) whenever it encounters the index.</p></dd>
 
 
-<dt>.weight</dt>
+<dt id="arg--weight">.weight<a class="anchor" aria-label="anchor" href="#arg--weight"></a></dt>
 <dd><p>(Tensor, optional) embeddings weights (in case you want to set it manually)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
 initialized from \(\mathcal{N}(0, 1)\)</p></li>
@@ -186,7 +170,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -195,14 +179,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nn_flatten.html b/reference/nn_flatten.html
index a8ef40c68d..32fbf37f1a 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Flattens a contiguous range of dims into a tensor.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-flatten.R" class="external-link"><code>R/nn-flatten.R</code></a></small>
+
+      <h1>Flattens a contiguous range of dims into a tensor.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-flatten.R" class="external-link"><code>R/nn-flatten.R</code></a></small>
       <div class="d-none name"><code>nn_flatten.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>start_dim</dt>
+
+
+<dl><dt id="arg-start-dim">start_dim<a class="anchor" aria-label="anchor" href="#arg-start-dim"></a></dt>
 <dd><p>first dim to flatten (default = 2).</p></dd>
 
 
-<dt>end_dim</dt>
+<dt id="arg-end-dim">end_dim<a class="anchor" aria-label="anchor" href="#arg-end-dim"></a></dt>
 <dd><p>last dim to flatten (default = -1).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: <code>(*, S_start,..., S_i, ..., S_end, *)</code>,
 where <code>S_i</code> is the size at dimension <code>i</code> and <code>*</code> means any
@@ -125,41 +109,41 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 1 to 10-0.8015 -1.0448  0.4715 -0.0156  0.7085  0.6113 -0.5675 -0.7171 -0.2615  1.2159</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.3857  1.2484 -0.5993  0.3149  1.0410 -0.0031 -0.1256 -1.4730 -0.4148  0.2949</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6647  2.6382 -1.9189  1.5107  0.1844 -0.4771 -0.1499  0.6239 -0.2575  0.1938</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0218 -0.1541  0.3580 -0.6831 -0.4747  1.5348  0.7168  0.3497 -0.4437  0.5037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5272 -1.2138 -2.3443 -0.6669  0.8383  1.6540 -0.5365  0.3552  1.8786 -1.7464</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6394 -0.0746 -0.3072 -0.5264 -0.9450  1.0271  0.0991  0.0619  0.3204 -0.2456</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0250  1.0297 -1.4002  1.7605  1.5106 -2.4053 -0.9495 -0.1437  0.0332  1.5030</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5799  0.0074  1.2746  1.4900 -1.0154  0.3608 -0.8096 -0.9809  0.0256  2.0557</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3317  0.3536  0.8048  0.7115  0.3078  1.2502  1.4476 -0.4187 -1.0832 -0.8844</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0850 -0.6192  0.1627 -1.9400 -0.6659  1.2134  0.2179  0.5830 -0.6183  0.0321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6692  0.7485 -0.8128  1.0693 -0.9317  0.9081 -0.7930 -0.7436 -0.6327  0.9231</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7045  0.1461  0.3835  1.6954 -0.1849 -0.2247  0.6355 -0.5568  0.3298  1.1491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1119 -0.1293 -0.1382  1.6020 -0.7861  0.8450  1.0183 -0.0673  0.3050  0.9445</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.8077 -0.1964 -0.1349  1.0182  0.7130 -0.4587 -2.3798  0.3252  1.3848 -0.7724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2489  0.5840  0.1690 -0.9544  1.1001 -0.4978 -1.2585 -0.9969  0.0469  0.6774</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7650 -0.4240 -0.3131  2.0314  1.0105  1.0466  0.2259 -0.2248  1.6021  1.5515</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8189 -0.2861 -0.1885 -0.1888  1.2369 -0.8073  1.0088  0.8614  0.9339  0.4690</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8568  1.5507 -1.4375  1.7597  0.7363 -1.4567  2.2990  1.3478  1.4151  0.4128</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1707  0.9011 -1.6190  0.0704  0.8973 -1.6506  0.3608  0.0830  1.6077 -1.5901</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0287 -0.4341  0.0636 -0.4749  0.1226 -1.1832 -0.0746  0.9812 -1.4514 -0.0938</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0018  1.7226 -0.3270  1.1839 -0.4936 -0.8200  0.0203 -0.7399  0.3987  0.4709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1172  0.9947  0.4600  1.3165 -1.3755  0.4174 -2.3616 -1.1633 -0.0096 -1.6175</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8800 -0.6760 -0.3397  1.4769 -0.1437  1.3507  0.3871 -0.4962 -1.1812  0.5646</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3333 -0.1028 -0.1121  0.2401 -0.9883  1.6939 -0.7209  1.0521 -0.3183 -0.0213</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.6093 -1.1583 -0.8108  0.3671 -0.3561  1.3556  0.1852 -0.6264  1.0287  0.8352</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8922  1.2746  0.8763 -0.0620 -0.4186 -0.4048  0.1820 -2.4252 -0.5739 -0.7585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6924 -0.4010 -0.0632 -0.6726  1.6817  0.9732 -0.9486 -1.2342 -1.1307  1.6650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1404  1.6418  0.8507 -1.0957 -0.4790  1.2576  2.6430  1.4992  1.5525 -1.8223</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1169 -1.0268  1.0565 -0.2669 -0.2602  1.5338 -0.1799 -0.2940 -2.9724 -0.5963</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2384  1.6367  0.3955 -0.0629  1.3515 -0.0084  1.0178 -0.1979  0.3229  0.7691</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 1 to 10-0.3199 -0.5470  0.6900 -0.4410  0.7617  0.1433 -0.9380  0.3535 -1.2080  0.1720</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3672  0.3245  2.4987  0.0329 -0.2421  1.7749 -0.3858  0.0299 -1.5719 -1.0935</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1906  1.1518 -0.8113  0.7621 -0.2383  0.6764 -1.0068  0.3444 -0.9040  0.3299</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5682  1.0434 -1.4358 -0.6868  0.1968  0.9005 -0.0376  1.1348 -0.4826 -1.2142</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1885  1.0115  0.0010  0.3205 -0.9197 -2.8422  0.0036 -1.0949 -0.4103 -2.4228</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1616  0.9570 -0.9730 -0.5115 -0.7795 -0.6466  1.2477  1.9586  1.5921  0.3300</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2595  1.1271 -0.5637 -0.2949 -1.3485  0.6355  1.6048  0.0823 -1.3432  1.5985</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0718 -0.4439  1.4137  0.3199  0.4508 -1.1021 -0.7063  0.1385  0.1935 -0.0515</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4836 -1.0455 -0.0227  0.0323  2.5342  0.5683  0.6712  1.1591 -1.5967  0.7863</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0818 -1.7147 -0.4228 -0.6275  0.3657 -1.3283  0.2849  1.2881  0.0971  1.0030</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1574  0.3463 -0.1375  0.8296 -1.7232 -0.7998 -1.2898  0.7290 -0.3915 -0.0916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4269 -1.1700  1.5849  1.6556 -0.2754  2.1463 -0.4575  0.1554  1.1298 -1.4076</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2696 -0.2911  1.5270  0.0218 -0.1632  0.6899 -0.5760 -0.4658  1.7844 -0.7699</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9222 -0.6399 -1.0585  1.7109 -0.5417  0.5917 -0.0074  1.2141 -0.0365 -0.6880</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5276 -0.2794 -1.1153 -1.7366  0.0878  2.1912  0.1281 -0.2582  0.3688 -0.6942</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1641 -1.8687  0.2645 -1.0567  0.8558  1.7184  0.1966 -0.4893  1.3731 -0.2916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2796 -0.0801 -2.6216  1.2877 -0.7383  1.4559 -0.7376 -0.8191  0.9838 -0.2870</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5289  0.2997  0.3484  0.4236  2.0946 -0.5954 -0.9860  1.9025  1.1765  2.6436</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6257  0.7267 -1.3255 -1.3947  0.3862  0.4103  0.1376 -0.3802 -1.6978 -0.8255</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1994  1.1267 -0.5165  0.2751  0.3473 -0.2350 -2.8236  0.4303  0.1155  0.1129</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1642 -0.2489  0.1635 -1.0925  0.6888 -0.7258 -0.4762 -0.4694 -0.3709 -0.4298</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7046 -0.1109 -0.0571  0.2755  1.1823 -0.2505  0.4844 -0.0388  0.8161  0.0671</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1539  1.1621 -0.4768 -0.2405  1.7020  1.4349  1.3076 -0.0111  0.6385  1.2546</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2940 -0.5328  0.2567 -1.5142 -2.0561 -0.1865 -0.1219  0.9133 -0.2866 -0.7963</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6937 -0.0177 -0.7141 -0.9364 -1.3136 -0.8536 -0.8504 -0.5033  0.1461  0.9744</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5276 -0.4393  0.8308  1.0943  0.1384 -0.4463  0.8638  0.4720  0.5617 -0.6370</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4416 -0.0977 -0.4188  0.0910  0.4841 -1.3073 -0.7666 -0.2519 -1.9687 -0.2757</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4955 -0.4640  0.4704  0.0696  0.7688  2.1967 -0.0799 -1.0043 -1.7932  0.0867</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5049 -1.3557  0.7561  0.8408 -0.8754  0.2716  0.0061  0.0291 -0.7523 -1.2831</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1470 -1.1432 -0.5351 -0.7528 -0.4445 -0.5141  0.7747  1.7050  0.7797 -0.8719</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{32,25} ]</span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_fractional_max_pool2d.Rd</code></div>
     </div>
 
@@ -103,22 +85,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over.
 Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the image of the form <code>oH x oW</code>.
 Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></dd>
 
 
-<dt>output_ratio</dt>
+<dt id="arg-output-ratio">output_ratio<a class="anchor" aria-label="anchor" href="#arg-output-ratio"></a></dt>
 <dd><p>If one wants to have an output size as a ratio of the input size, this option can be given.
 This has to be a number or tuple in the range (0, 1)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
 Useful to pass to <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></dd>
 
@@ -142,7 +126,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -151,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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index f727e849c5..934b300b31 100644
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@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_fractional_max_pool3d.Rd</code></div>
     </div>
 
@@ -103,22 +85,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over.
 Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the image of the form <code>oT x oH x oW</code>.
 Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></dd>
 
 
-<dt>output_ratio</dt>
+<dt id="arg-output-ratio">output_ratio<a class="anchor" aria-label="anchor" href="#arg-output-ratio"></a></dt>
 <dd><p>If one wants to have an output size as a ratio of the input size, this option can be given.
 This has to be a number or tuple in the range (0, 1)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the indices along with the outputs.
 Useful to pass to <code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></dd>
 
@@ -142,7 +126,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>GELU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>GELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_gelu.Rd</code></div>
     </div>
 
@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>approximate</dt>
+
+
+<dl><dt id="arg-approximate">approximate<a class="anchor" aria-label="anchor" href="#arg-approximate"></a></dt>
 <dd><p>the gelu approximation algorithm to use: <code>'none'</code> or <code>'tanh'</code>.
 Default: <code>'none'</code>.</p></dd>
 
@@ -109,7 +93,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_glu.html b/reference/nn_glu.html
index 5d00f5287d..c05f6e1d1b 100644
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@@ -1,12 +1,9 @@
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 \({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>GLU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>GLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_glu.Rd</code></div>
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@@ -101,13 +83,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dim</dt>
+
+
+<dl><dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int): the dimension on which to split the input. Default: -1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((\ast_1, N, \ast_2)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_group_norm.html b/reference/nn_group_norm.html
index 6380358602..b844a83174 100644
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+++ b/reference/nn_group_norm.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Group normalization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-normalization.R" class="external-link"><code>R/nn-normalization.R</code></a></small>
+
+      <h1>Group normalization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-normalization.R" class="external-link"><code>R/nn-normalization.R</code></a></small>
       <div class="d-none name"><code>nn_group_norm.Rd</code></div>
     </div>
 
@@ -98,19 +80,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_groups</dt>
+
+
+<dl><dt id="arg-num-groups">num_groups<a class="anchor" aria-label="anchor" href="#arg-num-groups"></a></dt>
 <dd><p>(int): number of groups to separate the channels into</p></dd>
 
 
-<dt>num_channels</dt>
+<dt id="arg-num-channels">num_channels<a class="anchor" aria-label="anchor" href="#arg-num-channels"></a></dt>
 <dd><p>(int): number of channels expected in input</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
 
 
-<dt>affine</dt>
+<dt id="arg-affine">affine<a class="anchor" aria-label="anchor" href="#arg-affine"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this module
 has learnable per-channel affine parameters initialized to ones (for weights)
 and zeros (for biases). Default: <code>TRUE</code>.</p></dd>
@@ -136,7 +120,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, *)\) where \(C=\mbox{num\_channels}\)</p></li>
 <li><p>Output: \((N, C, *)\)` (same shape as input)</p></li>
@@ -158,7 +142,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -167,14 +151,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_gru.html b/reference/nn_gru.html
index e7449bcd8d..a5f94c964d 100644
--- a/reference/nn_gru.html
+++ b/reference/nn_gru.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru"><meta name="description" content="For each element in the input sequence, each layer computes the following
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
+
+      <h1>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_gru.Rd</code></div>
     </div>
 
@@ -107,42 +89,44 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input_size</dt>
+
+
+<dl><dt id="arg-input-size">input_size<a class="anchor" aria-label="anchor" href="#arg-input-size"></a></dt>
 <dd><p>The number of expected features in the input <code>x</code></p></dd>
 
 
-<dt>hidden_size</dt>
+<dt id="arg-hidden-size">hidden_size<a class="anchor" aria-label="anchor" href="#arg-hidden-size"></a></dt>
 <dd><p>The number of features in the hidden state <code>h</code></p></dd>
 
 
-<dt>num_layers</dt>
+<dt id="arg-num-layers">num_layers<a class="anchor" aria-label="anchor" href="#arg-num-layers"></a></dt>
 <dd><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
 would mean stacking two GRUs together to form a <code>stacked GRU</code>,
 with the second GRU taking in outputs of the first GRU and
 computing the final results. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and <code>b_hh</code>.
 Default: <code>TRUE</code></p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>If <code>TRUE</code>, then the input and output tensors are provided
 as (batch, seq, feature). Default: <code>FALSE</code></p></dd>
 
 
-<dt>dropout</dt>
+<dt id="arg-dropout">dropout<a class="anchor" aria-label="anchor" href="#arg-dropout"></a></dt>
 <dd><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
 GRU layer except the last layer, with dropout probability equal to
 <code>dropout</code>. Default: 0</p></dd>
 
 
-<dt>bidirectional</dt>
+<dt id="arg-bidirectional">bidirectional<a class="anchor" aria-label="anchor" href="#arg-bidirectional"></a></dt>
 <dd><p>If <code>TRUE</code>, becomes a bidirectional GRU. Default: <code>FALSE</code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused.</p></dd>
 
 </dl></div>
@@ -169,7 +153,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
-    
+
 
 
 <p>Inputs: input, h_0</p><ul><li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
@@ -182,7 +166,7 @@ <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a><
 </ul></div>
     <div class="section level2">
     <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></a></h2>
-    
+
 
 
 <p>Outputs: output, h_n</p><ul><li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
@@ -200,7 +184,7 @@ <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p><code>weight_ih_l[k]</code> : the learnable input-hidden weights of the \(\mbox{k}^{th}\) layer
 (W_ir|W_iz|W_in), of shape <code>(3*hidden_size x input_size)</code></p></li>
@@ -223,7 +207,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -232,14 +216,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 26fb80d853..7202c9bbb2 100644
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-      <img src="" class="logo" alt=""><h1>Hardshwink module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
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@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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+<dl><dt id="arg-lambd">lambd<a class="anchor" aria-label="anchor" href="#arg-lambd"></a></dt>
 <dd><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></dd>
 
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-    
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 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
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diff --git a/reference/nn_hardsigmoid.html b/reference/nn_hardsigmoid.html
index fcba880a34..17ebd109b8 100644
--- a/reference/nn_hardsigmoid.html
+++ b/reference/nn_hardsigmoid.html
@@ -1,8 +1,5 @@
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
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@@ -106,7 +88,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -118,16 +100,16 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">m</span> <span class="op">&lt;-</span> <span class="fu">nn_hardswish</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">output</span> <span class="op">&lt;-</span> <span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -136,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/nn_hardtanh.html b/reference/nn_hardtanh.html
index 29b409c27d..790ec3cc99 100644
--- a/reference/nn_hardtanh.html
+++ b/reference/nn_hardtanh.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the HardTanh function element-wise
-HardTanh is defined as:"><title>Hardtanh module — nn_hardtanh • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Hardtanh module — nn_hardtanh"><meta property="og:description" content="Applies the HardTanh function element-wise
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hardtanh module — nn_hardtanh • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Hardtanh module — nn_hardtanh"><meta name="description" content="Applies the HardTanh function element-wise
+HardTanh is defined as:"><meta property="og:description" content="Applies the HardTanh function element-wise
+HardTanh is defined as:"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -12,77 +9,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Hardtanh module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Hardtanh module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_hardtanh.Rd</code></div>
     </div>
 
@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>min_val</dt>
+
+
+<dl><dt id="arg-min-val">min_val<a class="anchor" aria-label="anchor" href="#arg-min-val"></a></dt>
 <dd><p>minimum value of the linear region range. Default: -1</p></dd>
 
 
-<dt>max_val</dt>
+<dt id="arg-max-val">max_val<a class="anchor" aria-label="anchor" href="#arg-max-val"></a></dt>
 <dd><p>maximum value of the linear region range. Default: 1</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -125,7 +109,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/nn_hinge_embedding_loss.html b/reference/nn_hinge_embedding_loss.html
index 7503d0fd79..4829ae1495 100644
--- a/reference/nn_hinge_embedding_loss.html
+++ b/reference/nn_hinge_embedding_loss.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Hinge embedding loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Hinge embedding loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_hinge_embedding_loss.Rd</code></div>
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@@ -98,11 +80,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>margin</dt>
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 <dd><p>(float, optional): Has a default value of <code>1</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -132,7 +116,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\) where \(*\) means, any number of dimensions. The sum operation
 operates over all the elements.</p></li>
@@ -140,7 +124,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then same shape as the input</p></li>
 </ul></div>
 
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diff --git a/reference/nn_identity.html b/reference/nn_identity.html
index 655790442d..49b9e8f06e 100644
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diff --git a/reference/nn_init_calculate_gain.html b/reference/nn_init_calculate_gain.html
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       <div class="d-none name"><code>nn_init_calculate_gain.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>nonlinearity</dt>
+
+
+<dl><dt id="arg-nonlinearity">nonlinearity<a class="anchor" aria-label="anchor" href="#arg-nonlinearity"></a></dt>
 <dd><p>the non-linear function</p></dd>
 
 
-<dt>param</dt>
+<dt id="arg-param">param<a class="anchor" aria-label="anchor" href="#arg-param"></a></dt>
 <dd><p>optional parameter for the non-linear function</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -113,14 +97,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nn_init_constant_.html b/reference/nn_init_constant_.html
index 5a3d3699c2..b25b638d13 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Constant initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Constant initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_constant_.Rd</code></div>
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@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>Tensor</code></p></dd>
 
 
-<dt>val</dt>
+<dt id="arg-val">val<a class="anchor" aria-label="anchor" href="#arg-val"></a></dt>
 <dd><p>the value to fill the tensor with</p></dd>
 
 </dl></div>
@@ -117,7 +101,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
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diff --git a/reference/nn_init_dirac_.html b/reference/nn_init_dirac_.html
index df985424c5..043ad0d495 100644
--- a/reference/nn_init_dirac_.html
+++ b/reference/nn_init_dirac_.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the {3, 4, 5}-dimensional input Tensor with the Dirac
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 delta function. Preserves the identity of the inputs in Convolutional
 layers, where as many input channels are preserved as possible. In case
-of groups&amp;gt;1, each group of channels preserves identity."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+of groups&amp;gt;1, each group of channels preserves identity."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Dirac initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Dirac initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_dirac_.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>a {3, 4, 5}-dimensional <code>torch.Tensor</code></p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(optional) number of groups in the conv layer (default: 1)</p></dd>
 
 </dl></div>
@@ -116,15 +100,15 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">16</span>, <span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">nn_init_dirac_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -133,14 +117,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_init_eye_.html b/reference/nn_init_eye_.html
index ad1d42f4bd..8b6cb231b6 100644
--- a/reference/nn_init_eye_.html
+++ b/reference/nn_init_eye_.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the 2-dimensional input Tensor with the identity matrix.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Eye initialization — nn_init_eye_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Eye initialization — nn_init_eye_"><meta name="description" content="Fills the 2-dimensional input Tensor with the identity matrix.
 Preserves the identity of the inputs in Linear layers, where as
-many inputs are preserved as possible."><title>Eye initialization — nn_init_eye_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Eye initialization — nn_init_eye_"><meta property="og:description" content="Fills the 2-dimensional input Tensor with the identity matrix.
+many inputs are preserved as possible."><meta property="og:description" content="Fills the 2-dimensional input Tensor with the identity matrix.
 Preserves the identity of the inputs in Linear layers, where as
-many inputs are preserved as possible."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+many inputs are preserved as possible."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -14,77 +11,62 @@
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Eye initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Eye initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_eye_.Rd</code></div>
     </div>
 
@@ -101,7 +83,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>a 2-dimensional torch tensor.</p></dd>
 
 </dl></div>
@@ -119,7 +103,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
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@@ -128,14 +112,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_init_kaiming_normal_.html b/reference/nn_init_kaiming_normal_.html
index 25a650a673..065be9d346 100644
--- a/reference/nn_init_kaiming_normal_.html
+++ b/reference/nn_init_kaiming_normal_.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the input Tensor with values according to the method
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Kaiming normal initialization — nn_init_kaiming_normal_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Kaiming normal initialization — nn_init_kaiming_normal_"><meta name="description" content="Fills the input Tensor with values according to the method
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+normal distribution."><meta property="og:description" content="Fills the input Tensor with values according to the method
 described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a
-normal distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Kaiming normal initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Kaiming normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_kaiming_normal_.Rd</code></div>
     </div>
 
@@ -106,22 +88,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>torch.Tensor</code></p></dd>
 
 
-<dt>a</dt>
+<dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>the negative slope of the rectifier used after this layer (only used
 with <code>'leaky_relu'</code>)</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
 the magnitude of the variance of the weights in the forward pass. Choosing
 'fan_out' preserves the magnitudes in the backwards pass.</p></dd>
 
 
-<dt>nonlinearity</dt>
+<dt id="arg-nonlinearity">nonlinearity<a class="anchor" aria-label="anchor" href="#arg-nonlinearity"></a></dt>
 <dd><p>the non-linear function. recommended to use only with 'relu'
 or 'leaky_relu' (default).</p></dd>
 
@@ -134,13 +118,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_kaiming_normal_</span><span class="op">(</span><span class="va">w</span>, mode <span class="op">=</span> <span class="st">"fan_in"</span>, nonlinearity <span class="op">=</span> <span class="st">"leaky_relu"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5483  0.6051 -0.1700  0.4698 -0.3578</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0111  0.0262  0.0239 -0.5470 -1.0493</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7398  0.0686  0.7970 -0.6537 -0.3586</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0786 -1.0334  0.2745  0.0530  1.2171</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8785  0.2499 -0.2100  0.2391 -0.0418</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3469 -0.3616  0.1409 -0.8057 -0.3413</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_init_kaiming_uniform_.html b/reference/nn_init_kaiming_uniform_.html
index 60b4bfff2b..00dc0eb664 100644
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-uniform distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Kaiming uniform initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Kaiming uniform initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_kaiming_uniform_.Rd</code></div>
     </div>
 
@@ -106,22 +88,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>torch.Tensor</code></p></dd>
 
 
-<dt>a</dt>
+<dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>the negative slope of the rectifier used after this layer (only used
 with <code>'leaky_relu'</code>)</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
 the magnitude of the variance of the weights in the forward pass. Choosing
 'fan_out' preserves the magnitudes in the backwards pass.</p></dd>
 
 
-<dt>nonlinearity</dt>
+<dt id="arg-nonlinearity">nonlinearity<a class="anchor" aria-label="anchor" href="#arg-nonlinearity"></a></dt>
 <dd><p>the non-linear function. recommended to use only with 'relu'
 or 'leaky_relu' (default).</p></dd>
 
@@ -134,13 +118,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_kaiming_uniform_</span><span class="op">(</span><span class="va">w</span>, mode <span class="op">=</span> <span class="st">"fan_in"</span>, nonlinearity <span class="op">=</span> <span class="st">"leaky_relu"</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6448 -0.9118 -0.6907 -0.7555  0.7679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2540 -0.9912 -0.8890  0.7258  0.0094</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8867  0.6021 -0.3258 -0.0241  0.6353</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9239 -1.0055  0.5509 -0.7905 -0.9259</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0518  1.0741 -0.9186  0.7302 -0.5758</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4865  0.1697  0.6175  0.7713 -0.2031</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_init_normal_.html b/reference/nn_init_normal_.html
index 50111e6716..636fdf02ca 100644
--- a/reference/nn_init_normal_.html
+++ b/reference/nn_init_normal_.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Normal initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_normal_.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional Tensor</p></dd>
 
 
-<dt>mean</dt>
+<dt id="arg-mean">mean<a class="anchor" aria-label="anchor" href="#arg-mean"></a></dt>
 <dd><p>the mean of the normal distribution</p></dd>
 
 
-<dt>std</dt>
+<dt id="arg-std">std<a class="anchor" aria-label="anchor" href="#arg-std"></a></dt>
 <dd><p>the standard deviation of the normal distribution</p></dd>
 
 </dl></div>
@@ -115,13 +99,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_normal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1478  0.3302  1.4379  0.8988 -0.1674</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1804  1.2466 -1.3061 -0.3345  0.2397</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5363 -1.3692 -0.2766 -1.2271 -1.0775</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3168 -1.1078  0.8658 -0.6269  0.5245</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2218  2.9325  0.1645 -0.6746  1.0336</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3619 -0.2167 -0.5824  1.8980 -1.6161</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -130,14 +114,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_init_ones_.html b/reference/nn_init_ones_.html
index 7502b90025..8f6c24aab4 100644
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+++ b/reference/nn_init_ones_.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Ones initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Ones initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_ones_.Rd</code></div>
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@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>Tensor</code></p></dd>
 
 </dl></div>
@@ -113,7 +97,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/nn_init_orthogonal_.html b/reference/nn_init_orthogonal_.html
index 517677e885..b0ed215ef8 100644
--- a/reference/nn_init_orthogonal_.html
+++ b/reference/nn_init_orthogonal_.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Orthogonal initialization — nn_init_orthogonal_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Orthogonal initialization — nn_init_orthogonal_"><meta name="description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
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-trailing dimensions are flattened."><title>Orthogonal initialization — nn_init_orthogonal_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Orthogonal initialization — nn_init_orthogonal_"><meta property="og:description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
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 described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Orthogonal initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Orthogonal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_orthogonal_.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>Tensor</code></p></dd>
 
 
-<dt>gain</dt>
+<dt id="arg-gain">gain<a class="anchor" aria-label="anchor" href="#arg-gain"></a></dt>
 <dd><p>optional scaling factor</p></dd>
 
 </dl></div>
@@ -120,13 +104,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_orthogonal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1160 -0.2048 -0.9706  0.0430  0.0257</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7007  0.1632 -0.0854  0.4108  0.5535</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0030  0.3251 -0.0535  0.7074 -0.6253</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7160  0.3257  0.3196  0.5157 -0.1145</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0487 -0.4434  0.6289 -0.3018 -0.5607</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0492 -0.6043  0.3151  0.3915  0.6163</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/nn_init_sparse_.html b/reference/nn_init_sparse_.html
index cfcd09b1d8..25fb9a0106 100644
--- a/reference/nn_init_sparse_.html
+++ b/reference/nn_init_sparse_.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the 2D input Tensor as a sparse matrix, where the
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Sparse initialization — nn_init_sparse_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Sparse initialization — nn_init_sparse_"><meta name="description" content="Fills the 2D input Tensor as a sparse matrix, where the
 non-zero elements will be drawn from the normal distribution
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+as described in Deep learning via Hessian-free optimization - Martens, J. (2010)."><meta property="og:description" content="Fills the 2D input Tensor as a sparse matrix, where the
 non-zero elements will be drawn from the normal distribution
-as described in Deep learning via Hessian-free optimization - Martens, J. (2010)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+as described in Deep learning via Hessian-free optimization - Martens, J. (2010)."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sparse initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Sparse initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_sparse_.Rd</code></div>
     </div>
 
@@ -101,15 +83,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>Tensor</code></p></dd>
 
 
-<dt>sparsity</dt>
+<dt id="arg-sparsity">sparsity<a class="anchor" aria-label="anchor" href="#arg-sparsity"></a></dt>
 <dd><p>The fraction of elements in each column to be set to zero</p></dd>
 
 
-<dt>std</dt>
+<dt id="arg-std">std<a class="anchor" aria-label="anchor" href="#arg-std"></a></dt>
 <dd><p>the standard deviation of the normal distribution used to generate
 the non-zero values</p></dd>
 
@@ -118,14 +102,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">w</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">5</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">nn_init_sparse_</span><span class="op">(</span><span class="va">w</span>, sparsity <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
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diff --git a/reference/nn_init_trunc_normal_.html b/reference/nn_init_trunc_normal_.html
index 3dd5c454a1..736bafb098 100644
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@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Truncated normal initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Truncated normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_trunc_normal_.Rd</code></div>
     </div>
 
@@ -98,23 +80,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional Tensor</p></dd>
 
 
-<dt>mean</dt>
+<dt id="arg-mean">mean<a class="anchor" aria-label="anchor" href="#arg-mean"></a></dt>
 <dd><p>the mean of the normal distribution</p></dd>
 
 
-<dt>std</dt>
+<dt id="arg-std">std<a class="anchor" aria-label="anchor" href="#arg-std"></a></dt>
 <dd><p>the standard deviation of the normal distribution</p></dd>
 
 
-<dt>a</dt>
+<dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>the minimum cutoff value</p></dd>
 
 
-<dt>b</dt>
+<dt id="arg-b">b<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>the maximum cutoff value</p></dd>
 
 </dl></div>
@@ -126,13 +110,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_trunc_normal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6650 -0.5936 -0.4173 -0.3491 -0.2854</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7031 -0.3730  0.4309 -0.8737  0.2055</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7119  1.1223 -1.3891  1.1680 -0.0479</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3975  0.9598 -0.1321  0.2450  0.9107</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0950  1.1163 -1.3789  1.0975 -1.2351</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2719  0.0179 -0.1226  1.2499  1.3921</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_init_uniform_.html b/reference/nn_init_uniform_.html
index cf558ed2df..0d0153e9db 100644
--- a/reference/nn_init_uniform_.html
+++ b/reference/nn_init_uniform_.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Uniform initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Uniform initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_uniform_.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional Tensor</p></dd>
 
 
-<dt>a</dt>
+<dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>the lower bound of the uniform distribution</p></dd>
 
 
-<dt>b</dt>
+<dt id="arg-b">b<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>the upper bound of the uniform distribution</p></dd>
 
 </dl></div>
@@ -115,13 +99,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_uniform_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9775  0.7917  0.6477  0.2337  0.0261</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9267  0.4705  0.3090  0.4401  0.1671</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3938  0.8689  0.3312  0.1128  0.8623</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0434  0.1934  0.2546  0.7549  0.8938</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9165  0.1504  0.3006  0.9083  0.1952</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9987  0.8478  0.9906  0.1976  0.7061</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -130,14 +114,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_init_xavier_normal_.html b/reference/nn_init_xavier_normal_.html
index d539197490..0b5a42f001 100644
--- a/reference/nn_init_xavier_normal_.html
+++ b/reference/nn_init_xavier_normal_.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the input Tensor with values according to the method
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Xavier normal initialization — nn_init_xavier_normal_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Xavier normal initialization — nn_init_xavier_normal_"><meta name="description" content="Fills the input Tensor with values according to the method
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+distribution."><meta property="og:description" content="Fills the input Tensor with values according to the method
 described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. &amp;amp; Bengio, Y. (2010), using a normal
-distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Xavier normal initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Xavier normal initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_xavier_normal_.Rd</code></div>
     </div>
 
@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>Tensor</code></p></dd>
 
 
-<dt>gain</dt>
+<dt id="arg-gain">gain<a class="anchor" aria-label="anchor" href="#arg-gain"></a></dt>
 <dd><p>an optional scaling factor</p></dd>
 
 </dl></div>
@@ -117,13 +101,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_xavier_normal_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1958 -1.2098 -0.3898 -0.4418  0.3574</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2345  0.5271  0.5841 -0.1748  0.3071</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8960 -0.1640 -0.1738  0.7133  0.8885</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2405  0.0297  0.1791 -1.1906  0.5827</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2368 -0.3485  0.4034  0.3764 -0.2217</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0696  0.0207 -0.4911 -0.1007  0.1210</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
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   </body></html>
 
diff --git a/reference/nn_init_xavier_uniform_.html b/reference/nn_init_xavier_uniform_.html
index 539664b263..e327f0f603 100644
--- a/reference/nn_init_xavier_uniform_.html
+++ b/reference/nn_init_xavier_uniform_.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Fills the input Tensor with values according to the method
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Xavier uniform initialization — nn_init_xavier_uniform_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Xavier uniform initialization — nn_init_xavier_uniform_"><meta name="description" content="Fills the input Tensor with values according to the method
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+distribution."><meta property="og:description" content="Fills the input Tensor with values according to the method
 described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. &amp;amp; Bengio, Y. (2010), using a uniform
-distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Xavier uniform initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Xavier uniform initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_xavier_uniform_.Rd</code></div>
     </div>
 
@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional <code>Tensor</code></p></dd>
 
 
-<dt>gain</dt>
+<dt id="arg-gain">gain<a class="anchor" aria-label="anchor" href="#arg-gain"></a></dt>
 <dd><p>an optional scaling factor</p></dd>
 
 </dl></div>
@@ -117,13 +101,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">nn_init_xavier_uniform_</span><span class="op">(</span><span class="va">w</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5071 -0.3531  0.1250  0.7562 -0.6302</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6892  0.6461  0.5584  0.7683 -0.3572</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5540 -0.8478 -0.5756  0.1296  0.1135</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0352  0.7292  0.5037  0.6919  0.0880</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6865 -0.4254  0.0470  0.6638 -0.7051</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6776 -0.6500 -0.7674 -0.1183  0.8304</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nn_init_zeros_.html b/reference/nn_init_zeros_.html
index a23b96b462..a0065d233d 100644
--- a/reference/nn_init_zeros_.html
+++ b/reference/nn_init_zeros_.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Zeros initialization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
+
+      <h1>Zeros initialization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-init.R" class="external-link"><code>R/nn-init.R</code></a></small>
       <div class="d-none name"><code>nn_init_zeros_.Rd</code></div>
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@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>an n-dimensional tensor</p></dd>
 
 </dl></div>
@@ -113,7 +97,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
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diff --git a/reference/nn_kl_div_loss.html b/reference/nn_kl_div_loss.html
index 1400482618..ba1a8246d1 100644
--- a/reference/nn_kl_div_loss.html
+++ b/reference/nn_kl_div_loss.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The Kullback-Leibler divergence loss measure
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Kullback-Leibler divergence loss — nn_kl_div_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Kullback-Leibler divergence loss — nn_kl_div_loss"><meta name="description" content="The Kullback-Leibler divergence loss measure
 Kullback-Leibler divergence
 is a useful distance measure for continuous distributions and is often
 useful when performing direct regression over the space of (discretely sampled)
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+continuous output distributions."><meta property="og:description" content="The Kullback-Leibler divergence loss measure
 Kullback-Leibler divergence
 is a useful distance measure for continuous distributions and is often
 useful when performing direct regression over the space of (discretely sampled)
-continuous output distributions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Kullback-Leibler divergence loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Kullback-Leibler divergence loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_kl_div_loss.Rd</code></div>
     </div>
 
@@ -107,7 +89,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>reduction</dt>
+
+
+<dl><dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'batchmean'</code> | <code>'sum'</code> | <code>'mean'</code>.
 <code>'none'</code>: no reduction will be applied.
@@ -155,7 +139,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
@@ -164,7 +148,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 the same shape as the input</p></li>
 </ul></div>
 
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diff --git a/reference/nn_l1_loss.html b/reference/nn_l1_loss.html
index 6b7e1b17fc..20f3c4661f 100644
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+++ b/reference/nn_l1_loss.html
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@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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+
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 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
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@@ -129,7 +113,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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+
 
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@@ -149,7 +133,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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-      <img src="" class="logo" alt=""><h1>Layer normalization</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-normalization.R" class="external-link"><code>R/nn-normalization.R</code></a></small>
+
+      <h1>Layer normalization</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-normalization.R" class="external-link"><code>R/nn-normalization.R</code></a></small>
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@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>normalized_shape</dt>
+
+
+<dl><dt id="arg-normalized-shape">normalized_shape<a class="anchor" aria-label="anchor" href="#arg-normalized-shape"></a></dt>
 <dd><p>(int or list): input shape from an expected input
 of size
 \([* \times \mbox{normalized\_shape}[0] \times \mbox{normalized\_shape}[1] \times \ldots \times \mbox{normalized\_shape}[-1]]\)
@@ -106,11 +90,11 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 normalize over the last dimension which is expected to be of that specific size.</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
 
 
-<dt>elementwise_affine</dt>
+<dt id="arg-elementwise-affine">elementwise_affine<a class="anchor" aria-label="anchor" href="#arg-elementwise-affine"></a></dt>
 <dd><p>a boolean value that when set to <code>TRUE</code>, this module
 has learnable per-element affine parameters initialized to ones (for weights)
 and zeros (for biases). Default: <code>TRUE</code>.</p></dd>
@@ -140,7 +124,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\)</p></li>
 <li><p>Output: \((N, *)\) (same shape as input)</p></li>
@@ -164,7 +148,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -173,14 +157,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_leaky_relu.html b/reference/nn_leaky_relu.html
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-      <img src="" class="logo" alt=""><h1>LeakyReLU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>LeakyReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_leaky_relu.Rd</code></div>
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@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>negative_slope</dt>
+
+
+<dl><dt id="arg-negative-slope">negative_slope<a class="anchor" aria-label="anchor" href="#arg-negative-slope"></a></dt>
 <dd><p>Controls the angle of the negative slope. Default: 1e-2</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
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@@ -120,7 +104,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -136,7 +120,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_linear.html b/reference/nn_linear.html
index 04119d710a..d2bd648a14 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Linear module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
+
+      <h1>Linear module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-linear.R" class="external-link"><code>R/nn-linear.R</code></a></small>
       <div class="d-none name"><code>nn_linear.Rd</code></div>
     </div>
 
@@ -95,22 +77,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>in_features</dt>
+
+
+<dl><dt id="arg-in-features">in_features<a class="anchor" aria-label="anchor" href="#arg-in-features"></a></dt>
 <dd><p>size of each input sample</p></dd>
 
 
-<dt>out_features</dt>
+<dt id="arg-out-features">out_features<a class="anchor" aria-label="anchor" href="#arg-out-features"></a></dt>
 <dd><p>size of each output sample</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
 Default: <code>TRUE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: <code>(N, *, H_in)</code> where <code>*</code> means any number of
 additional dimensions and <code>H_in = in_features</code>.</p></li>
@@ -119,7 +103,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight: the learnable weights of the module of shape
 <code>(out_features, in_features)</code>. The values are
@@ -142,7 +126,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 128  30</span>
 </code></pre></div>
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@@ -151,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/nn_log_sigmoid.html b/reference/nn_log_sigmoid.html
index 76b7c0b2da..9c808919b6 100644
--- a/reference/nn_log_sigmoid.html
+++ b/reference/nn_log_sigmoid.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LogSigmoid module — nn_log_sigmoid • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LogSigmoid module — nn_log_sigmoid"><meta name="description" content="Applies the element-wise function:
 $$
   \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
- $$"><title>LogSigmoid module — nn_log_sigmoid • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LogSigmoid module — nn_log_sigmoid"><meta property="og:description" content="Applies the element-wise function:
+ $$"><meta property="og:description" content="Applies the element-wise function:
 $$
   \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
- $$"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>LogSigmoid module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>LogSigmoid module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_log_sigmoid.Rd</code></div>
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@@ -104,7 +86,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
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 dimensions</p></li>
@@ -120,7 +102,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_log_softmax.html b/reference/nn_log_softmax.html
index a484c51d4b..ff5bdeba47 100644
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@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>LogSoftmax module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>LogSoftmax module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_log_softmax.Rd</code></div>
     </div>
 
@@ -98,15 +80,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dim</dt>
+
+
+<dl><dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int): A dimension along which LogSoftmax will be computed.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a Tensor of the same dimension and shape as the input with
+    <p>a Tensor of the same dimension and shape as the input with
 values in the range [-inf, 0)</p>
     </div>
     <div class="section level2">
@@ -117,7 +99,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -133,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_lp_pool1d.html b/reference/nn_lp_pool1d.html
index 8ed2cbd4c1..d508829219 100644
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-      <img src="" class="logo" alt=""><h1>Applies a 1D power-average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 1D power-average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_lp_pool1d.Rd</code></div>
     </div>
 
@@ -95,20 +77,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>norm_type</dt>
+
+
+<dl><dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>if inf than one gets max pooling if 0 you get sum pooling (
 proportional to the avg pooling)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>a single int, the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 </dl></div>
@@ -126,7 +110,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, L_{in})\)</p></li>
 <li><p>Output: \((N, C, L_{out})\), where</p></li>
@@ -145,7 +129,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -154,14 +138,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_lp_pool2d.html b/reference/nn_lp_pool2d.html
index c278167ba3..0f4ba85271 100644
--- a/reference/nn_lp_pool2d.html
+++ b/reference/nn_lp_pool2d.html
@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a 2D power-average pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 2D power-average pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_lp_pool2d.Rd</code></div>
     </div>
 
@@ -95,20 +77,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>norm_type</dt>
+
+
+<dl><dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>if inf than one gets max pooling if 0 you get sum pooling (
 proportional to the avg pooling)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 </dl></div>
@@ -118,8 +102,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
   f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
 $$</p><ul><li><p>At p = \(\infty\), one gets Max Pooling</p></li>
 <li><p>At p = 1, one gets Sum Pooling (which is proportional to average pooling)</p></li>
-</ul><p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+</ul><p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints – in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
@@ -129,7 +113,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
@@ -154,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_lstm.html b/reference/nn_lstm.html
index c999770109..bc6e795603 100644
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+++ b/reference/nn_lstm.html
@@ -1,10 +1,7 @@
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
+
+      <h1>Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_lstm.Rd</code></div>
     </div>
 
@@ -107,42 +89,44 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input_size</dt>
+
+
+<dl><dt id="arg-input-size">input_size<a class="anchor" aria-label="anchor" href="#arg-input-size"></a></dt>
 <dd><p>The number of expected features in the input <code>x</code></p></dd>
 
 
-<dt>hidden_size</dt>
+<dt id="arg-hidden-size">hidden_size<a class="anchor" aria-label="anchor" href="#arg-hidden-size"></a></dt>
 <dd><p>The number of features in the hidden state <code>h</code></p></dd>
 
 
-<dt>num_layers</dt>
+<dt id="arg-num-layers">num_layers<a class="anchor" aria-label="anchor" href="#arg-num-layers"></a></dt>
 <dd><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
 would mean stacking two LSTMs together to form a <code>stacked LSTM</code>,
 with the second LSTM taking in outputs of the first LSTM and
 computing the final results. Default: 1</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and <code>b_hh</code>.
 Default: <code>TRUE</code></p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>If <code>TRUE</code>, then the input and output tensors are provided
 as (batch, seq, feature). Default: <code>FALSE</code></p></dd>
 
 
-<dt>dropout</dt>
+<dt id="arg-dropout">dropout<a class="anchor" aria-label="anchor" href="#arg-dropout"></a></dt>
 <dd><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
 LSTM layer except the last layer, with dropout probability equal to
 <code>dropout</code>. Default: 0</p></dd>
 
 
-<dt>bidirectional</dt>
+<dt id="arg-bidirectional">bidirectional<a class="anchor" aria-label="anchor" href="#arg-bidirectional"></a></dt>
 <dd><p>If <code>TRUE</code>, becomes a bidirectional LSTM. Default: <code>FALSE</code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>currently unused.</p></dd>
 
 </dl></div>
@@ -172,7 +156,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
-    
+
 
 
 <p>Inputs: input, (h_0, c_0)</p><ul><li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
@@ -188,7 +172,7 @@ <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a><
     </div>
     <div class="section level2">
     <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></a></h2>
-    
+
 
 
 <p>Outputs: output, (h_n, c_n)</p><ul><li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
@@ -208,7 +192,7 @@ <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p><code>weight_ih_l[k]</code> : the learnable input-hidden weights of the \(\mbox{k}^{th}\) layer
 <code>(W_ii|W_if|W_ig|W_io)</code>, of shape <code>(4*hidden_size x input_size)</code></p></li>
@@ -231,7 +215,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -240,14 +224,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_margin_ranking_loss.html b/reference/nn_margin_ranking_loss.html
index 9067acd074..ca61970e37 100644
--- a/reference/nn_margin_ranking_loss.html
+++ b/reference/nn_margin_ranking_loss.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the loss given
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Margin ranking loss — nn_margin_ranking_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Margin ranking loss — nn_margin_ranking_loss"><meta name="description" content="Creates a criterion that measures the loss given
 inputs \(x1\), \(x2\), two 1D mini-batch Tensors,
 and a label 1D mini-batch tensor \(y\) (containing 1 or -1).
 If \(y = 1\) then it assumed the first input should be ranked higher
-(have a larger value) than the second input, and vice-versa for \(y = -1\)."><title>Margin ranking loss — nn_margin_ranking_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Margin ranking loss — nn_margin_ranking_loss"><meta property="og:description" content="Creates a criterion that measures the loss given
+(have a larger value) than the second input, and vice-versa for \(y = -1\)."><meta property="og:description" content="Creates a criterion that measures the loss given
 inputs \(x1\), \(x2\), two 1D mini-batch Tensors,
 and a label 1D mini-batch tensor \(y\) (containing 1 or -1).
 If \(y = 1\) then it assumed the first input should be ranked higher
-(have a larger value) than the second input, and vice-versa for \(y = -1\)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
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@@ -107,11 +89,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>margin</dt>
+
+
+<dl><dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>(float, optional): Has a default value of \(0\).</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -127,7 +111,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input1: \((N)\) where <code>N</code> is the batch size.</p></li>
 <li><p>Input2: \((N)\), same shape as the Input1.</p></li>
@@ -147,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>MaxPool1D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>MaxPool1D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
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@@ -105,28 +87,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on both sides</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>a parameter that controls the stride of elements in the window</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the max indices along with the outputs.
 Useful for  <code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> later.</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 </dl></div>
@@ -145,7 +129,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, L_{in})\)</p></li>
 <li><p>Output: \((N, C, L_{out})\), where</p></li>
@@ -165,7 +149,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -174,14 +158,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_max_pool2d.html b/reference/nn_max_pool2d.html
index 6b8822ba73..2e680285a3 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>MaxPool2D module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>MaxPool2D module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_max_pool2d.Rd</code></div>
     </div>
 
@@ -105,28 +87,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on both sides</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>a parameter that controls the stride of elements in the window</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the max indices along with the outputs.
 Useful for <code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> later.</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 </dl></div>
@@ -145,13 +129,13 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
 It is harder to describe, but this <code>link</code> has a nice visualization of what <code>dilation</code> does.</p>
-<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
-<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints – in which case, the first <code>int</code> is used for the height dimension,
 and the second <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
@@ -177,7 +161,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -186,14 +170,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_max_pool3d.html b/reference/nn_max_pool3d.html
index 884ce77820..d29829d750 100644
--- a/reference/nn_max_pool3d.html
+++ b/reference/nn_max_pool3d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d"><meta name="description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
 output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
-can be precisely described as:"><title>Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+can be precisely described as:"><meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
 output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
-can be precisely described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Applies a 3D max pooling over an input signal composed of several input planes.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Applies a 3D max pooling over an input signal composed of several input planes.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_max_pool3d.Rd</code></div>
     </div>
 
@@ -108,28 +90,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Default value is <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on all three sides</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>a parameter that controls the stride of elements in the window</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>TRUE</code>, will return the max indices along with the outputs.
 Useful for <code>torch_nn.MaxUnpool3d</code> later</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></dd>
 
 </dl></div>
@@ -144,13 +128,13 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
 for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
 It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.
-The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
-<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul><li><p>a single <code>int</code> – in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints – in which case, the first <code>int</code> is used for the depth dimension,
 the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where
@@ -180,7 +164,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -189,14 +173,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_max_unpool1d.html b/reference/nn_max_unpool1d.html
index 02d5bbc99b..bd11078053 100644
--- a/reference/nn_max_unpool1d.html
+++ b/reference/nn_max_unpool1d.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="MaxPool1d is not fully invertible, since the non-maximal values are lost.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes a partial inverse of MaxPool1d. — nn_max_unpool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool1d. — nn_max_unpool1d"><meta name="description" content="MaxPool1d is not fully invertible, since the non-maximal values are lost.
 MaxUnpool1d takes in as input the output of MaxPool1d
 including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero."><title>Computes a partial inverse of MaxPool1d. — nn_max_unpool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool1d. — nn_max_unpool1d"><meta property="og:description" content="MaxPool1d is not fully invertible, since the non-maximal values are lost.
+in which all non-maximal values are set to zero."><meta property="og:description" content="MaxPool1d is not fully invertible, since the non-maximal values are lost.
 MaxUnpool1d takes in as input the output of MaxPool1d
 including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_max_unpool1d.Rd</code></div>
     </div>
 
@@ -104,16 +86,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the max pooling window.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple): Stride of the max pooling window.
 It is set to <code>kernel_size</code> by default.</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple): Padding that was added to the input</p></dd>
 
 </dl></div>
@@ -127,7 +111,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
-    
+
 
 <ul><li><p><code>input</code>: the input Tensor to invert</p></li>
 <li><p><code>indices</code>: the indices given out by <code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code></p></li>
@@ -135,7 +119,7 @@ <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a><
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out})\), where
@@ -174,7 +158,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,8,1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -183,14 +167,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_max_unpool2d.html b/reference/nn_max_unpool2d.html
index bbaa10e3b7..39711a1737 100644
--- a/reference/nn_max_unpool2d.html
+++ b/reference/nn_max_unpool2d.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes a partial inverse of MaxPool2d. — nn_max_unpool2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool2d. — nn_max_unpool2d"><meta name="description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
 MaxUnpool2d takes in as input the output of MaxPool2d
 including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero."><title>Computes a partial inverse of MaxPool2d. — nn_max_unpool2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool2d. — nn_max_unpool2d"><meta property="og:description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
+in which all non-maximal values are set to zero."><meta property="og:description" content="MaxPool2d is not fully invertible, since the non-maximal values are lost.
 MaxUnpool2d takes in as input the output of MaxPool2d
 including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_max_unpool2d.Rd</code></div>
     </div>
 
@@ -104,16 +86,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the max pooling window.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple): Stride of the max pooling window.
 It is set to <code>kernel_size</code> by default.</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple): Padding that was added to the input</p></dd>
 
 </dl></div>
@@ -127,7 +111,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
-    
+
 
 <ul><li><p><code>input</code>: the input Tensor to invert</p></li>
 <li><p><code>indices</code>: the indices given out by <code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code></p></li>
@@ -135,7 +119,7 @@ <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a><
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, H_{out}, W_{out})\), where
@@ -163,15 +147,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.6845  0.0000  0.6537</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.7033  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  2.4918  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2663  0.0000  1.2793  0.0000  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  1.3880  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.7067  0.0000  0.0000  0.0000  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  0.0000  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,5,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -180,14 +164,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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diff --git a/reference/nn_max_unpool3d.html b/reference/nn_max_unpool3d.html
index cce4a3d6f6..175374ef4e 100644
--- a/reference/nn_max_unpool3d.html
+++ b/reference/nn_max_unpool3d.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="MaxPool3d is not fully invertible, since the non-maximal values are lost.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes a partial inverse of MaxPool3d. — nn_max_unpool3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool3d. — nn_max_unpool3d"><meta name="description" content="MaxPool3d is not fully invertible, since the non-maximal values are lost.
 MaxUnpool3d takes in as input the output of MaxPool3d
 including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero."><title>Computes a partial inverse of MaxPool3d. — nn_max_unpool3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes a partial inverse of MaxPool3d. — nn_max_unpool3d"><meta property="og:description" content="MaxPool3d is not fully invertible, since the non-maximal values are lost.
+in which all non-maximal values are set to zero."><meta property="og:description" content="MaxPool3d is not fully invertible, since the non-maximal values are lost.
 MaxUnpool3d takes in as input the output of MaxPool3d
 including the indices of the maximal values and computes a partial inverse
-in which all non-maximal values are set to zero."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
+
+      <h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-pooling.R" class="external-link"><code>R/nn-pooling.R</code></a></small>
       <div class="d-none name"><code>nn_max_unpool3d.Rd</code></div>
     </div>
 
@@ -104,16 +86,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>kernel_size</dt>
+
+
+<dl><dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>(int or tuple): Size of the max pooling window.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(int or tuple): Stride of the max pooling window.
 It is set to <code>kernel_size</code> by default.</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>(int or tuple): Padding that was added to the input</p></dd>
 
 </dl></div>
@@ -127,7 +111,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
-    
+
 
 <ul><li><p><code>input</code>: the input Tensor to invert</p></li>
 <li><p><code>indices</code>: the indices given out by <code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code></p></li>
@@ -135,7 +119,7 @@ <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a><
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
 <li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
@@ -165,7 +149,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 20 16 51 33 15</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -174,14 +158,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_module.html b/reference/nn_module.html
index 64d1fdbafd..f11e0b4729 100644
--- a/reference/nn_module.html
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Base class for all neural network modules.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Base class for all neural network modules.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>nn_module.Rd</code></div>
     </div>
 
@@ -102,27 +84,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>classname</dt>
+
+
+<dl><dt id="arg-classname">classname<a class="anchor" aria-label="anchor" href="#arg-classname"></a></dt>
 <dd><p>an optional name for the module</p></dd>
 
 
-<dt>inherit</dt>
+<dt id="arg-inherit">inherit<a class="anchor" aria-label="anchor" href="#arg-inherit"></a></dt>
 <dd><p>an optional module to inherit from</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>methods implementation</p></dd>
 
 
-<dt>private</dt>
+<dt id="arg-private">private<a class="anchor" aria-label="anchor" href="#arg-private"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 
-<dt>active</dt>
+<dt id="arg-active">active<a class="anchor" aria-label="anchor" href="#arg-active"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 
-<dt>parent_env</dt>
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
 <dd><p>passed to <code><a href="https://r6.r-lib.org/reference/R6Class.html" class="external-link">R6::R6Class()</a></code>.</p></dd>
 
 </dl></div>
@@ -135,7 +119,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="initialize">Initialize<a class="anchor" aria-label="anchor" href="#initialize"></a></h2>
-    
+
 
 
 <p>The initialize function will be called whenever a new instance of the <code>nn_module</code>
@@ -154,7 +138,7 @@ <h2 id="initialize">Initialize<a class="anchor" aria-label="anchor" href="#initi
     </div>
     <div class="section level2">
     <h2 id="forward">Forward<a class="anchor" aria-label="anchor" href="#forward"></a></h2>
-    
+
 
 
 <p>The forward method is called whenever an instance of <code>nn_module</code> is called.
@@ -174,7 +158,7 @@ <h2 id="forward">Forward<a class="anchor" aria-label="anchor" href="#forward"></
     </div>
     <div class="section level2">
     <h2 id="cloning">Cloning<a class="anchor" aria-label="anchor" href="#cloning"></a></h2>
-    
+
 
 <p>To finalize the cloning of a module, you can define a private <code>finalize_deep_clone()</code> method.
 This method is called on the cloned object when deep-cloning a module, after all the modules, parameters and
@@ -200,7 +184,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -209,14 +193,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_module_dict.html b/reference/nn_module_dict.html
index 272320dc84..86468e6ef2 100644
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-      <img src="" class="logo" alt=""><h1>Container that allows named values</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Container that allows named values</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>nn_module_dict.Rd</code></div>
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@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>dict</dt>
+
+
+<dl><dt id="arg-dict">dict<a class="anchor" aria-label="anchor" href="#arg-dict"></a></dt>
 <dd><p>A named list of submodules that will be saved in that module.</p></dd>
 
 </dl></div>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_module_list.html b/reference/nn_module_list.html
index 17b50d4e49..e3edf7716b 100644
--- a/reference/nn_module_list.html
+++ b/reference/nn_module_list.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="nn_module_list can be indexed like a regular R list, but
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Holds submodules in a list. — nn_module_list • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Holds submodules in a list. — nn_module_list"><meta name="description" content="nn_module_list can be indexed like a regular R list, but
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-      <img src="" class="logo" alt=""><h1>Holds submodules in a list.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Holds submodules in a list.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>nn_module_list.Rd</code></div>
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     <div class="section level2">
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-    <dl><dt>modules</dt>
+
+
+<dl><dt id="arg-modules">modules<a class="anchor" aria-label="anchor" href="#arg-modules"></a></dt>
 <dd><p>a list of modules to add</p></dd>
 
 </dl></div>
@@ -128,7 +112,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_mse_loss.html b/reference/nn_mse_loss.html
index 7a4ffbb52b..34367ff07b 100644
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+++ b/reference/nn_mse_loss.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the mean squared error (squared L2 norm) between
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>MSE loss — nn_mse_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="MSE loss — nn_mse_loss"><meta name="description" content="Creates a criterion that measures the mean squared error (squared L2 norm) between
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-      <img src="" class="logo" alt=""><h1>MSE loss</h1>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
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@@ -104,7 +86,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>reduction</dt>
+
+
+<dl><dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
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diff --git a/reference/nn_multi_margin_loss.html b/reference/nn_multi_margin_loss.html
index 2995f4f76e..a06bf2c357 100644
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@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that optimizes a multi-class classification hinge
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 loss (margin-based loss) between input \(x\) (a 2D mini-batch Tensor) and
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
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@@ -104,22 +86,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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 <dd><p>(float, optional): Has a default value of \(1\).</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional): a manual rescaling weight given to each
 class. If given, it has to be a Tensor of size <code>C</code>. Otherwise, it is
 treated as if having all ones.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -143,7 +127,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 $$</p>
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diff --git a/reference/nn_multihead_attention.html b/reference/nn_multihead_attention.html
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+representation subspaces. See reference: Attention Is All You Need"><meta property="og:description" content="Allows the model to jointly attend to information from different
+representation subspaces. See reference: Attention Is All You Need"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>MultiHead attention</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>MultiHead attention</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_multihead_attention.Rd</code></div>
     </div>
 
@@ -108,43 +90,45 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>embed_dim</dt>
+
+
+<dl><dt id="arg-embed-dim">embed_dim<a class="anchor" aria-label="anchor" href="#arg-embed-dim"></a></dt>
 <dd><p>total dimension of the model.</p></dd>
 
 
-<dt>num_heads</dt>
+<dt id="arg-num-heads">num_heads<a class="anchor" aria-label="anchor" href="#arg-num-heads"></a></dt>
 <dd><p>parallel attention heads. Note that <code>embed_dim</code> will be split
 across <code>num_heads</code> (i.e. each head will have dimension <code>embed_dim %/% num_heads</code>).</p></dd>
 
 
-<dt>dropout</dt>
+<dt id="arg-dropout">dropout<a class="anchor" aria-label="anchor" href="#arg-dropout"></a></dt>
 <dd><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>add bias as module parameter. Default: True.</p></dd>
 
 
-<dt>add_bias_kv</dt>
+<dt id="arg-add-bias-kv">add_bias_kv<a class="anchor" aria-label="anchor" href="#arg-add-bias-kv"></a></dt>
 <dd><p>add bias to the key and value sequences at dim=0.</p></dd>
 
 
-<dt>add_zero_attn</dt>
+<dt id="arg-add-zero-attn">add_zero_attn<a class="anchor" aria-label="anchor" href="#arg-add-zero-attn"></a></dt>
 <dd><p>add a new batch of zeros to the key and value sequences
 at dim=1.</p></dd>
 
 
-<dt>kdim</dt>
+<dt id="arg-kdim">kdim<a class="anchor" aria-label="anchor" href="#arg-kdim"></a></dt>
 <dd><p>total number of features in key. Default: <code>NULL</code></p></dd>
 
 
-<dt>vdim</dt>
+<dt id="arg-vdim">vdim<a class="anchor" aria-label="anchor" href="#arg-vdim"></a></dt>
 <dd><p>total number of features in value. Default: <code>NULL</code>. Note: if kdim
 and vdim are <code>NULL</code>, they will be set to embed_dim such that query, key,
 and value have the same number of features.</p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>if <code>TRUE</code> then the input and output tensors are \((N,
   S, E)\) instead of \((S, N, E)\), where N is the batch size, S is the
 sequence length, and E is the embedding dimension.</p></dd>
@@ -157,7 +141,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 
 <p>Inputs:</p><ul><li><p>query: \((L, N, E)\) where L is the target sequence length, N is the
@@ -199,17 +183,17 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">multihead_attn</span> <span class="op">&lt;-</span> <span class="fu">nn_multihead_attention</span><span class="op">(</span><span class="va">embed_dim</span>, <span class="va">num_heads</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">multihead_attn</span><span class="op">(</span><span class="va">query</span>, <span class="va">key</span>, <span class="va">value</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">attn_output</span> <span class="op">&lt;-</span> <span class="va">out</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span>
 <span class="r-in"><span><span class="va">attn_output_weights</span> <span class="op">&lt;-</span> <span class="va">out</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -218,14 +202,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_multilabel_margin_loss.html b/reference/nn_multilabel_margin_loss.html
index 498bfa45b4..06e65f1085 100644
--- a/reference/nn_multilabel_margin_loss.html
+++ b/reference/nn_multilabel_margin_loss.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that optimizes a multi-class multi-classification
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Multilabel margin loss — nn_multilabel_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multilabel margin loss — nn_multilabel_margin_loss"><meta name="description" content="Creates a criterion that optimizes a multi-class multi-classification
 hinge loss (margin-based loss) between input \(x\) (a 2D mini-batch Tensor)
 and output \(y\) (which is a 2D Tensor of target class indices).
-For each sample in the mini-batch:"><title>Multilabel margin loss — nn_multilabel_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multilabel margin loss — nn_multilabel_margin_loss"><meta property="og:description" content="Creates a criterion that optimizes a multi-class multi-classification
+For each sample in the mini-batch:"><meta property="og:description" content="Creates a criterion that optimizes a multi-class multi-classification
 hinge loss (margin-based loss) between input \(x\) (a 2D mini-batch Tensor)
 and output \(y\) (which is a 2D Tensor of target class indices).
-For each sample in the mini-batch:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Multilabel margin loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Multilabel margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_multilabel_margin_loss.Rd</code></div>
     </div>
 
@@ -104,7 +86,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>reduction</dt>
+
+
+<dl><dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -127,7 +111,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((C)\) or \((N, C)\) where <code>N</code> is the batch size and <code>C</code>
 is the number of classes.</p></li>
@@ -149,7 +133,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
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diff --git a/reference/nn_multilabel_soft_margin_loss.html b/reference/nn_multilabel_soft_margin_loss.html
index fe62d9deeb..bcdf37d41b 100644
--- a/reference/nn_multilabel_soft_margin_loss.html
+++ b/reference/nn_multilabel_soft_margin_loss.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that optimizes a multi-label one-versus-all
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Multi label soft margin loss — nn_multilabel_soft_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multi label soft margin loss — nn_multilabel_soft_margin_loss"><meta name="description" content="Creates a criterion that optimizes a multi-label one-versus-all
 loss based on max-entropy, between input \(x\) and target \(y\) of size
-\((N, C)\)."><title>Multi label soft margin loss — nn_multilabel_soft_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multi label soft margin loss — nn_multilabel_soft_margin_loss"><meta property="og:description" content="Creates a criterion that optimizes a multi-label one-versus-all
+\((N, C)\)."><meta property="og:description" content="Creates a criterion that optimizes a multi-label one-versus-all
 loss based on max-entropy, between input \(x\) and target \(y\) of size
-\((N, C)\)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Multi label soft margin loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Multi label soft margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_multilabel_soft_margin_loss.Rd</code></div>
     </div>
 
@@ -101,13 +83,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>weight</dt>
+
+
+<dl><dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional): a manual rescaling weight given to each
 class. If given, it has to be a Tensor of size <code>C</code>. Otherwise, it is
 treated as if having all ones.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -126,14 +110,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C)\) where <code>N</code> is the batch size and <code>C</code> is the number of classes.</p></li>
 <li><p>Target: \((N, C)\), label targets padded by -1 ensuring same shape as the input.</p></li>
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N)\).</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -142,14 +126,14 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/nn_nll_loss.html b/reference/nn_nll_loss.html
index 2304641f00..57b9ed2e83 100644
--- a/reference/nn_nll_loss.html
+++ b/reference/nn_nll_loss.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Nll loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
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@@ -98,18 +80,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>weight</dt>
+
+
+<dl><dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional): a manual rescaling weight given to each
 class. If given, it has to be a Tensor of size <code>C</code>. Otherwise, it is
 treated as if having all ones.</p></dd>
 
 
-<dt>ignore_index</dt>
+<dt id="arg-ignore-index">ignore_index<a class="anchor" aria-label="anchor" href="#arg-ignore-index"></a></dt>
 <dd><p>(int, optional): Specifies a target value that is ignored
 and does not contribute to the input gradient.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will
 be applied, <code>'mean'</code>: the weighted mean of the output is taken,
@@ -156,7 +140,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
 \((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
@@ -197,7 +181,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -206,14 +190,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 53fc825d2b..cd69ace312 100644
--- a/reference/nn_pairwise_distance.html
+++ b/reference/nn_pairwise_distance.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Pairwise distance</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-distance.R" class="external-link"><code>R/nn-distance.R</code></a></small>
+
+      <h1>Pairwise distance</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-distance.R" class="external-link"><code>R/nn-distance.R</code></a></small>
       <div class="d-none name"><code>nn_pairwise_distance.Rd</code></div>
     </div>
 
@@ -98,16 +80,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>p</dt>
+
+
+<dl><dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(real): the norm degree. Default: 2</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): Small value to avoid division by zero.
 Default: 1e-6</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool, optional): Determines whether or not to keep the vector dimension.
 Default: FALSE</p></dd>
 
@@ -120,7 +104,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input1: \((N, D)\) where <code>D = vector dimension</code></p></li>
 <li><p>Input2: \((N, D)\), same shape as the Input1</p></li>
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_parameter.html b/reference/nn_parameter.html
index 1584c27cab..18843354e0 100644
--- a/reference/nn_parameter.html
+++ b/reference/nn_parameter.html
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-      <img src="" class="logo" alt=""><h1>Creates an <code>nn_parameter</code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Creates an <code>nn_parameter</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>nn_parameter.Rd</code></div>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>the tensor that you want to indicate as parameter</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>whether this parameter should  have
 <code>requires_grad = TRUE</code></p></dd>
 
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diff --git a/reference/nn_poisson_nll_loss.html b/reference/nn_poisson_nll_loss.html
index 2c7d8864ac..86e5711f63 100644
--- a/reference/nn_poisson_nll_loss.html
+++ b/reference/nn_poisson_nll_loss.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-The loss can be described as:"><title>Poisson NLL loss — nn_poisson_nll_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Poisson NLL loss — nn_poisson_nll_loss"><meta property="og:description" content="Negative log likelihood loss with Poisson distribution of target.
-The loss can be described as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Poisson NLL loss — nn_poisson_nll_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Poisson NLL loss — nn_poisson_nll_loss"><meta name="description" content="Negative log likelihood loss with Poisson distribution of target.
+The loss can be described as:"><meta property="og:description" content="Negative log likelihood loss with Poisson distribution of target.
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Poisson NLL loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Poisson NLL loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_poisson_nll_loss.Rd</code></div>
     </div>
 
@@ -103,24 +85,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>log_input</dt>
+
+
+<dl><dt id="arg-log-input">log_input<a class="anchor" aria-label="anchor" href="#arg-log-input"></a></dt>
 <dd><p>(bool, optional): if <code>TRUE</code> the loss is computed as
 \(\exp(\mbox{input}) - \mbox{target}*\mbox{input}\), if <code>FALSE</code> the loss is
 \(\mbox{input} - \mbox{target}*\log(\mbox{input}+\mbox{eps})\).</p></dd>
 
 
-<dt>full</dt>
+<dt id="arg-full">full<a class="anchor" aria-label="anchor" href="#arg-full"></a></dt>
 <dd><p>(bool, optional): whether to compute full loss, i. e. to add the
 Stirling approximation term
 \(\mbox{target}*\log(\mbox{target}) - \mbox{target} + 0.5 * \log(2\pi\mbox{target})\).</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): Small value to avoid evaluation of \(\log(0)\) when
 <code>log_input = FALSE</code>. Default: 1e-8</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -140,7 +124,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
@@ -160,7 +144,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -169,14 +153,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/nn_prelu.html b/reference/nn_prelu.html
index 2d644ba361..9dee2fdc59 100644
--- a/reference/nn_prelu.html
+++ b/reference/nn_prelu.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PReLU module — nn_prelu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="PReLU module — nn_prelu"><meta name="description" content="Applies the element-wise function:
 $$
   \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
 $$
@@ -11,7 +11,7 @@
 ax, &amp;amp; \mbox{ otherwise }
 \end{array}
 \right.
-$$"><title>PReLU module — nn_prelu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="PReLU module — nn_prelu"><meta property="og:description" content="Applies the element-wise function:
+$$"><meta property="og:description" content="Applies the element-wise function:
 $$
   \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
 $$
@@ -23,10 +23,7 @@
 ax, &amp;amp; \mbox{ otherwise }
 \end{array}
 \right.
-$$"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PReLU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>PReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_prelu.Rd</code></div>
     </div>
 
@@ -131,13 +113,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_parameters</dt>
+
+
+<dl><dt id="arg-num-parameters">num_parameters<a class="anchor" aria-label="anchor" href="#arg-num-parameters"></a></dt>
 <dd><p>(int): number of \(a\) to learn.
 Although it takes an int as input, there is only two values are legitimate:
 1, or the number of channels at input. Default: 1</p></dd>
 
 
-<dt>init</dt>
+<dt id="arg-init">init<a class="anchor" aria-label="anchor" href="#arg-init"></a></dt>
 <dd><p>(float): the initial value of \(a\). Default: 0.25</p></dd>
 
 </dl></div>
@@ -155,7 +139,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -163,7 +147,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
 </ul></div>
@@ -177,7 +161,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -186,14 +170,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_prune_head.html b/reference/nn_prune_head.html
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@@ -1,12 +1,9 @@
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 replace them by your own head, or in order to use the pruned module
-as a sequential embedding module."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prune top layer(s) of a network</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
+
+      <h1>Prune top layer(s) of a network</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn.R" class="external-link"><code>R/nn.R</code></a></small>
       <div class="d-none name"><code>nn_prune_head.Rd</code></div>
     </div>
 
@@ -101,19 +83,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>nn_network to prune</p></dd>
 
 
-<dt>head_size</dt>
+<dt id="arg-head-size">head_size<a class="anchor" aria-label="anchor" href="#arg-head-size"></a></dt>
 <dd><p>number of nn_layers to prune</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a nn_sequential network with the top nn_layer removed</p>
+    <p>a nn_sequential network with the top nn_layer removed</p>
     </div>
 
     <div class="section level2">
@@ -144,7 +126,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> • 4: &lt;nn_linear&gt; #30 parameters</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -153,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nn_relu.html b/reference/nn_relu.html
index c86c0fb3b9..aebcfb5a11 100644
--- a/reference/nn_relu.html
+++ b/reference/nn_relu.html
@@ -1,10 +1,7 @@
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+$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$"><meta property="og:description" content="Applies the rectified linear unit function element-wise
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-      <img src="" class="logo" alt=""><h1>ReLU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>ReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_relu.Rd</code></div>
     </div>
 
@@ -98,13 +80,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>inplace</dt>
+
+
+<dl><dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -119,12 +103,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2521</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.01 *</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.7849</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -133,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_relu6.html b/reference/nn_relu6.html
index a34673c1d9..7a915c046e 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>ReLu6 module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>ReLu6 module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_relu6.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>inplace</dt>
+
+
+<dl><dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -107,7 +91,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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index b77829b5dd..a6111552d1 100644
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>RNN module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
+
+      <h1>RNN module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-rnn.R" class="external-link"><code>R/nn-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_rnn.Rd</code></div>
     </div>
 
@@ -108,47 +90,49 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input_size</dt>
+
+
+<dl><dt id="arg-input-size">input_size<a class="anchor" aria-label="anchor" href="#arg-input-size"></a></dt>
 <dd><p>The number of expected features in the input <code>x</code></p></dd>
 
 
-<dt>hidden_size</dt>
+<dt id="arg-hidden-size">hidden_size<a class="anchor" aria-label="anchor" href="#arg-hidden-size"></a></dt>
 <dd><p>The number of features in the hidden state <code>h</code></p></dd>
 
 
-<dt>num_layers</dt>
+<dt id="arg-num-layers">num_layers<a class="anchor" aria-label="anchor" href="#arg-num-layers"></a></dt>
 <dd><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
 would mean stacking two RNNs together to form a <code>stacked RNN</code>,
 with the second RNN taking in outputs of the first RNN and
 computing the final results. Default: 1</p></dd>
 
 
-<dt>nonlinearity</dt>
+<dt id="arg-nonlinearity">nonlinearity<a class="anchor" aria-label="anchor" href="#arg-nonlinearity"></a></dt>
 <dd><p>The non-linearity to use. Can be either <code>'tanh'</code> or
 <code>'relu'</code>. Default: <code>'tanh'</code></p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
 <code>b_hh</code>. Default: <code>TRUE</code></p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>If <code>TRUE</code>, then the input and output tensors are provided
 as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></dd>
 
 
-<dt>dropout</dt>
+<dt id="arg-dropout">dropout<a class="anchor" aria-label="anchor" href="#arg-dropout"></a></dt>
 <dd><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
 RNN layer except the last layer, with dropout probability equal to
 <code>dropout</code>. Default: 0</p></dd>
 
 
-<dt>bidirectional</dt>
+<dt id="arg-bidirectional">bidirectional<a class="anchor" aria-label="anchor" href="#arg-bidirectional"></a></dt>
 <dd><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>other arguments that can be passed to the super class.</p></dd>
 
 </dl></div>
@@ -167,7 +151,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a></h2>
-    
+
 
 <ul><li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
 of the input sequence. The input can also be a packed variable length
@@ -179,7 +163,7 @@ <h2 id="inputs">Inputs<a class="anchor" aria-label="anchor" href="#inputs"></a><
 </ul></div>
     <div class="section level2">
     <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></a></h2>
-    
+
 
 <ul><li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
 containing the output features (<code>h_t</code>) from the last layer of the RNN,
@@ -196,7 +180,7 @@ <h2 id="outputs">Outputs<a class="anchor" aria-label="anchor" href="#outputs"></
 </ul></div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
 \(H_{in}=\mbox{input\_size}\) and <code>L</code> represents a sequence length.</p></li>
@@ -211,7 +195,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </ul></div>
     <div class="section level2">
     <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attributes"></a></h2>
-    
+
 
 <ul><li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
 of shape <code>(hidden_size, input_size)</code> for <code>k = 0</code>. Otherwise, the shape is
@@ -225,7 +209,7 @@ <h2 id="attributes">Attributes<a class="anchor" aria-label="anchor" href="#attri
 </ul></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
@@ -243,70 +227,70 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.6194  0.1261 -0.5859 -0.5130  0.2639  0.2436  0.7532 -0.1778 -0.1698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7915 -0.2606  0.5423 -0.2332 -0.5692  0.0797 -0.3458 -0.2994 -0.4752</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7498 -0.1321 -0.2842  0.4756 -0.1615  0.2806  0.3849  0.2648 -0.1627</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.5944 -0.6401 -0.4884  0.2165 -0.4695 -0.5841  0.4003  0.0293  0.1361</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3141 -0.4003  0.6153  0.1675 -0.1991  0.1980 -0.0638  0.2009  0.3664</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3311 -0.2844  0.2501 -0.5501 -0.0930 -0.1435 -0.4153  0.1443  0.3987</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.4432 -0.2098 -0.7064 -0.0722 -0.5759 -0.7316  0.3305 -0.2668  0.0912</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3085 -0.2419  0.3502  0.3794 -0.2719  0.7153  0.0670  0.6914  0.6160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0390  0.7705  0.9537  0.7160 -0.5791  0.8475 -0.8662  0.0169 -0.3920</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.8953 -0.3160  0.7690  0.4353 -0.9125  0.1048 -0.3679 -0.2261 -0.7134</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8789  0.2743  0.3330 -0.9160 -0.2329 -0.4732  0.7987  0.2498 -0.3513</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0595 -0.0414  0.2853  0.5087 -0.1204 -0.6069 -0.4815  0.3294  0.2926</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20 -0.1107  0.3902</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4782 -0.1679</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9254  0.8202</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.6519 -0.8058</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1518  0.2662</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5490  0.2870</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9 -0.2733  0.5807 -0.5705 -0.2137 -0.1263  0.2720  0.6784  0.3367  0.2812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2298  0.4555 -0.4291  0.0732  0.4738 -0.2003  0.2658  0.3731 -0.0465</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2809  0.1940 -0.0990  0.2505 -0.0127 -0.4197  0.1765  0.8446 -0.0952</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.6038  0.5381 -0.2702 -0.2611  0.1526  0.5815  0.5129 -0.1028  0.0631</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6053  0.2879  0.2676 -0.1108 -0.5120 -0.1488  0.2754 -0.0545  0.2809</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4840 -0.0389  0.4519  0.0532  0.3017  0.4180  0.0665 -0.3966  0.2519</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18  0.2151  0.3480  0.2816  0.6208 -0.4694 -0.2574 -0.3221  0.7333 -0.4520</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2748 -0.2761 -0.7137  0.4835 -0.1636 -0.6345 -0.0590  0.2484  0.0123</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5653  0.5783 -0.5571  0.2605  0.1348 -0.0049  0.2231  0.4494  0.2747</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.0173 -0.4471  0.4763  0.6337 -0.3888 -0.1050 -0.5589  0.2354 -0.5651</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0250 -0.3052 -0.2256 -0.0450 -0.1452  0.3206 -0.0882 -0.1725 -0.1897</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2431 -0.5869  0.4712  0.0990 -0.1732  0.0419  0.4712  0.5383 -0.7632</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.4467  0.2031</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3049  0.3067</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0243  0.4457</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20 -0.0893 -0.5099</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4132  0.1224</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1380 -0.1980</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9 -0.4587  0.5419 -0.3534 -0.4045 -0.4276 -0.2290  0.0918  0.5118 -0.5355</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1085  0.3107 -0.3817 -0.3413 -0.4206 -0.2209  0.3014  0.1557 -0.1130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2597  0.2839 -0.4300  0.1852  0.0349  0.0473  0.3646 -0.3881  0.4646</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.1520  0.5244  0.0603 -0.0252  0.1342  0.4345  0.4929 -0.0837  0.2292</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2401  0.0731 -0.0629 -0.2905 -0.0231  0.3164  0.2400  0.1131  0.3396</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4581  0.1719  0.1856 -0.4919 -0.2999  0.4888  0.1800 -0.3973 -0.1134</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,3,20} ][ grad_fn = &lt;StackBackward0&gt; ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.0086 -0.2831 -0.5776  0.0057 -0.0709  0.6271  0.5970  0.2080  0.1378</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0209 -0.5796 -0.3668  0.0877 -0.3049  0.4987  0.4293 -0.7983 -0.5304</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2519 -0.3285  0.2158 -0.1921  0.4215  0.2205  0.2465 -0.4121 -0.2367</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.7433  0.0132  0.7771 -0.5761 -0.0948 -0.3255  0.7292 -0.4665 -0.1679</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2671 -0.0385  0.1691 -0.5765 -0.0204  0.3695  0.0650 -0.3395  0.2541</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1996 -0.3506  0.4058 -0.1777  0.5691  0.4692  0.8625  0.7024  0.0454</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18  0.8283  0.0237  0.4405  0.3959  0.7915 -0.3492 -0.5114  0.2015  0.4390</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4698 -0.1859  0.3392  0.6283  0.7378  0.7038  0.3066  0.7614 -0.0596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0976  0.1275  0.5496  0.4558 -0.0012  0.2623  0.1556 -0.0674 -0.4739</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18 -0.5392  0.6396  0.7567  0.7827 -0.4873  0.1251 -0.3834 -0.0474  0.0489</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1094 -0.2379 -0.7107 -0.1123 -0.3464 -0.3640 -0.1792 -0.3552  0.5378</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5916 -0.0385  0.8658  0.1396  0.5103  0.5543  0.5276  0.5941 -0.6288</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20  0.4836  0.6168</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4734  0.4156</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2577  0.4979</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20 -0.4282 -0.1346</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3171 -0.1174</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6226 -0.1535</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9 -0.1114  0.3486 -0.4200 -0.1580 -0.5198 -0.3118  0.2938  0.5197 -0.0018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2732  0.4590 -0.4445  0.2851 -0.4892  0.0292  0.2732  0.1335  0.0589</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3064  0.3742 -0.6699 -0.2006 -0.5265 -0.1959  0.4728  0.1926 -0.0590</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 9  0.4440 -0.2710  0.1350 -0.3705 -0.0772 -0.4557  0.0953  0.2506  0.6398</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0045  0.3164  0.4202 -0.4949 -0.2211  0.4044 -0.0044 -0.2685  0.6013</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7687 -0.2897  0.5142 -0.2122 -0.1092  0.0634  0.4387  0.3466  0.2665</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18  0.0210  0.3693 -0.2735  0.4198 -0.6618 -0.2076 -0.0963  0.3921 -0.5134</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1978  0.4044 -0.1352  0.3989 -0.5551 -0.5138  0.0447  0.2919 -0.1836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1193 -0.0102  0.0760  0.4765 -0.0831 -0.3886  0.2522  0.4670 -0.1084</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 10 to 18  0.2733  0.1802 -0.6125 -0.2482  0.4468  0.4122 -0.1445 -0.1921 -0.0106</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1850 -0.0958 -0.4403 -0.3507  0.6460 -0.3670 -0.1135  0.2356  0.3084</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0798 -0.4872 -0.2650  0.1576  0.3333  0.2148 -0.6969  0.5199 -0.4006</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20 -0.0799  0.3596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2372  0.5238</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2247  0.4753</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Columns 19 to 20 -0.2628  0.2681</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4175 -0.3963</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3259 -0.2066</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3,20} ][ grad_fn = &lt;StackBackward0&gt; ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -315,14 +299,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>RReLU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>RReLU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_rrelu.Rd</code></div>
     </div>
 
@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>lower</dt>
+
+
+<dl><dt id="arg-lower">lower<a class="anchor" aria-label="anchor" href="#arg-lower"></a></dt>
 <dd><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -128,7 +112,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -143,13 +127,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">m</span><span class="op">(</span><span class="va">input</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.01 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9460</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -29.5220</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1454</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0347</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +141,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_selu.html b/reference/nn_selu.html
index 1e71747578..1234e7583f 100644
--- a/reference/nn_selu.html
+++ b/reference/nn_selu.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>SELU module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>SELU module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_selu.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>inplace</dt>
+
+
+<dl><dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -111,7 +95,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_sequential.html b/reference/nn_sequential.html
index 019b65ce8a..47ce89b85e 100644
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@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A sequential container.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>A sequential container — nn_sequential • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="A sequential container — nn_sequential"><meta name="description" content="A sequential container.
 Modules will be added to it in the order they are passed in the constructor.
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+See examples."><meta property="og:description" content="A sequential container.
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diff --git a/reference/nn_silu.html b/reference/nn_silu.html
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-      <img src="" class="logo" alt=""><h1>Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_silu.Rd</code></div>
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@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>inplace</dt>
+
+
+<dl><dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -111,7 +95,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 where the SiLU was experimented with later.</p>
     </div>
 
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diff --git a/reference/nn_smooth_l1_loss.html b/reference/nn_smooth_l1_loss.html
index 13a31fc25e..2377cb2a16 100644
--- a/reference/nn_smooth_l1_loss.html
+++ b/reference/nn_smooth_l1_loss.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that uses a squared term if the absolute
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Smooth L1 loss — nn_smooth_l1_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Smooth L1 loss — nn_smooth_l1_loss"><meta name="description" content="Creates a criterion that uses a squared term if the absolute
 element-wise error falls below 1 and an L1 term otherwise.
 It is less sensitive to outliers than the MSELoss and in some cases
 prevents exploding gradients (e.g. see Fast R-CNN paper by Ross Girshick).
-Also known as the Huber loss:"><title>Smooth L1 loss — nn_smooth_l1_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Smooth L1 loss — nn_smooth_l1_loss"><meta property="og:description" content="Creates a criterion that uses a squared term if the absolute
+Also known as the Huber loss:"><meta property="og:description" content="Creates a criterion that uses a squared term if the absolute
 element-wise error falls below 1 and an L1 term otherwise.
 It is less sensitive to outliers than the MSELoss and in some cases
 prevents exploding gradients (e.g. see Fast R-CNN paper by Ross Girshick).
-Also known as the Huber loss:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -107,7 +89,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>reduction</dt>
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 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -133,7 +117,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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-    
+
 
 <ul><li><p>Input: \((N, *)\) where \(*\) means, any number of additional
 dimensions</p></li>
@@ -142,7 +126,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 \((N, *)\), same shape as the input</p></li>
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diff --git a/reference/nn_soft_margin_loss.html b/reference/nn_soft_margin_loss.html
index fb79dc3b5a..c7ab86f544 100644
--- a/reference/nn_soft_margin_loss.html
+++ b/reference/nn_soft_margin_loss.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that optimizes a two-class classification
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Soft margin loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Soft margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_soft_margin_loss.Rd</code></div>
     </div>
 
@@ -101,7 +83,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>reduction</dt>
+
+
+<dl><dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -116,7 +100,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\) where \(*\) means, any number of additional
 dimensions</p></li>
@@ -124,7 +108,7 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 <li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then same shape as the input</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -133,14 +117,14 @@ <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2
 </div>
 
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diff --git a/reference/nn_softmax.html b/reference/nn_softmax.html
index 00462e0174..525542628f 100644
--- a/reference/nn_softmax.html
+++ b/reference/nn_softmax.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the Softmax function to an n-dimensional input Tensor
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Softmax module — nn_softmax • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softmax module — nn_softmax"><meta name="description" content="Applies the Softmax function to an n-dimensional input Tensor
 rescaling them so that the elements of the n-dimensional output Tensor
 lie in the range [0,1] and sum to 1.
-Softmax is defined as:"><title>Softmax module — nn_softmax • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softmax module — nn_softmax"><meta property="og:description" content="Applies the Softmax function to an n-dimensional input Tensor
+Softmax is defined as:"><meta property="og:description" content="Applies the Softmax function to an n-dimensional input Tensor
 rescaling them so that the elements of the n-dimensional output Tensor
 lie in the range [0,1] and sum to 1.
-Softmax is defined as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+Softmax is defined as:"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -16,77 +13,62 @@
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 </script></head><body>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Softmax module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Softmax module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_softmax.Rd</code></div>
     </div>
 
@@ -104,20 +86,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dim</dt>
+
+
+<dl><dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int): A dimension along which Softmax will be computed (so every slice
 along dim will sum to 1).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>:
+    <p>:
 a Tensor of the same dimension and shape as the input with
 values in the range <code>[0, 1]</code></p>
-
-
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -135,7 +115,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -151,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/nn_softmax2d.html b/reference/nn_softmax2d.html
index cdd1f6bf4b..79384e564e 100644
--- a/reference/nn_softmax2d.html
+++ b/reference/nn_softmax2d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies SoftMax over features to each spatial location.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Softmax2d module — nn_softmax2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softmax2d module — nn_softmax2d"><meta name="description" content="Applies SoftMax over features to each spatial location.
 When given an image of Channels x Height x Width, it will
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+apply Softmax to each location \((Channels, h_i, w_j)\)"><meta property="og:description" content="Applies SoftMax over features to each spatial location.
 When given an image of Channels x Height x Width, it will
-apply Softmax to each location \((Channels, h_i, w_j)\)"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Softmax2d module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Softmax2d module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_softmax2d.Rd</code></div>
     </div>
 
@@ -101,16 +83,12 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a Tensor of the same dimension and shape as the input with
+    <p>a Tensor of the same dimension and shape as the input with
 values in the range <code>[0, 1]</code></p>
-
-
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, C, H, W)\)</p></li>
 <li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
@@ -125,7 +103,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -134,14 +112,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/nn_softmin.html b/reference/nn_softmin.html
index 02675182bc..5c51702435 100644
--- a/reference/nn_softmin.html
+++ b/reference/nn_softmin.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the Softmin function to an n-dimensional input Tensor
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Softmin — nn_softmin • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softmin — nn_softmin"><meta name="description" content="Applies the Softmin function to an n-dimensional input Tensor
 rescaling them so that the elements of the n-dimensional output Tensor
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+Softmin is defined as:"><meta property="og:description" content="Applies the Softmin function to an n-dimensional input Tensor
 rescaling them so that the elements of the n-dimensional output Tensor
 lie in the range [0, 1] and sum to 1.
-Softmin is defined as:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -16,77 +13,62 @@
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Softmin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Softmin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_softmin.Rd</code></div>
     </div>
 
@@ -104,16 +86,16 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dim</dt>
+
+
+<dl><dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int): A dimension along which Softmin will be computed (so every slice
 along dim will sum to 1).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a Tensor of the same dimension and shape as the input, with
+    <p>a Tensor of the same dimension and shape as the input, with
 values in the range <code>[0, 1]</code>.</p>
     </div>
     <div class="section level2">
@@ -124,7 +106,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((*)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -140,7 +122,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +131,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
 
-  
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diff --git a/reference/nn_softplus.html b/reference/nn_softplus.html
index 77d25da265..0b20de4e47 100644
--- a/reference/nn_softplus.html
+++ b/reference/nn_softplus.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Softplus module — nn_softplus • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softplus module — nn_softplus"><meta name="description" content="Applies the element-wise function:
 $$
   \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
-$$"><title>Softplus module — nn_softplus • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softplus module — nn_softplus"><meta property="og:description" content="Applies the element-wise function:
+$$"><meta property="og:description" content="Applies the element-wise function:
 $$
   \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
-$$"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Softplus module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Softplus module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_softplus.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>beta</dt>
+
+
+<dl><dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>values above this revert to a linear function. Default: 20</p></dd>
 
 </dl></div>
@@ -121,7 +105,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
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diff --git a/reference/nn_softshrink.html b/reference/nn_softshrink.html
index da089873a9..255114fdae 100644
--- a/reference/nn_softshrink.html
+++ b/reference/nn_softshrink.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the soft shrinkage function elementwise:"><title>Softshrink module — nn_softshrink • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softshrink module — nn_softshrink"><meta property="og:description" content="Applies the soft shrinkage function elementwise:"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Softshrink module — nn_softshrink • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softshrink module — nn_softshrink"><meta name="description" content="Applies the soft shrinkage function elementwise:"><meta property="og:description" content="Applies the soft shrinkage function elementwise:"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Softshrink module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Softshrink module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_softshrink.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>lambd</dt>
+
+
+<dl><dt id="arg-lambd">lambd<a class="anchor" aria-label="anchor" href="#arg-lambd"></a></dt>
 <dd><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></dd>
 
 </dl></div>
@@ -113,7 +97,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +122,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_softsign.html b/reference/nn_softsign.html
index c4fe97a75a..43b5533d6f 100644
--- a/reference/nn_softsign.html
+++ b/reference/nn_softsign.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the element-wise function:
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Softsign module — nn_softsign • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softsign module — nn_softsign"><meta name="description" content="Applies the element-wise function:
 $$
   \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
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-      <img src="" class="logo" alt=""><h1>Softsign module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
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diff --git a/reference/nn_tanh.html b/reference/nn_tanh.html
index eed4c4ad53..eb1187abef 100644
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+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
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@@ -101,7 +83,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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 dimensions</p></li>
@@ -117,7 +99,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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-      <img src="" class="logo" alt=""><h1>Tanhshrink module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Tanhshrink module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
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@@ -101,7 +83,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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 dimensions</p></li>
@@ -117,7 +99,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_threshold.html b/reference/nn_threshold.html
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-      <img src="" class="logo" alt=""><h1>Threshold module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
+
+      <h1>Threshold module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-activation.R" class="external-link"><code>R/nn-activation.R</code></a></small>
       <div class="d-none name"><code>nn_threshold.Rd</code></div>
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@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>threshold</dt>
+
+
+<dl><dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>The value to threshold at</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>The value to replace with</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -121,7 +105,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where <code>*</code> means, any number of additional
 dimensions</p></li>
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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diff --git a/reference/nn_triplet_margin_loss.html b/reference/nn_triplet_margin_loss.html
index 19fc063592..9134c28318 100644
--- a/reference/nn_triplet_margin_loss.html
+++ b/reference/nn_triplet_margin_loss.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the triplet loss given an input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Triplet margin loss — nn_triplet_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet margin loss — nn_triplet_margin_loss"><meta name="description" content="Creates a criterion that measures the triplet loss given an input
 tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\).
 This is used for measuring a relative similarity between samples. A triplet
 is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be
-\((N, D)\)."><title>Triplet margin loss — nn_triplet_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet margin loss — nn_triplet_margin_loss"><meta property="og:description" content="Creates a criterion that measures the triplet loss given an input
+\((N, D)\)."><meta property="og:description" content="Creates a criterion that measures the triplet loss given an input
 tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\).
 This is used for measuring a relative similarity between samples. A triplet
 is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be
-\((N, D)\)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Triplet margin loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Triplet margin loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_triplet_margin_loss.Rd</code></div>
     </div>
 
@@ -113,25 +95,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>margin</dt>
+
+
+<dl><dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>(float, optional): Default: \(1\).</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(int, optional): The norm degree for pairwise distance. Default: \(2\).</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>constant to avoid NaN's</p></dd>
 
 
-<dt>swap</dt>
+<dt id="arg-swap">swap<a class="anchor" aria-label="anchor" href="#arg-swap"></a></dt>
 <dd><p>(bool, optional): The distance swap is described in detail in the paper
 <a href="https://paperswithcode.com/paper/learning-local-feature-descriptors-with" class="external-link">Learning shallow convolutional feature descriptors with triplet losses</a> by
 V. Balntas, E. Riba et al. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -156,7 +140,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, D)\) where \(D\) is the vector dimension.</p></li>
 <li><p>Output: A Tensor of shape \((N)\) if <code>reduction</code> is <code>'none'</code>, or a scalar
@@ -175,7 +159,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -184,14 +168,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_triplet_margin_with_distance_loss.html b/reference/nn_triplet_margin_with_distance_loss.html
index 66ef7e6421..76b9eda82c 100644
--- a/reference/nn_triplet_margin_with_distance_loss.html
+++ b/reference/nn_triplet_margin_with_distance_loss.html
@@ -1,18 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Creates a criterion that measures the triplet loss given input
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-anchor and negative example ("negative distance").'><title>Triplet margin with distance loss — nn_triplet_margin_with_distance_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet margin with distance loss — nn_triplet_margin_with_distance_loss"><meta property="og:description" content='Creates a criterion that measures the triplet loss given input
+anchor and negative example ("negative distance").'><meta property="og:description" content='Creates a criterion that measures the triplet loss given input
 tensors \(a\), \(p\), and \(n\) (representing anchor,
 positive, and negative examples, respectively), and a nonnegative,
 real-valued function ("distance function") used to compute the relationship
 between the anchor and positive example ("positive distance") and the
-anchor and negative example ("negative distance").'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Triplet margin with distance loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
+
+      <h1>Triplet margin with distance loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-loss.R" class="external-link"><code>R/nn-loss.R</code></a></small>
       <div class="d-none name"><code>nn_triplet_margin_with_distance_loss.Rd</code></div>
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@@ -115,20 +97,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>distance_function</dt>
+
+
+<dl><dt id="arg-distance-function">distance_function<a class="anchor" aria-label="anchor" href="#arg-distance-function"></a></dt>
 <dd><p>(callable, optional): A nonnegative, real-valued function that
 quantifies the closeness of two tensors. If not specified,
 <code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> will be used.  Default: <code>None</code></p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>(float, optional): A non-negative margin representing the minimum difference
 between the positive and negative distances required for the loss to be 0. Larger
 margins penalize cases where the negative examples are not distant enough from the
 anchors, relative to the positives. Default: \(1\).</p></dd>
 
 
-<dt>swap</dt>
+<dt id="arg-swap">swap<a class="anchor" aria-label="anchor" href="#arg-swap"></a></dt>
 <dd><p>(bool, optional): Whether to use the distance swap described in the paper
 <a href="https://paperswithcode.com/paper/learning-local-feature-descriptors-with" class="external-link">Learning shallow convolutional feature descriptors with triplet losses</a> by
 V. Balntas, E. Riba et al. If TRUE, and if the positive example is closer to the
@@ -136,7 +120,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 the loss computation. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional): Specifies the (optional) reduction to apply to the output:
 <code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
 <code>'mean'</code>: the sum of the output will be divided by the number of
@@ -171,7 +155,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="shape">Shape<a class="anchor" aria-label="anchor" href="#shape"></a></h2>
-    
+
 
 <ul><li><p>Input: \((N, *)\) where \(*\) represents any number of additional dimensions
 as supported by the distance function.</p></li>
@@ -218,7 +202,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -227,14 +211,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Unflattens a tensor dim expanding it to a desired shape. For use with [<a href="nn_sequential.html">nn_sequential</a>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-flatten.R" class="external-link"><code>R/nn-flatten.R</code></a></small>
+
+      <h1>Unflattens a tensor dim expanding it to a desired shape. For use with [<a href="nn_sequential.html">nn_sequential</a>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-flatten.R" class="external-link"><code>R/nn-flatten.R</code></a></small>
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@@ -98,11 +80,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dim</dt>
+
+
+<dl><dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>Dimension to be unflattened</p></dd>
 
 
-<dt>unflattened_size</dt>
+<dt id="arg-unflattened-size">unflattened_size<a class="anchor" aria-label="anchor" href="#arg-unflattened-size"></a></dt>
 <dd><p>New shape of the unflattened dimension</p></dd>
 
 </dl></div>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2 2 5 5</span>
 </code></pre></div>
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diff --git a/reference/nn_upsample.html b/reference/nn_upsample.html
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 The input data is assumed to be of the form minibatch x channels x optional depth x
 optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for
-volumetric inputs, we expect a 5D Tensor."><title>Upsample module — nn_upsample • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Upsample module — nn_upsample"><meta property="og:description" content="Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+volumetric inputs, we expect a 5D Tensor."><meta property="og:description" content="Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
 The input data is assumed to be of the form minibatch x channels x optional depth x
 optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for
-volumetric inputs, we expect a 5D Tensor."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Upsample module</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-upsampling.R" class="external-link"><code>R/nn-upsampling.R</code></a></small>
+
+      <h1>Upsample module</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-upsampling.R" class="external-link"><code>R/nn-upsampling.R</code></a></small>
       <div class="d-none name"><code>nn_upsample.Rd</code></div>
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@@ -109,23 +91,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>size</dt>
+
+
+<dl><dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>, optional):
 output spatial sizes</p></dd>
 
 
-<dt>scale_factor</dt>
+<dt id="arg-scale-factor">scale_factor<a class="anchor" aria-label="anchor" href="#arg-scale-factor"></a></dt>
 <dd><p>(float or <code>Tuple[float]</code> or <code>Tuple[float, float]</code> or <code>Tuple[float, float, float]</code>, optional):
 multiplier for spatial size. Has to match input size if it is a tuple.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(str, optional): the upsampling algorithm: one of <code>'nearest'</code>,
 <code>'linear'</code>, <code>'bilinear'</code>, <code>'bicubic'</code> and <code>'trilinear'</code>.
 Default: <code>'nearest'</code></p></dd>
 
 
-<dt>align_corners</dt>
+<dt id="arg-align-corners">align_corners<a class="anchor" aria-label="anchor" href="#arg-align-corners"></a></dt>
 <dd><p>(bool, optional): if <code>TRUE</code>, the corner pixels of the input
 and output tensors are aligned, and thus preserving the values at
 those pixels. This only has effect when <code>mode</code> is
@@ -156,7 +140,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,1,4,4} ]</span>
 </code></pre></div>
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@@ -165,14 +149,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nn_utils_clip_grad_norm_.html b/reference/nn_utils_clip_grad_norm_.html
index 0012791fad..8b69282732 100644
--- a/reference/nn_utils_clip_grad_norm_.html
+++ b/reference/nn_utils_clip_grad_norm_.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The norm is computed over all gradients together, as if they were
-concatenated into a single vector. Gradients are modified in-place."><title>Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_"><meta property="og:description" content="The norm is computed over all gradients together, as if they were
-concatenated into a single vector. Gradients are modified in-place."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+concatenated into a single vector. Gradients are modified in-place."><meta property="og:description" content="The norm is computed over all gradients together, as if they were
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Clips gradient norm of an iterable of parameters.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-clip-grad.R" class="external-link"><code>R/nn-utils-clip-grad.R</code></a></small>
+
+      <h1>Clips gradient norm of an iterable of parameters.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-clip-grad.R" class="external-link"><code>R/nn-utils-clip-grad.R</code></a></small>
       <div class="d-none name"><code>nn_utils_clip_grad_norm_.Rd</code></div>
     </div>
 
@@ -98,28 +80,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>parameters</dt>
+
+
+<dl><dt id="arg-parameters">parameters<a class="anchor" aria-label="anchor" href="#arg-parameters"></a></dt>
 <dd><p>(IterableTensor or Tensor): an iterable of Tensors or a
 single Tensor that will have gradients normalized</p></dd>
 
 
-<dt>max_norm</dt>
+<dt id="arg-max-norm">max_norm<a class="anchor" aria-label="anchor" href="#arg-max-norm"></a></dt>
 <dd><p>(float or int): max norm of the gradients</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>(float or int): type of the used p-norm. Can be <code>Inf</code> for
 infinity norm.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Total norm of the parameters (viewed as a single vector).</p>
+    <p>Total norm of the parameters (viewed as a single vector).</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -128,14 +110,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nn_utils_clip_grad_value_.html b/reference/nn_utils_clip_grad_value_.html
index 979b1a5b34..557f7c037d 100644
--- a/reference/nn_utils_clip_grad_value_.html
+++ b/reference/nn_utils_clip_grad_value_.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Clips gradient of an iterable of parameters at specified value.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-clip-grad.R" class="external-link"><code>R/nn-utils-clip-grad.R</code></a></small>
+
+      <h1>Clips gradient of an iterable of parameters at specified value.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-clip-grad.R" class="external-link"><code>R/nn-utils-clip-grad.R</code></a></small>
       <div class="d-none name"><code>nn_utils_clip_grad_value_.Rd</code></div>
     </div>
 
@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>parameters</dt>
+
+
+<dl><dt id="arg-parameters">parameters<a class="anchor" aria-label="anchor" href="#arg-parameters"></a></dt>
 <dd><p>(Iterable(Tensor) or Tensor): an iterable of Tensors or a
 single Tensor that will have gradients normalized</p></dd>
 
 
-<dt>clip_value</dt>
+<dt id="arg-clip-value">clip_value<a class="anchor" aria-label="anchor" href="#arg-clip-value"></a></dt>
 <dd><p>(float or int): maximum allowed value of the gradients.</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 \(\left[\mbox{-clip\_value}, \mbox{clip\_value}\right]\)</p>
     </div>
 
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 </div>
 
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diff --git a/reference/nn_utils_rnn_pack_padded_sequence.html b/reference/nn_utils_rnn_pack_padded_sequence.html
index a5f08e1210..e2f32c9fa9 100644
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+++ b/reference/nn_utils_rnn_pack_padded_sequence.html
@@ -1,14 +1,11 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="input can be of size T x B x * where T is the length of the
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+TRUE, B x T x * input is expected."><meta property="og:description" content="input can be of size T x B x * where T is the length of the
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-      <img src="" class="logo" alt=""><h1>Packs a Tensor containing padded sequences of variable length.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+
+      <h1>Packs a Tensor containing padded sequences of variable length.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
     </div>
 
@@ -109,20 +91,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor): padded batch of variable length sequences.</p></dd>
 
 
-<dt>lengths</dt>
+<dt id="arg-lengths">lengths<a class="anchor" aria-label="anchor" href="#arg-lengths"></a></dt>
 <dd><p>(Tensor): list of sequences lengths of each batch element.</p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
 format.</p></dd>
 
 
-<dt>enforce_sorted</dt>
+<dt id="arg-enforce-sorted">enforce_sorted<a class="anchor" aria-label="anchor" href="#arg-enforce-sorted"></a></dt>
 <dd><p>(bool, optional): if <code>TRUE</code>, the input is expected to
 contain sequences sorted by length in a decreasing order. If
 <code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></dd>
@@ -130,9 +114,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>PackedSequence</code> object</p>
+    <p>a <code>PackedSequence</code> object</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -149,7 +131,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 a <code>PackedSequence</code> object by accessing its <code>.data</code> attribute.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -158,14 +140,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 </div>
 
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diff --git a/reference/nn_utils_rnn_pack_sequence.html b/reference/nn_utils_rnn_pack_sequence.html
index 5b84299a54..d353f9db72 100644
--- a/reference/nn_utils_rnn_pack_sequence.html
+++ b/reference/nn_utils_rnn_pack_sequence.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
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+including zero."><meta property="og:description" content="sequences should be a list of Tensors of size L x *, where L is
 the length of a sequence and * is any number of trailing dimensions,
-including zero."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Packs a list of variable length Tensors</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+
+      <h1>Packs a list of variable length Tensors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
     </div>
 
@@ -101,11 +83,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>sequences</dt>
+
+
+<dl><dt id="arg-sequences">sequences<a class="anchor" aria-label="anchor" href="#arg-sequences"></a></dt>
 <dd><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></dd>
 
 
-<dt>enforce_sorted</dt>
+<dt id="arg-enforce-sorted">enforce_sorted<a class="anchor" aria-label="anchor" href="#arg-enforce-sorted"></a></dt>
 <dd><p>(bool, optional): if <code>TRUE</code>, checks that the input
 contains sequences sorted by length in a decreasing order. If
 <code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></dd>
@@ -113,9 +97,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a <code>PackedSequence</code> object</p>
+    <p>a <code>PackedSequence</code> object</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -135,7 +117,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -144,14 +126,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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+
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diff --git a/reference/nn_utils_rnn_pad_packed_sequence.html b/reference/nn_utils_rnn_pad_packed_sequence.html
index ef12cb5793..40a6c7112c 100644
--- a/reference/nn_utils_rnn_pad_packed_sequence.html
+++ b/reference/nn_utils_rnn_pad_packed_sequence.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="It is an inverse operation to nn_utils_rnn_pack_padded_sequence()."><title>Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence"><meta property="og:description" content="It is an inverse operation to nn_utils_rnn_pack_padded_sequence()."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Pads a packed batch of variable length sequences.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+
+      <h1>Pads a packed batch of variable length sequences.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
     </div>
 
@@ -100,20 +82,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>sequence</dt>
+
+
+<dl><dt id="arg-sequence">sequence<a class="anchor" aria-label="anchor" href="#arg-sequence"></a></dt>
 <dd><p>(PackedSequence): batch to pad</p></dd>
 
 
-<dt>batch_first</dt>
-<dd><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
+<dd><p>(bool, optional): if <code>True</code>, the output will be in “B x T x *`
 format.</p></dd>
 
 
-<dt>padding_value</dt>
+<dt id="arg-padding-value">padding_value<a class="anchor" aria-label="anchor" href="#arg-padding-value"></a></dt>
 <dd><p>(float, optional): values for padded elements.</p></dd>
 
 
-<dt>total_length</dt>
+<dt id="arg-total-length">total_length<a class="anchor" aria-label="anchor" href="#arg-total-length"></a></dt>
 <dd><p>(int, optional): if not <code>NULL</code>, the output will be padded to
 have length <code>total_length</code>. This method will throw <code>ValueError</code>
 if <code>total_length</code> is less than the max sequence length in
@@ -122,9 +106,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Tuple of Tensor containing the padded sequence, and a Tensor
+    <p>Tuple of Tensor containing the padded sequence, and a Tensor
 containing the list of lengths of each sequence in the batch.
 Batch elements will be re-ordered as they were ordered originally when
 the batch was passed to <code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> or
@@ -171,7 +153,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -180,14 +162,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_utils_rnn_pad_sequence.html b/reference/nn_utils_rnn_pad_sequence.html
index 56d3bb7f73..c9b06b5a07 100644
--- a/reference/nn_utils_rnn_pad_sequence.html
+++ b/reference/nn_utils_rnn_pad_sequence.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="pad_sequence stacks a list of Tensors along a new dimension,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence"><meta name="description" content="pad_sequence stacks a list of Tensors along a new dimension,
 and pads them to equal length. For example, if the input is list of
 sequences with size L x * and if batch_first is False, and T x B x *
-otherwise."><title>Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence"><meta property="og:description" content="pad_sequence stacks a list of Tensors along a new dimension,
+otherwise."><meta property="og:description" content="pad_sequence stacks a list of Tensors along a new dimension,
 and pads them to equal length. For example, if the input is list of
 sequences with size L x * and if batch_first is False, and T x B x *
-otherwise."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
+
+      <h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-rnn.R" class="external-link"><code>R/nn-utils-rnn.R</code></a></small>
       <div class="d-none name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
     </div>
 
@@ -104,24 +86,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>sequences</dt>
+
+
+<dl><dt id="arg-sequences">sequences<a class="anchor" aria-label="anchor" href="#arg-sequences"></a></dt>
 <dd><p><code>(list[Tensor])</code>: list of variable length sequences.</p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
 or in <code>T x B x *</code> otherwise</p></dd>
 
 
-<dt>padding_value</dt>
+<dt id="arg-padding-value">padding_value<a class="anchor" aria-label="anchor" href="#arg-padding-value"></a></dt>
 <dd><p>(float, optional): value for padded elements. Default: 0.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
+    <p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
 Tensor of size <code>B x T x *</code> otherwise</p>
     </div>
     <div class="section level2">
@@ -149,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1]  25   3 300</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -158,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nn_utils_weight_norm.html b/reference/nn_utils_weight_norm.html
index f46fb1c59d..5592d7dfb4 100644
--- a/reference/nn_utils_weight_norm.html
+++ b/reference/nn_utils_weight_norm.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>nn_utils_weight_norm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nn-utils-weight-norm.R" class="external-link"><code>R/nn-utils-weight-norm.R</code></a></small>
+
+      <h1>nn_utils_weight_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nn-utils-weight-norm.R" class="external-link"><code>R/nn-utils-weight-norm.R</code></a></small>
       <div class="d-none name"><code>nn_utils_weight_norm.Rd</code></div>
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@@ -91,9 +73,7 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>The original module with the weight_v and weight_g paramters.</p>
+    <p>The original module with the weight_v and weight_g paramters.</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -118,7 +98,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
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-    
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@@ -292,7 +272,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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index ccbfe2c2a5..6d8aeb82d6 100644
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-      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
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@@ -98,16 +80,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Adaptive_avg_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_adaptive_avg_pool2d.Rd</code></div>
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@@ -98,16 +80,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size (single integer or double-integer tuple)</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nnf_adaptive_avg_pool3d.html b/reference/nnf_adaptive_avg_pool3d.html
index 64eaa7cf8c..86d9924ded 100644
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-      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
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@@ -98,16 +80,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size (single integer or triple-integer tuple)</p></dd>
 
 </dl></div>
 
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diff --git a/reference/nnf_adaptive_max_pool1d.html b/reference/nnf_adaptive_max_pool1d.html
index 2f7442f430..ad003dc83a 100644
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-      <img src="" class="logo" alt=""><h1>Adaptive_max_pool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Adaptive_max_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
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@@ -98,20 +80,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size (single integer)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>whether to return pooling indices. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_adaptive_max_pool2d.html b/reference/nnf_adaptive_max_pool2d.html
index b7124c8135..d336ecb666 100644
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-      <img src="" class="logo" alt=""><h1>Adaptive_max_pool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Adaptive_max_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_adaptive_max_pool2d.Rd</code></div>
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@@ -98,20 +80,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size (single integer or double-integer tuple)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>whether to return pooling indices. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
 
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diff --git a/reference/nnf_adaptive_max_pool3d.html b/reference/nnf_adaptive_max_pool3d.html
index 7df398dd9d..a0676580ac 100644
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+++ b/reference/nnf_adaptive_max_pool3d.html
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-      <img src="" class="logo" alt=""><h1>Adaptive_max_pool3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Adaptive_max_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_adaptive_max_pool3d.Rd</code></div>
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@@ -98,20 +80,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size (single integer or triple-integer tuple)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>whether to return pooling indices. Default:<code>FALSE</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_affine_grid.html b/reference/nnf_affine_grid.html
index eef181da2f..564cc38f68 100644
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+++ b/reference/nnf_affine_grid.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Affine_grid</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-vision.R" class="external-link"><code>R/nnf-vision.R</code></a></small>
+
+      <h1>Affine_grid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-vision.R" class="external-link"><code>R/nnf-vision.R</code></a></small>
       <div class="d-none name"><code>nnf_affine_grid.Rd</code></div>
     </div>
 
@@ -98,18 +80,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>theta</dt>
+
+
+<dl><dt id="arg-theta">theta<a class="anchor" aria-label="anchor" href="#arg-theta"></a></dt>
 <dd><p>(Tensor) input batch of affine matrices with shape
 (\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
 for 2D or \(N \times C \times D \times H \times W\) for 3D)
 Example: torch.Size((32, 3, 24, 24))</p></dd>
 
 
-<dt>align_corners</dt>
+<dt id="arg-align-corners">align_corners<a class="anchor" aria-label="anchor" href="#arg-align-corners"></a></dt>
 <dd><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
 to refer to the centers of the corner pixels rather than the image corners.
 Refer to <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> for a more complete description. A grid generated by
@@ -119,14 +103,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>This function is often used in conjunction with <code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code>
 to build <code>Spatial Transformer Networks</code>_ .</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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     </footer></div>
 
-  
 
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diff --git a/reference/nnf_alpha_dropout.html b/reference/nnf_alpha_dropout.html
index f6aedc015e..3d35eb27de 100644
--- a/reference/nnf_alpha_dropout.html
+++ b/reference/nnf_alpha_dropout.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Alpha_dropout</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
+
+      <h1>Alpha_dropout</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
       <div class="d-none name"><code>nnf_alpha_dropout.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>probability of an element to be zeroed. Default: 0.5</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>If set to <code>TRUE</code>, will do this operation in-place.
 Default: <code>FALSE</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -122,14 +106,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_avg_pool1d.html b/reference/nnf_avg_pool1d.html
index b6a49ad601..147a0ac97f 100644
--- a/reference/nnf_avg_pool1d.html
+++ b/reference/nnf_avg_pool1d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Avg_pool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Avg_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_avg_pool1d.Rd</code></div>
     </div>
 
@@ -105,37 +87,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window. Can be a single number or a
 tuple <code>(kW,)</code>.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Can be a single number or a tuple
 <code>(sW,)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a
 single number or a tuple <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
 output shape. Default: <code>FALSE</code></p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when True, will include the zero-padding in the
 averaging calculation. Default: <code>TRUE</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -144,14 +128,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_avg_pool2d.html b/reference/nnf_avg_pool2d.html
index 17dab231c2..abe18756c4 100644
--- a/reference/nnf_avg_pool2d.html
+++ b/reference/nnf_avg_pool2d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies 2D average-pooling operation in \(kH * kW\) regions by step size
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 \(sH * sW\) steps. The number of output features is equal to the number of
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+input planes."><meta property="og:description" content="Applies 2D average-pooling operation in \(kH * kW\) regions by step size
 \(sH * sW\) steps. The number of output features is equal to the number of
-input planes."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Avg_pool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Avg_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_avg_pool2d.Rd</code></div>
     </div>
 
@@ -109,42 +91,44 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>size of the pooling region. Can be a single number or a
 tuple <code>(kH, kW)</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>stride of the pooling operation. Can be a single number or a
 tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a
 single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
 to compute the output shape. Default: <code>FALSE</code></p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when True, will include the zero-padding in the
 averaging calculation. Default: <code>TRUE</code></p></dd>
 
 
-<dt>divisor_override</dt>
+<dt id="arg-divisor-override">divisor_override<a class="anchor" aria-label="anchor" href="#arg-divisor-override"></a></dt>
 <dd><p>if specified, it will be used as divisor, otherwise
 size of the pooling region will be used. Default: <code>NULL</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -153,14 +137,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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   </body></html>
 
diff --git a/reference/nnf_avg_pool3d.html b/reference/nnf_avg_pool3d.html
index 73247a945c..f43d92712f 100644
--- a/reference/nnf_avg_pool3d.html
+++ b/reference/nnf_avg_pool3d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
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+\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\)."><meta property="og:description" content="Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
 size \(sT * sH * sW\) steps. The number of output features is equal to
-\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -109,42 +91,44 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>size of the pooling region. Can be a single number or a
 tuple <code>(kT, kH, kW)</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>stride of the pooling operation. Can be a single number or a
 tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a
 single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
 to compute the output shape</p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when True, will include the zero-padding in the
 averaging calculation</p></dd>
 
 
-<dt>divisor_override</dt>
+<dt id="arg-divisor-override">divisor_override<a class="anchor" aria-label="anchor" href="#arg-divisor-override"></a></dt>
 <dd><p>NA if specified, it will be used as divisor, otherwise
 size of the pooling region will be used. Default: <code>NULL</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_batch_norm.html b/reference/nnf_batch_norm.html
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-      <img src="" class="logo" alt=""><h1>Batch_norm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-batchnorm.R" class="external-link"><code>R/nnf-batchnorm.R</code></a></small>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-batchnorm.R" class="external-link"><code>R/nnf-batchnorm.R</code></a></small>
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@@ -104,41 +86,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor</p></dd>
 
 
-<dt>running_mean</dt>
+<dt id="arg-running-mean">running_mean<a class="anchor" aria-label="anchor" href="#arg-running-mean"></a></dt>
 <dd><p>the running_mean tensor</p></dd>
 
 
-<dt>running_var</dt>
+<dt id="arg-running-var">running_var<a class="anchor" aria-label="anchor" href="#arg-running-var"></a></dt>
 <dd><p>the running_var tensor</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>the weight tensor</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>the bias tensor</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>bool wether it's training. Default: FALSE</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
 Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
 
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diff --git a/reference/nnf_bilinear.html b/reference/nnf_bilinear.html
index d63dd60029..5f0ec43232 100644
--- a/reference/nnf_bilinear.html
+++ b/reference/nnf_bilinear.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a bilinear transformation to the incoming data:
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 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Bilinear</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-linear.R" class="external-link"><code>R/nnf-linear.R</code></a></small>
+
+      <h1>Bilinear</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-linear.R" class="external-link"><code>R/nnf-linear.R</code></a></small>
       <div class="d-none name"><code>nnf_bilinear.Rd</code></div>
     </div>
 
@@ -98,36 +80,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input1</dt>
+
+
+<dl><dt id="arg-input-">input1<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
 and \(*\) means any number of additional dimensions.
 All but the last dimension of the inputs should be the same.</p></dd>
 
 
-<dt>input2</dt>
+<dt id="arg-input-">input2<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>\((\mbox{out\_features}, \mbox{in1\_features},
 \mbox{in2\_features})\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>\((\mbox{out\_features})\)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)</p>
-
-
-<p>and all but the last dimension are the same shape as the input.</p>
+    <p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
+and all but the last dimension are the same shape as the input.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -136,14 +116,14 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_binary_cross_entropy.html b/reference/nnf_binary_cross_entropy.html
index 2f03256f35..82acf3fbef 100644
--- a/reference/nnf_binary_cross_entropy.html
+++ b/reference/nnf_binary_cross_entropy.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Function that measures the Binary Cross Entropy
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-      <img src="" class="logo" alt=""><h1>Binary_cross_entropy</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Binary_cross_entropy</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_binary_cross_entropy.Rd</code></div>
     </div>
 
@@ -103,19 +85,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(tensor) weight for each value.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -123,7 +107,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
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@@ -132,14 +116,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nnf_binary_cross_entropy_with_logits.html b/reference/nnf_binary_cross_entropy_with_logits.html
index 67a146079a..1141778562 100644
--- a/reference/nnf_binary_cross_entropy_with_logits.html
+++ b/reference/nnf_binary_cross_entropy_with_logits.html
@@ -1,10 +1,7 @@
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+      <h1>Binary_cross_entropy_with_logits</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
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@@ -104,33 +86,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
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 <dd><p>(Tensor, optional) a manual rescaling weight if provided it's
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+<dt id="arg-pos-weight">pos_weight<a class="anchor" aria-label="anchor" href="#arg-pos-weight"></a></dt>
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diff --git a/reference/nnf_celu.html b/reference/nnf_celu.html
index 3cd3af00b1..273c46acf0 100644
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 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Celu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Celu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -97,21 +79,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the alpha value for the CELU formulation. Default: 1.0</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_celu_.html b/reference/nnf_celu_.html
new file mode 100644
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index 39cef3697b..7d59adf964 100644
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-      <img src="" class="logo" alt=""><h1>Sparsemax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Sparsemax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_contrib_sparsemax.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>The dimension over which to apply the sparsemax function. (-1)</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 The implementation is based on <a href="https://github.com/aced125/sparsemax/tree/master/sparsemax" class="external-link">aced125/sparsemax</a></p>
     </div>
 
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diff --git a/reference/nnf_conv1d.html b/reference/nnf_conv1d.html
index 6cfff14452..7e8d832051 100644
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+++ b/reference/nnf_conv1d.html
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Conv1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
       <div class="d-none name"><code>nnf_conv1d.Rd</code></div>
     </div>
 
@@ -106,40 +88,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch, in_channels , iW)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape (out_channels, in_channels/groups , kW)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or
 a one-element tuple <code>(sW,)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a
 single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or
 a one-element tuple <code>(dW,)</code>. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, <code>in_channels</code> should be divisible by
 the number of groups. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
 
-  
+
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diff --git a/reference/nnf_conv2d.html b/reference/nnf_conv2d.html
index 620cfd0a1c..0940342580 100644
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+++ b/reference/nnf_conv2d.html
@@ -1,10 +1,7 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
       <div class="d-none name"><code>nnf_conv2d.Rd</code></div>
     </div>
 
@@ -106,40 +88,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a
 tuple <code>(sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a
 single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or
 a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, <code>in_channels</code> should be divisible by the
 number of groups. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_conv3d.html b/reference/nnf_conv3d.html
index 441158a593..3c915f7b5c 100644
--- a/reference/nnf_conv3d.html
+++ b/reference/nnf_conv3d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 3D convolution over an input image composed of several input
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
       <div class="d-none name"><code>nnf_conv3d.Rd</code></div>
     </div>
 
@@ -106,40 +88,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a
 tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a
 single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or
 a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, <code>in_channels</code> should be divisible by
 the number of groups. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/nnf_conv_tbc.html b/reference/nnf_conv_tbc.html
index 143db549a1..117ba3e61e 100644
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+++ b/reference/nnf_conv_tbc.html
@@ -1,10 +1,7 @@
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-Input and output dimensions are (Time, Batch, Channels) - hence TBC."><title>Conv_tbc — nnf_conv_tbc • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv_tbc — nnf_conv_tbc"><meta property="og:description" content="Applies a 1-dimensional sequence convolution over an input sequence.
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+Input and output dimensions are (Time, Batch, Channels) - hence TBC."><meta property="og:description" content="Applies a 1-dimensional sequence convolution over an input sequence.
+Input and output dimensions are (Time, Batch, Channels) - hence TBC."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Conv_tbc</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv_tbc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
       <div class="d-none name"><code>nnf_conv_tbc.Rd</code></div>
     </div>
 
@@ -98,26 +80,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{sequence length} \times
 batch \times \mbox{in\_channels})\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels}
 \times \mbox{out\_channels}\))</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>bias of shape (\(\mbox{out\_channels}\))</p></dd>
 
 
-<dt>pad</dt>
+<dt id="arg-pad">pad<a class="anchor" aria-label="anchor" href="#arg-pad"></a></dt>
 <dd><p>number of timesteps to pad. Default: 0</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/nnf_conv_transpose1d.html b/reference/nnf_conv_transpose1d.html
index 8a77539379..25ab96859f 100644
--- a/reference/nnf_conv_transpose1d.html
+++ b/reference/nnf_conv_transpose1d.html
@@ -1,10 +1,7 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".'><title>Conv_transpose1d — nnf_conv_transpose1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv_transpose1d — nnf_conv_transpose1d"><meta property="og:description" content='Applies a 1D transposed convolution operator over an input signal
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Conv_transpose1d — nnf_conv_transpose1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv_transpose1d — nnf_conv_transpose1d"><meta name="description" content='Applies a 1D transposed convolution operator over an input signal
+composed of several input planes, sometimes also called "deconvolution".'><meta property="og:description" content='Applies a 1D transposed convolution operator over an input signal
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 </nav><div class="container template-reference-topic">
 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Conv_transpose1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv_transpose1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
       <div class="d-none name"><code>nnf_conv_transpose1d.Rd</code></div>
     </div>
 
@@ -107,44 +89,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch, in_channels , iW)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape (out_channels, in_channels/groups , kW)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or
 a one-element tuple <code>(sW,)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a
 single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>padding applied to the output</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, <code>in_channels</code> should be divisible by
 the number of groups. Default: 1</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or
 a one-element tuple <code>(dW,)</code>. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -153,14 +137,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
 
-  
+
+
 
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diff --git a/reference/nnf_conv_transpose2d.html b/reference/nnf_conv_transpose2d.html
index e133706e21..337fb28a55 100644
--- a/reference/nnf_conv_transpose2d.html
+++ b/reference/nnf_conv_transpose2d.html
@@ -1,10 +1,7 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Conv_transpose2d — nnf_conv_transpose2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv_transpose2d — nnf_conv_transpose2d"><meta name="description" content='Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution".'><meta property="og:description" content='Applies a 2D transposed convolution operator over an input image
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-      <img src="" class="logo" alt=""><h1>Conv_transpose2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv_transpose2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
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@@ -107,44 +89,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a
 tuple <code>(sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a
 single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>padding applied to the output</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, <code>in_channels</code> should be divisible by the
 number of groups. Default: 1</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or
 a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -153,14 +137,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/nnf_conv_transpose3d.html b/reference/nnf_conv_transpose3d.html
index fa5d35a0c6..65edc24ffa 100644
--- a/reference/nnf_conv_transpose3d.html
+++ b/reference/nnf_conv_transpose3d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-composed of several input planes, sometimes also called "deconvolution"'><title>Conv_transpose3d — nnf_conv_transpose3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv_transpose3d — nnf_conv_transpose3d"><meta property="og:description" content='Applies a 3D transposed convolution operator over an input image
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+composed of several input planes, sometimes also called "deconvolution"'><meta property="og:description" content='Applies a 3D transposed convolution operator over an input image
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Conv_transpose3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
+
+      <h1>Conv_transpose3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-conv.R" class="external-link"><code>R/nnf-conv.R</code></a></small>
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@@ -107,44 +89,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a
 tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a
 single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>padding applied to the output</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, <code>in_channels</code> should be divisible by
 the number of groups. Default: 1</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or
 a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -153,14 +137,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/nnf_cosine_embedding_loss.html b/reference/nnf_cosine_embedding_loss.html
index d87041d70d..d53d36a81f 100644
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+++ b/reference/nnf_cosine_embedding_loss.html
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cosine_embedding_loss — nnf_cosine_embedding_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cosine_embedding_loss — nnf_cosine_embedding_loss"><meta name="description" content="Creates a criterion that measures the loss given input tensors x_1, x_2 and a
 Tensor label y with values 1 or -1. This is used for measuring whether two inputs
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-for learning nonlinear embeddings or semi-supervised learning."><title>Cosine_embedding_loss — nnf_cosine_embedding_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cosine_embedding_loss — nnf_cosine_embedding_loss"><meta property="og:description" content="Creates a criterion that measures the loss given input tensors x_1, x_2 and a
+for learning nonlinear embeddings or semi-supervised learning."><meta property="og:description" content="Creates a criterion that measures the loss given input tensors x_1, x_2 and a
 Tensor label y with values 1 or -1. This is used for measuring whether two inputs
 are similar or dissimilar, using the cosine distance, and is typically used
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-      <img src="" class="logo" alt=""><h1>Cosine_embedding_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Cosine_embedding_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_cosine_embedding_loss.Rd</code></div>
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@@ -110,24 +92,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input1</dt>
+
+
+<dl><dt id="arg-input-">input1<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>the input x_1 tensor</p></dd>
 
 
-<dt>input2</dt>
+<dt id="arg-input-">input2<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>the input x_2 tensor</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>the target tensor</p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
 is missing, the default value is 0.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -135,7 +119,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
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@@ -144,14 +128,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nnf_cosine_similarity.html b/reference/nnf_cosine_similarity.html
index c296555858..e2a4b093a3 100644
--- a/reference/nnf_cosine_similarity.html
+++ b/reference/nnf_cosine_similarity.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Cosine_similarity</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-distance.R" class="external-link"><code>R/nnf-distance.R</code></a></small>
+
+      <h1>Cosine_similarity</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-distance.R" class="external-link"><code>R/nnf-distance.R</code></a></small>
       <div class="d-none name"><code>nnf_cosine_similarity.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x1</dt>
+
+
+<dl><dt id="arg-x-">x1<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) First input.</p></dd>
 
 
-<dt>x2</dt>
+<dt id="arg-x-">x2<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) Second input (of size matching x1).</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) Dimension of vectors. Default: 2</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional) Small value to avoid division by zero.
 Default: 1e-8</p></dd>
 
@@ -119,7 +103,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 $$</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_cross_entropy.html b/reference/nnf_cross_entropy.html
index c1ca210031..761c59671e 100644
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+++ b/reference/nnf_cross_entropy.html
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-      <img src="" class="logo" alt=""><h1>Cross_entropy</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Cross_entropy</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_cross_entropy.Rd</code></div>
     </div>
 
@@ -104,28 +86,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
 in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
 in the case of K-dimensional loss.</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
 or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional) a manual rescaling weight given to each class. If
 given, has to be a Tensor of size <code>C</code></p></dd>
 
 
-<dt>ignore_index</dt>
+<dt id="arg-ignore-index">ignore_index<a class="anchor" aria-label="anchor" href="#arg-ignore-index"></a></dt>
 <dd><p>(int, optional) Specifies a target value that is ignored
 and does not contribute to the input gradient.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -133,7 +117,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -142,14 +126,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_ctc_loss.html b/reference/nnf_ctc_loss.html
index 85d7289d41..adb0132c95 100644
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+++ b/reference/nnf_ctc_loss.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Ctc_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Ctc_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_ctc_loss.Rd</code></div>
     </div>
 
@@ -103,44 +85,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>log_probs</dt>
+
+
+<dl><dt id="arg-log-probs">log_probs<a class="anchor" aria-label="anchor" href="#arg-log-probs"></a></dt>
 <dd><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
 T = input length, and N = batch size. The logarithmized probabilities of
 the outputs (e.g. obtained with <a href="nnf_log_softmax.html">nnf_log_softmax</a>).</p></dd>
 
 
-<dt>targets</dt>
+<dt id="arg-targets">targets<a class="anchor" aria-label="anchor" href="#arg-targets"></a></dt>
 <dd><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
 In the second form, the targets are assumed to be concatenated.</p></dd>
 
 
-<dt>input_lengths</dt>
+<dt id="arg-input-lengths">input_lengths<a class="anchor" aria-label="anchor" href="#arg-input-lengths"></a></dt>
 <dd><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></dd>
 
 
-<dt>target_lengths</dt>
+<dt id="arg-target-lengths">target_lengths<a class="anchor" aria-label="anchor" href="#arg-target-lengths"></a></dt>
 <dd><p>\((N)\). Lengths of the targets</p></dd>
 
 
-<dt>blank</dt>
+<dt id="arg-blank">blank<a class="anchor" aria-label="anchor" href="#arg-blank"></a></dt>
 <dd><p>(int, optional) Blank label. Default \(0\).</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
 'sum': the output will be summed. Default: 'mean'</p></dd>
 
 
-<dt>zero_infinity</dt>
+<dt id="arg-zero-infinity">zero_infinity<a class="anchor" aria-label="anchor" href="#arg-zero-infinity"></a></dt>
 <dd><p>(bool, optional) Whether to zero infinite losses and the
 associated gradients. Default: <code>FALSE</code> Infinite losses mainly occur when the
 inputs are too short to be aligned to the targets.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -149,14 +133,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_dropout.html b/reference/nnf_dropout.html
index 67e999651f..00f3302a6c 100644
--- a/reference/nnf_dropout.html
+++ b/reference/nnf_dropout.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="During training, randomly zeroes some of the elements of the input
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dropout — nnf_dropout • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout — nnf_dropout"><meta name="description" content="During training, randomly zeroes some of the elements of the input
 tensor with probability p using samples from a Bernoulli
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+distribution."><meta property="og:description" content="During training, randomly zeroes some of the elements of the input
 tensor with probability p using samples from a Bernoulli
-distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Dropout</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
+
+      <h1>Dropout</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
       <div class="d-none name"><code>nnf_dropout.Rd</code></div>
     </div>
 
@@ -101,25 +83,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>probability of an element to be zeroed. Default: 0.5</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>If set to <code>TRUE</code>, will do this operation in-place.
 Default: <code>FALSE</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -128,14 +112,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_dropout2d.html b/reference/nnf_dropout2d.html
index a21df63fa2..f841a2d3a6 100644
--- a/reference/nnf_dropout2d.html
+++ b/reference/nnf_dropout2d.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Randomly zero out entire channels (a channel is a 2D feature map,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dropout2d — nnf_dropout2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout2d — nnf_dropout2d"><meta name="description" content="Randomly zero out entire channels (a channel is a 2D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
 Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution."><title>Dropout2d — nnf_dropout2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout2d — nnf_dropout2d"><meta property="og:description" content="Randomly zero out entire channels (a channel is a 2D feature map,
+probability p using samples from a Bernoulli distribution."><meta property="og:description" content="Randomly zero out entire channels (a channel is a 2D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
 Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Dropout2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
+
+      <h1>Dropout2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
       <div class="d-none name"><code>nnf_dropout2d.Rd</code></div>
     </div>
 
@@ -107,25 +89,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>probability of a channel to be zeroed. Default: 0.5</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>If set to <code>TRUE</code>, will do this operation in-place.
 Default: <code>FALSE</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -134,14 +118,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_dropout3d.html b/reference/nnf_dropout3d.html
index a9216ef785..e843778a51 100644
--- a/reference/nnf_dropout3d.html
+++ b/reference/nnf_dropout3d.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Randomly zero out entire channels (a channel is a 3D feature map,
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 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
 Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution."><title>Dropout3d — nnf_dropout3d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dropout3d — nnf_dropout3d"><meta property="og:description" content="Randomly zero out entire channels (a channel is a 3D feature map,
+probability p using samples from a Bernoulli distribution."><meta property="og:description" content="Randomly zero out entire channels (a channel is a 3D feature map,
 e.g., the \(j\)-th channel of the \(i\)-th sample in the
 batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
 Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
+      <h1>Dropout3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-dropout.R" class="external-link"><code>R/nnf-dropout.R</code></a></small>
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@@ -107,25 +89,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>probability of a channel to be zeroed. Default: 0.5</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>If set to <code>TRUE</code>, will do this operation in-place.
 Default: <code>FALSE</code></p></dd>
 
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 </div>
 
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index 113e5c3f57..4ce16e5937 100644
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@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Elu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Elu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -100,16 +82,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the alpha value for the ELU formulation. Default: 1.0</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -134,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nnf_elu_.html b/reference/nnf_elu_.html
new file mode 100644
index 0000000000..40183cd279
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@@ -0,0 +1,8 @@
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index 81a4a91ccc..ad933e4284 100644
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-      <img src="" class="logo" alt=""><h1>Embedding</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
+
+      <h1>Embedding</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
       <div class="d-none name"><code>nnf_embedding.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(LongTensor) Tensor containing indices into the embedding matrix</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor) The embedding matrix with number of rows equal to the
 maximum possible index + 1, and number of columns equal to the embedding size</p></dd>
 
 
-<dt>padding_idx</dt>
+<dt id="arg-padding-idx">padding_idx<a class="anchor" aria-label="anchor" href="#arg-padding-idx"></a></dt>
 <dd><p>(int, optional) If given, pads the output with the embedding
 vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></dd>
 
 
-<dt>max_norm</dt>
+<dt id="arg-max-norm">max_norm<a class="anchor" aria-label="anchor" href="#arg-max-norm"></a></dt>
 <dd><p>(float, optional) If given, each embedding vector with norm larger
 than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>. Note: this will modify
 <code>weight</code> in-place.</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
 option. Default <code>2</code>.</p></dd>
 
 
-<dt>scale_grad_by_freq</dt>
+<dt id="arg-scale-grad-by-freq">scale_grad_by_freq<a class="anchor" aria-label="anchor" href="#arg-scale-grad-by-freq"></a></dt>
 <dd><p>(boolean, optional) If given, this will scale gradients
 by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></dd>
 
 
-<dt>sparse</dt>
+<dt id="arg-sparse">sparse<a class="anchor" aria-label="anchor" href="#arg-sparse"></a></dt>
 <dd><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
 sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
 sparse gradients.</p></dd>
@@ -146,7 +130,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 and the output is the corresponding word embeddings.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -155,14 +139,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_embedding_bag.html b/reference/nnf_embedding_bag.html
index c75506f8db..aa63c8bda5 100644
--- a/reference/nnf_embedding_bag.html
+++ b/reference/nnf_embedding_bag.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Embedding_bag</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
+
+      <h1>Embedding_bag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
       <div class="d-none name"><code>nnf_embedding_bag.Rd</code></div>
     </div>
 
@@ -110,66 +92,68 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor) The embedding matrix with number of rows equal to the
 maximum possible index + 1, and number of columns equal to the embedding size</p></dd>
 
 
-<dt>offsets</dt>
+<dt id="arg-offsets">offsets<a class="anchor" aria-label="anchor" href="#arg-offsets"></a></dt>
 <dd><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
 determines the starting index position of each bag (sequence) in <code>input</code>.</p></dd>
 
 
-<dt>max_norm</dt>
+<dt id="arg-max-norm">max_norm<a class="anchor" aria-label="anchor" href="#arg-max-norm"></a></dt>
 <dd><p>(float, optional) If given, each embedding vector with norm
 larger than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>.
 Note: this will modify <code>weight</code> in-place.</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
 <code>max_norm</code> option. Default <code>2</code>.</p></dd>
 
 
-<dt>scale_grad_by_freq</dt>
+<dt id="arg-scale-grad-by-freq">scale_grad_by_freq<a class="anchor" aria-label="anchor" href="#arg-scale-grad-by-freq"></a></dt>
 <dd><p>(boolean, optional) if given, this will scale gradients
 by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
 the way to reduce the bag. Default: 'mean'</p></dd>
 
 
-<dt>sparse</dt>
+<dt id="arg-sparse">sparse<a class="anchor" aria-label="anchor" href="#arg-sparse"></a></dt>
 <dd><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
 sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
 sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></dd>
 
 
-<dt>per_sample_weights</dt>
+<dt id="arg-per-sample-weights">per_sample_weights<a class="anchor" aria-label="anchor" href="#arg-per-sample-weights"></a></dt>
 <dd><p>(Tensor, optional) a tensor of float / double weights,
 or NULL to indicate all weights should be taken to be 1. If specified,
 <code>per_sample_weights</code> must have exactly the same shape as input and is treated
 as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></dd>
 
 
-<dt>include_last_offset</dt>
+<dt id="arg-include-last-offset">include_last_offset<a class="anchor" aria-label="anchor" href="#arg-include-last-offset"></a></dt>
 <dd><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
 equal to the number of bags + 1.</p></dd>
 
 
-<dt>padding_idx</dt>
+<dt id="arg-padding-idx">padding_idx<a class="anchor" aria-label="anchor" href="#arg-padding-idx"></a></dt>
 <dd><p>(int, optional) If given, pads the output with the embedding
 vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -178,14 +162,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nnf_fold.html b/reference/nnf_fold.html
index 22119d5636..17eab9147e 100644
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@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-fold.R" class="external-link"><code>R/nnf-fold.R</code></a></small>
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@@ -105,44 +87,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the shape of the spatial dimensions of the output (i.e.,
 <code>output$sizes()[-c(1,2)]</code>)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the sliding blocks</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>a parameter that controls the stride of elements within the
 neighborhood. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on both sides of input.
 Default: 0</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the sliding blocks in the input spatial dimensions.
 Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 
 <p>Currently, only 4-D output tensors (batched image-like tensors) are
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diff --git a/reference/nnf_fractional_max_pool2d.html b/reference/nnf_fractional_max_pool2d.html
index fa02092fec..fafdaa51ef 100644
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+++ b/reference/nnf_fractional_max_pool2d.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Fractional_max_pool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
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@@ -102,31 +84,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over. Can be a
 single number \(k\) (for a square kernel of \(k * k\)) or
 a tuple <code>(kH, kW)</code></p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the image of the form \(oH * oW\).
 Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></dd>
 
 
-<dt>output_ratio</dt>
+<dt id="arg-output-ratio">output_ratio<a class="anchor" aria-label="anchor" href="#arg-output-ratio"></a></dt>
 <dd><p>If one wants to have an output size as a ratio of the input size,
 this option can be given. This has to be a number or tuple in the range (0, 1)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>True</code>, will return the indices along with the outputs.</p></dd>
 
 
-<dt>random_samples</dt>
+<dt id="arg-random-samples">random_samples<a class="anchor" aria-label="anchor" href="#arg-random-samples"></a></dt>
 <dd><p>optional random samples.</p></dd>
 
 </dl></div>
@@ -138,7 +122,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/nnf_fractional_max_pool3d.html b/reference/nnf_fractional_max_pool3d.html
index ebf0d0ecb3..67811d022f 100644
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+++ b/reference/nnf_fractional_max_pool3d.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Fractional_max_pool3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Fractional_max_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_fractional_max_pool3d.Rd</code></div>
     </div>
 
@@ -102,32 +84,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window to take a max over. Can be a single number \(k\)
 (for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size of the form \(oT * oH * oW\).
 Can be a tuple <code>(oT, oH, oW)</code> or a single number \(oH\) for a cubic output
 \(oH * oH * oH\)</p></dd>
 
 
-<dt>output_ratio</dt>
+<dt id="arg-output-ratio">output_ratio<a class="anchor" aria-label="anchor" href="#arg-output-ratio"></a></dt>
 <dd><p>If one wants to have an output size as a ratio of the
 input size, this option can be given. This has to be a number or tuple in the
 range (0, 1)</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>if <code>True</code>, will return the indices along with the outputs.</p></dd>
 
 
-<dt>random_samples</dt>
+<dt id="arg-random-samples">random_samples<a class="anchor" aria-label="anchor" href="#arg-random-samples"></a></dt>
 <dd><p>undocumented argument.</p></dd>
 
 </dl></div>
@@ -139,7 +123,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 The number of output features is equal to the number of input planes.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
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diff --git a/reference/nnf_gelu.html b/reference/nnf_gelu.html
index 9e7666402a..f4c18100a5 100644
--- a/reference/nnf_gelu.html
+++ b/reference/nnf_gelu.html
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Gelu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Gelu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_gelu.Rd</code></div>
     </div>
 
@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>approximate</dt>
+<dt id="arg-approximate">approximate<a class="anchor" aria-label="anchor" href="#arg-approximate"></a></dt>
 <dd><p>By default it's none, and applies element-wise x*pnorm(x),
 if 'tanh', then GELU is estimated. See <a href="https://arxiv.org/abs/1606.08415" class="external-link">GELU</a> for
 more info.</p></dd>
@@ -108,7 +92,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="gelu-input-gt-tensor-">gelu(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gelu-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies element-wise the function
@@ -118,7 +102,7 @@ <h2 id="gelu-input-gt-tensor-">gelu(input) -&gt; Tensor <a class="anchor" aria-l
 <p>See <a href="https://arxiv.org/abs/1606.08415" class="external-link">Gaussian Error Linear Units (GELUs)</a>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -127,14 +111,14 @@ <h2 id="gelu-input-gt-tensor-">gelu(input) -&gt; Tensor <a class="anchor" aria-l
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_glu.html b/reference/nnf_glu.html
index 8d99d97e8a..ccb8779011 100644
--- a/reference/nnf_glu.html
+++ b/reference/nnf_glu.html
@@ -1,8 +1,5 @@
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Glu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Glu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_glu.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) input tensor</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension on which to split the input. Default: -1</p></dd>
 
 </dl></div>
@@ -112,7 +96,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <p>See <a href="https://arxiv.org/abs/1612.08083" class="external-link">Language Modeling with Gated Convolutional Networks</a>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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diff --git a/reference/nnf_grid_sample.html b/reference/nnf_grid_sample.html
index 38fd5c1938..8e9c346d53 100644
--- a/reference/nnf_grid_sample.html
+++ b/reference/nnf_grid_sample.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Given an input and a flow-field grid, computes the
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+
+      <h1>Grid_sample</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-vision.R" class="external-link"><code>R/nnf-vision.R</code></a></small>
       <div class="d-none name"><code>nnf_grid_sample.Rd</code></div>
     </div>
 
@@ -104,25 +86,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></dd>
 
 
-<dt>grid</dt>
+<dt id="arg-grid">grid<a class="anchor" aria-label="anchor" href="#arg-grid"></a></dt>
 <dd><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
 Default: <code>'bilinear'</code></p></dd>
 
 
-<dt>padding_mode</dt>
+<dt id="arg-padding-mode">padding_mode<a class="anchor" aria-label="anchor" href="#arg-padding-mode"></a></dt>
 <dd><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
 | <code>'reflection'</code>. Default: <code>'zeros'</code></p></dd>
 
 
-<dt>align_corners</dt>
+<dt id="arg-align-corners">align_corners<a class="anchor" aria-label="anchor" href="#arg-align-corners"></a></dt>
 <dd><p>(bool, optional) Geometrically, we consider the pixels of the
 input  as squares rather than points. If set to <code>True</code>, the extrema (<code>-1</code> and
 <code>1</code>) are considered as referring to the center points of the input's corner pixels.
@@ -165,14 +149,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>This function is often used in conjunction with <code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code>
 to build <code>Spatial Transformer Networks</code>_ .</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -181,14 +165,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nnf_group_norm.html b/reference/nnf_group_norm.html
index de4e0bf268..2962dc141d 100644
--- a/reference/nnf_group_norm.html
+++ b/reference/nnf_group_norm.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies Group Normalization for last certain number of dimensions."><title>Group_norm — nnf_group_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Group_norm — nnf_group_norm"><meta property="og:description" content="Applies Group Normalization for last certain number of dimensions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Group_norm — nnf_group_norm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Group_norm — nnf_group_norm"><meta name="description" content="Applies Group Normalization for last certain number of dimensions."><meta property="og:description" content="Applies Group Normalization for last certain number of dimensions."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Group_norm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
+
+      <h1>Group_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
       <div class="d-none name"><code>nnf_group_norm.Rd</code></div>
     </div>
 
@@ -95,28 +77,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>num_groups</dt>
+<dt id="arg-num-groups">num_groups<a class="anchor" aria-label="anchor" href="#arg-num-groups"></a></dt>
 <dd><p>number of groups to separate the channels into</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>the weight tensor</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>the bias tensor</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -125,14 +109,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_gumbel_softmax.html b/reference/nnf_gumbel_softmax.html
index 6b09ac0dba..7aaf4f4c5b 100644
--- a/reference/nnf_gumbel_softmax.html
+++ b/reference/nnf_gumbel_softmax.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Gumbel_softmax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Gumbel_softmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_gumbel_softmax.Rd</code></div>
     </div>
 
@@ -98,24 +80,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>logits</dt>
+
+
+<dl><dt id="arg-logits">logits<a class="anchor" aria-label="anchor" href="#arg-logits"></a></dt>
 <dd><p><code>[..., num_features]</code> unnormalized log probabilities</p></dd>
 
 
-<dt>tau</dt>
+<dt id="arg-tau">tau<a class="anchor" aria-label="anchor" href="#arg-tau"></a></dt>
 <dd><p>non-negative scalar temperature</p></dd>
 
 
-<dt>hard</dt>
+<dt id="arg-hard">hard<a class="anchor" aria-label="anchor" href="#arg-hard"></a></dt>
 <dd><p>if <code>True</code>, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) A dimension along which softmax will be computed. Default: -1.</p></dd>
 
 </dl></div>
 
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@@ -124,14 +108,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nnf_hardshrink.html b/reference/nnf_hardshrink.html
index b92516f36d..6aa084ff42 100644
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+++ b/reference/nnf_hardshrink.html
@@ -1,8 +1,5 @@
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diff --git a/reference/nnf_hardsigmoid.html b/reference/nnf_hardsigmoid.html
index cc83c66230..7332d34bec 100644
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+
+      <h1>Hardsigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_hardsigmoid.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>NA If set to <code>True</code>, will do this operation in-place. Default: <code>False</code></p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -114,14 +98,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_hardswish.html b/reference/nnf_hardswish.html
index f922725fd5..84cac44104 100644
--- a/reference/nnf_hardswish.html
+++ b/reference/nnf_hardswish.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Hardswish</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Hardswish</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_hardswish.Rd</code></div>
     </div>
 
@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
@@ -118,7 +102,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
   \right. $$</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_hardtanh.html b/reference/nnf_hardtanh.html
index 29b4d4dc13..86e5751bb8 100644
--- a/reference/nnf_hardtanh.html
+++ b/reference/nnf_hardtanh.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies the HardTanh function element-wise."><title>Hardtanh — nnf_hardtanh • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Hardtanh — nnf_hardtanh"><meta property="og:description" content="Applies the HardTanh function element-wise."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Hardtanh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Hardtanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_hardtanh.Rd</code></div>
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@@ -97,25 +79,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
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+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>min_val</dt>
+<dt id="arg-min-val">min_val<a class="anchor" aria-label="anchor" href="#arg-min-val"></a></dt>
 <dd><p>minimum value of the linear region range. Default: -1</p></dd>
 
 
-<dt>max_val</dt>
+<dt id="arg-max-val">max_val<a class="anchor" aria-label="anchor" href="#arg-max-val"></a></dt>
 <dd><p>maximum value of the linear region range. Default: 1</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
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diff --git a/reference/nnf_hardtanh_.html b/reference/nnf_hardtanh_.html
new file mode 100644
index 0000000000..300df719c4
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@@ -0,0 +1,8 @@
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diff --git a/reference/nnf_hinge_embedding_loss.html b/reference/nnf_hinge_embedding_loss.html
index 0e42da4bc8..327875edf6 100644
--- a/reference/nnf_hinge_embedding_loss.html
+++ b/reference/nnf_hinge_embedding_loss.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hinge_embedding_loss — nnf_hinge_embedding_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Hinge_embedding_loss — nnf_hinge_embedding_loss"><meta name="description" content="Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
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+embeddings or semi-supervised learning."><meta property="og:description" content="Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
 This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
 using the L1 pairwise distance as xx , and is typically used for learning nonlinear
-embeddings or semi-supervised learning."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
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@@ -104,19 +86,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>Has a default value of 1.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
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 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
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@@ -124,7 +108,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_instance_norm.html b/reference/nnf_instance_norm.html
index f3ae4b4eb5..e90ce6a7e8 100644
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@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Instance_norm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-instancenorm.R" class="external-link"><code>R/nnf-instancenorm.R</code></a></small>
+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-instancenorm.R" class="external-link"><code>R/nnf-instancenorm.R</code></a></small>
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@@ -107,40 +89,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>running_mean</dt>
+<dt id="arg-running-mean">running_mean<a class="anchor" aria-label="anchor" href="#arg-running-mean"></a></dt>
 <dd><p>the running_mean tensor</p></dd>
 
 
-<dt>running_var</dt>
+<dt id="arg-running-var">running_var<a class="anchor" aria-label="anchor" href="#arg-running-var"></a></dt>
 <dd><p>the running var tensor</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>the weight tensor</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>the bias tensor</p></dd>
 
 
-<dt>use_input_stats</dt>
+<dt id="arg-use-input-stats">use_input_stats<a class="anchor" aria-label="anchor" href="#arg-use-input-stats"></a></dt>
 <dd><p>whether to use input stats</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>a double for the momentum</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>an eps double for numerical stability</p></dd>
 
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_interpolate.html b/reference/nnf_interpolate.html
index 704c18775d..278f2cc568 100644
--- a/reference/nnf_interpolate.html
+++ b/reference/nnf_interpolate.html
@@ -1,10 +1,7 @@
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Interpolate</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-upsampling.R" class="external-link"><code>R/nnf-upsampling.R</code></a></small>
+
+      <h1>Interpolate</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-upsampling.R" class="external-link"><code>R/nnf-upsampling.R</code></a></small>
       <div class="d-none name"><code>nnf_interpolate.Rd</code></div>
     </div>
 
@@ -105,26 +87,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
 output spatial size.</p></dd>
 
 
-<dt>scale_factor</dt>
+<dt id="arg-scale-factor">scale_factor<a class="anchor" aria-label="anchor" href="#arg-scale-factor"></a></dt>
 <dd><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
 Has to match input size if it is a tuple.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
 | 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></dd>
 
 
-<dt>align_corners</dt>
+<dt id="arg-align-corners">align_corners<a class="anchor" aria-label="anchor" href="#arg-align-corners"></a></dt>
 <dd><p>(bool, optional) Geometrically, we consider the pixels
 of the input and output as squares rather than points. If set to TRUE,
 the input and output tensors are aligned by the center points of their corner
@@ -136,11 +120,11 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></dd>
 
 
-<dt>recompute_scale_factor</dt>
+<dt id="arg-recompute-scale-factor">recompute_scale_factor<a class="anchor" aria-label="anchor" href="#arg-recompute-scale-factor"></a></dt>
 <dd><p>(bool, optional) recompute the scale_factor
 for use in the interpolation calculation.  When <code>scale_factor</code> is passed
 as a parameter, it is used to compute the <code>output_size</code>.  If <code>recompute_scale_factor</code>
-is ```True`` or not specified, a new <code>scale_factor</code> will be computed based on
+is “`True“ or not specified, a new <code>scale_factor</code> will be computed based on
 the output and input sizes for use in the interpolation computation (i.e. the
 computation will be identical to if the computed `output_size` were passed-in
 explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
@@ -160,7 +144,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <code>bilinear</code>, <code>bicubic</code> (4D-only), <code>trilinear</code> (5D-only), <code>area</code></p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -169,14 +153,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_kl_div.html b/reference/nnf_kl_div.html
index d413dfc61e..bdc2ff38bc 100644
--- a/reference/nnf_kl_div.html
+++ b/reference/nnf_kl_div.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The Kullback-Leibler divergence Loss."><title>Kl_div — nnf_kl_div • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Kl_div — nnf_kl_div"><meta property="og:description" content="The Kullback-Leibler divergence Loss."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Kl_div</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Kl_div</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_kl_div.Rd</code></div>
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@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -111,7 +95,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
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diff --git a/reference/nnf_l1_loss.html b/reference/nnf_l1_loss.html
index 65b5ef7862..b9c583bc61 100644
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
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@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
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@@ -111,7 +95,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
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diff --git a/reference/nnf_layer_norm.html b/reference/nnf_layer_norm.html
index bc2d68991c..070a576538 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Layer_norm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
+
+      <h1>Layer_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
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@@ -101,30 +83,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>normalized_shape</dt>
+<dt id="arg-normalized-shape">normalized_shape<a class="anchor" aria-label="anchor" href="#arg-normalized-shape"></a></dt>
 <dd><p>input shape from an expected input of size. If a single
 integer is used, it is treated as a singleton list, and this module will normalize
 over the last dimension which is expected to be of that specific size.</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>the weight tensor</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>the bias tensor</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>a value added to the denominator for numerical stability. Default: 1e-5</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -133,14 +117,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nnf_leaky_relu.html b/reference/nnf_leaky_relu.html
index e7e34a5064..ccb814aeda 100644
--- a/reference/nnf_leaky_relu.html
+++ b/reference/nnf_leaky_relu.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Leaky_relu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Leaky_relu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_leaky_relu.Rd</code></div>
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@@ -98,21 +80,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>negative_slope</dt>
+<dt id="arg-negative-slope">negative_slope<a class="anchor" aria-label="anchor" href="#arg-negative-slope"></a></dt>
 <dd><p>Controls the angle of the negative slope. Default: 1e-2</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
 
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diff --git a/reference/nnf_linear.html b/reference/nnf_linear.html
index ab845c9ff2..c036444636 100644
--- a/reference/nnf_linear.html
+++ b/reference/nnf_linear.html
@@ -1,8 +1,5 @@
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@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>\((out\_features, in\_features)\) the weights tensor.</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional tensor \((out\_features)\)</p></dd>
 
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diff --git a/reference/nnf_local_response_norm.html b/reference/nnf_local_response_norm.html
index 8e405eec1b..da3f9bca84 100644
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+
+      <h1>Local_response_norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
       <div class="d-none name"><code>nnf_local_response_norm.Rd</code></div>
     </div>
 
@@ -101,28 +83,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>amount of neighbouring channels used for normalization</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>multiplicative factor. Default: 0.0001</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>exponent. Default: 0.75</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>additive factor. Default: 1</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -131,14 +115,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_log_softmax.html b/reference/nnf_log_softmax.html
index afec95182a..f6f99b8574 100644
--- a/reference/nnf_log_softmax.html
+++ b/reference/nnf_log_softmax.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Log_softmax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Log_softmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_log_softmax.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) input</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) A dimension along which log_softmax will be computed.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.
 If specified, the input tensor is casted to <code>dtype</code> before the operation
 is performed. This is useful for preventing data type overflows.
@@ -117,7 +101,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 uses an alternative formulation to compute the output and gradient correctly.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -126,14 +110,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
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diff --git a/reference/nnf_logsigmoid.html b/reference/nnf_logsigmoid.html
index 2337a667a3..791ccfa9c8 100644
--- a/reference/nnf_logsigmoid.html
+++ b/reference/nnf_logsigmoid.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Logsigmoid</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Logsigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_logsigmoid.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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diff --git a/reference/nnf_lp_pool1d.html b/reference/nnf_lp_pool1d.html
index 6825495106..cf1fcc5aa1 100644
--- a/reference/nnf_lp_pool1d.html
+++ b/reference/nnf_lp_pool1d.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a 1D power-average pooling over an input signal composed of
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Lp_pool1d — nnf_lp_pool1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Lp_pool1d — nnf_lp_pool1d"><meta name="description" content="Applies a 1D power-average pooling over an input signal composed of
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 several input planes. If the sum of all inputs to the power of p is
-zero, the gradient is set to zero as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Lp_pool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Lp_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_lp_pool1d.Rd</code></div>
     </div>
 
@@ -101,29 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>if inf than one gets max pooling if 0 you get sum pooling (
 proportional to the avg pooling)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>a single int, the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>a single int, the stride of the window. Default value is kernel_size</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use ceil instead of floor to compute the output shape</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -132,14 +116,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_lp_pool2d.html b/reference/nnf_lp_pool2d.html
index 563564cd89..a7ab5a9247 100644
--- a/reference/nnf_lp_pool2d.html
+++ b/reference/nnf_lp_pool2d.html
@@ -1,12 +1,9 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Lp_pool2d — nnf_lp_pool2d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Lp_pool2d — nnf_lp_pool2d"><meta name="description" content="Applies a 2D power-average pooling over an input signal composed of
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Lp_pool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Lp_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_lp_pool2d.Rd</code></div>
     </div>
 
@@ -101,29 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>norm_type</dt>
+<dt id="arg-norm-type">norm_type<a class="anchor" aria-label="anchor" href="#arg-norm-type"></a></dt>
 <dd><p>if inf than one gets max pooling if 0 you get sum pooling (
 proportional to the avg pooling)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>a single int, the size of the window</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>a single int, the stride of the window. Default value is kernel_size</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use ceil instead of floor to compute the output shape</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_margin_ranking_loss.html b/reference/nnf_margin_ranking_loss.html
index b75f1f46cd..a43ad129e0 100644
--- a/reference/nnf_margin_ranking_loss.html
+++ b/reference/nnf_margin_ranking_loss.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
-mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)."><title>Margin_ranking_loss — nnf_margin_ranking_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Margin_ranking_loss — nnf_margin_ranking_loss"><meta property="og:description" content="Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
-mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Margin_ranking_loss — nnf_margin_ranking_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Margin_ranking_loss — nnf_margin_ranking_loss"><meta name="description" content="Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
+mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)."><meta property="og:description" content="Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
+mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1)."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -12,77 +9,62 @@
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 </script></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Margin_ranking_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Margin_ranking_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_margin_ranking_loss.Rd</code></div>
     </div>
 
@@ -98,23 +80,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input1</dt>
+
+
+<dl><dt id="arg-input-">input1<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>the first tensor</p></dd>
 
 
-<dt>input2</dt>
+<dt id="arg-input-">input2<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>the second input tensor</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>the target tensor</p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>Has a default value of 00 .</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -122,7 +106,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -131,14 +115,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_max_pool1d.html b/reference/nnf_max_pool1d.html
index 21734c6d11..ab179463bc 100644
--- a/reference/nnf_max_pool1d.html
+++ b/reference/nnf_max_pool1d.html
@@ -1,10 +1,7 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Max_pool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Max_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_max_pool1d.Rd</code></div>
     </div>
 
@@ -106,41 +88,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape (minibatch , in_channels , iW)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window. Can be a single number or a
 tuple <code>(kW,)</code>.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Can be a single number or a tuple
 <code>(sW,)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a
 single number or a tuple <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>controls the spacing between the kernel points; also known as
 the à trous algorithm.</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
 output shape. Default: <code>FALSE</code></p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>whether to return the indices where the max occurs.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_max_pool2d.html b/reference/nnf_max_pool2d.html
index ad44465129..9ae1a6cce4 100644
--- a/reference/nnf_max_pool2d.html
+++ b/reference/nnf_max_pool2d.html
@@ -1,10 +1,7 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Max_pool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Max_pool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_max_pool2d.Rd</code></div>
     </div>
 
@@ -106,41 +88,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iH , iW)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>size of the pooling region. Can be a single number or a
 tuple <code>(kH, kW)</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>stride of the pooling operation. Can be a single number or a
 tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a
 single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>controls the spacing between the kernel points; also known as
 the à trous algorithm.</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
 to compute the output shape. Default: <code>FALSE</code></p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>whether to return the indices where the max occurs.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_max_pool3d.html b/reference/nnf_max_pool3d.html
index 1a42315dcc..b8cf37af7f 100644
--- a/reference/nnf_max_pool3d.html
+++ b/reference/nnf_max_pool3d.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Max_pool3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Max_pool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_max_pool3d.Rd</code></div>
     </div>
 
@@ -106,41 +88,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>size of the pooling region. Can be a single number or a
 tuple <code>(kT, kH, kW)</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>stride of the pooling operation. Can be a single number or a
 tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a
 single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>controls the spacing between the kernel points; also known as
 the à trous algorithm.</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
 to compute the output shape</p></dd>
 
 
-<dt>return_indices</dt>
+<dt id="arg-return-indices">return_indices<a class="anchor" aria-label="anchor" href="#arg-return-indices"></a></dt>
 <dd><p>whether to return the indices where the max occurs.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_max_unpool1d.html b/reference/nnf_max_unpool1d.html
index 4227f788cf..23b2b814b1 100644
--- a/reference/nnf_max_unpool1d.html
+++ b/reference/nnf_max_unpool1d.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Max_unpool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Max_unpool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_max_unpool1d.Rd</code></div>
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@@ -102,32 +84,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input Tensor to invert</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>the indices given out by max pool</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>Size of the max pooling window.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>Stride of the max pooling window. It is set to kernel_size by default.</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>Padding that was added to the input</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the targeted output size</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -136,14 +120,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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-      <img src="" class="logo" alt=""><h1>Max_unpool2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Max_unpool2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_max_unpool2d.Rd</code></div>
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@@ -102,32 +84,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input Tensor to invert</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>the indices given out by max pool</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>Size of the max pooling window.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>Stride of the max pooling window. It is set to kernel_size by default.</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>Padding that was added to the input</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the targeted output size</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_max_unpool3d.html b/reference/nnf_max_unpool3d.html
index 058d41c67d..8b92b89fc0 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Max_unpool3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
+
+      <h1>Max_unpool3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pooling.R" class="external-link"><code>R/nnf-pooling.R</code></a></small>
       <div class="d-none name"><code>nnf_max_unpool3d.Rd</code></div>
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@@ -102,32 +84,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input Tensor to invert</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>the indices given out by max pool</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>Size of the max pooling window.</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>Stride of the max pooling window. It is set to kernel_size by default.</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>Padding that was added to the input</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the targeted output size</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/nnf_mse_loss.html b/reference/nnf_mse_loss.html
index b0b65a3ee1..ae2d899b77 100644
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mse_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Mse_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_mse_loss.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -111,7 +95,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -120,14 +104,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_multi_head_attention_forward.html b/reference/nnf_multi_head_attention_forward.html
index 6b07b2b693..92c8a2fe90 100644
--- a/reference/nnf_multi_head_attention_forward.html
+++ b/reference/nnf_multi_head_attention_forward.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-subspaces. See reference: Attention Is All You Need"><title>Multi head attention forward — nnf_multi_head_attention_forward • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multi head attention forward — nnf_multi_head_attention_forward"><meta property="og:description" content="Allows the model to jointly attend to information from different representation
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Multi head attention forward — nnf_multi_head_attention_forward • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multi head attention forward — nnf_multi_head_attention_forward"><meta name="description" content="Allows the model to jointly attend to information from different representation
+subspaces. See reference: Attention Is All You Need"><meta property="og:description" content="Allows the model to jointly attend to information from different representation
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Multi head attention forward</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Multi head attention forward</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_multi_head_attention_forward.Rd</code></div>
     </div>
 
@@ -124,78 +106,80 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>query</dt>
+
+
+<dl><dt id="arg-query">query<a class="anchor" aria-label="anchor" href="#arg-query"></a></dt>
 <dd><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
 the embedding dimension. If batch_first is TRUE, the first two dimensions are transposed.</p></dd>
 
 
-<dt>key</dt>
+<dt id="arg-key">key<a class="anchor" aria-label="anchor" href="#arg-key"></a></dt>
 <dd><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
 the embedding dimension. If batch_first is TRUE, the first two dimensions are transposed.</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
 the embedding dimension. If batch_first is TRUE, the first two dimensions are transposed.</p></dd>
 
 
-<dt>embed_dim_to_check</dt>
+<dt id="arg-embed-dim-to-check">embed_dim_to_check<a class="anchor" aria-label="anchor" href="#arg-embed-dim-to-check"></a></dt>
 <dd><p>total dimension of the model.</p></dd>
 
 
-<dt>num_heads</dt>
+<dt id="arg-num-heads">num_heads<a class="anchor" aria-label="anchor" href="#arg-num-heads"></a></dt>
 <dd><p>parallel attention heads.</p></dd>
 
 
-<dt>in_proj_weight</dt>
+<dt id="arg-in-proj-weight">in_proj_weight<a class="anchor" aria-label="anchor" href="#arg-in-proj-weight"></a></dt>
 <dd><p>input projection weight.</p></dd>
 
 
-<dt>in_proj_bias</dt>
+<dt id="arg-in-proj-bias">in_proj_bias<a class="anchor" aria-label="anchor" href="#arg-in-proj-bias"></a></dt>
 <dd><p>input projection bias.</p></dd>
 
 
-<dt>bias_k</dt>
+<dt id="arg-bias-k">bias_k<a class="anchor" aria-label="anchor" href="#arg-bias-k"></a></dt>
 <dd><p>bias of the key and value sequences to be added at dim=0.</p></dd>
 
 
-<dt>bias_v</dt>
+<dt id="arg-bias-v">bias_v<a class="anchor" aria-label="anchor" href="#arg-bias-v"></a></dt>
 <dd><p>currently undocumented.</p></dd>
 
 
-<dt>add_zero_attn</dt>
+<dt id="arg-add-zero-attn">add_zero_attn<a class="anchor" aria-label="anchor" href="#arg-add-zero-attn"></a></dt>
 <dd><p>add a new batch of zeros to the key and
 value sequences at dim=1.</p></dd>
 
 
-<dt>dropout_p</dt>
+<dt id="arg-dropout-p">dropout_p<a class="anchor" aria-label="anchor" href="#arg-dropout-p"></a></dt>
 <dd><p>probability of an element to be zeroed.</p></dd>
 
 
-<dt>out_proj_weight</dt>
+<dt id="arg-out-proj-weight">out_proj_weight<a class="anchor" aria-label="anchor" href="#arg-out-proj-weight"></a></dt>
 <dd><p>the output projection weight.</p></dd>
 
 
-<dt>out_proj_bias</dt>
+<dt id="arg-out-proj-bias">out_proj_bias<a class="anchor" aria-label="anchor" href="#arg-out-proj-bias"></a></dt>
 <dd><p>output projection bias.</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>apply dropout if is <code>TRUE</code>.</p></dd>
 
 
-<dt>key_padding_mask</dt>
+<dt id="arg-key-padding-mask">key_padding_mask<a class="anchor" aria-label="anchor" href="#arg-key-padding-mask"></a></dt>
 <dd><p>\((N, S)\) where N is the batch size, S is the source sequence length.
 If a ByteTensor is provided, the non-zero positions will be ignored while the position
 with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
 value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></dd>
 
 
-<dt>need_weights</dt>
+<dt id="arg-need-weights">need_weights<a class="anchor" aria-label="anchor" href="#arg-need-weights"></a></dt>
 <dd><p>output attn_output_weights.</p></dd>
 
 
-<dt>attn_mask</dt>
+<dt id="arg-attn-mask">attn_mask<a class="anchor" aria-label="anchor" href="#arg-attn-mask"></a></dt>
 <dd><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
 3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
 S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
@@ -205,45 +189,45 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 is provided, it will be added to the attention weight.</p></dd>
 
 
-<dt>avg_weights</dt>
+<dt id="arg-avg-weights">avg_weights<a class="anchor" aria-label="anchor" href="#arg-avg-weights"></a></dt>
 <dd><p>Logical; whether to average attn_output_weights over the
 attention heads before outputting them. This doesn't change the returned
 value of attn_output; it only affects the returned attention weight matrix.</p></dd>
 
 
-<dt>use_separate_proj_weight</dt>
+<dt id="arg-use-separate-proj-weight">use_separate_proj_weight<a class="anchor" aria-label="anchor" href="#arg-use-separate-proj-weight"></a></dt>
 <dd><p>the function accept the proj. weights for
 query, key, and value in different forms. If false, in_proj_weight will be used,
 which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></dd>
 
 
-<dt>q_proj_weight</dt>
+<dt id="arg-q-proj-weight">q_proj_weight<a class="anchor" aria-label="anchor" href="#arg-q-proj-weight"></a></dt>
 <dd><p>input projection weight and bias.</p></dd>
 
 
-<dt>k_proj_weight</dt>
+<dt id="arg-k-proj-weight">k_proj_weight<a class="anchor" aria-label="anchor" href="#arg-k-proj-weight"></a></dt>
 <dd><p>currently undocumented.</p></dd>
 
 
-<dt>v_proj_weight</dt>
+<dt id="arg-v-proj-weight">v_proj_weight<a class="anchor" aria-label="anchor" href="#arg-v-proj-weight"></a></dt>
 <dd><p>currently undocumented.</p></dd>
 
 
-<dt>static_k</dt>
+<dt id="arg-static-k">static_k<a class="anchor" aria-label="anchor" href="#arg-static-k"></a></dt>
 <dd><p>static key and value used for attention operators.</p></dd>
 
 
-<dt>static_v</dt>
+<dt id="arg-static-v">static_v<a class="anchor" aria-label="anchor" href="#arg-static-v"></a></dt>
 <dd><p>currently undocumented.</p></dd>
 
 
-<dt>batch_first</dt>
+<dt id="arg-batch-first">batch_first<a class="anchor" aria-label="anchor" href="#arg-batch-first"></a></dt>
 <dd><p>Logical; whether to expect query, key, and value to have
 batch as their first parameter, and to return output with batch first.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -252,14 +236,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
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diff --git a/reference/nnf_multi_margin_loss.html b/reference/nnf_multi_margin_loss.html
index c6504a6eb2..ba9cb54cc6 100644
--- a/reference/nnf_multi_margin_loss.html
+++ b/reference/nnf_multi_margin_loss.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that optimizes a multi-class classification hinge loss
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Multi_margin_loss — nnf_multi_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multi_margin_loss — nnf_multi_margin_loss"><meta name="description" content="Creates a criterion that optimizes a multi-class classification hinge loss
 (margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, 0 &amp;lt;= y &amp;lt;= x$size(2) - 1 )."><title>Multi_margin_loss — nnf_multi_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Multi_margin_loss — nnf_multi_margin_loss"><meta property="og:description" content="Creates a criterion that optimizes a multi-class classification hinge loss
+(which is a 1D tensor of target class indices, 0 &amp;lt;= y &amp;lt;= x$size(2) - 1 )."><meta property="og:description" content="Creates a criterion that optimizes a multi-class classification hinge loss
 (margin-based loss) between input x (a 2D mini-batch Tensor) and output y
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Multi_margin_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Multi_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_multi_margin_loss.Rd</code></div>
     </div>
 
@@ -108,28 +90,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>Has a default value of 1. 1 and 2 are the only supported values.</p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>Has a default value of 1.</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>a manual rescaling weight given to each class. If given, it has to
 be a Tensor of size C. Otherwise, it is treated as if having all ones.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -137,7 +121,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/nnf_multilabel_margin_loss.html b/reference/nnf_multilabel_margin_loss.html
index a28af05c0d..b90db53130 100644
--- a/reference/nnf_multilabel_margin_loss.html
+++ b/reference/nnf_multilabel_margin_loss.html
@@ -1,12 +1,9 @@
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+is a 2D Tensor of target class indices)."><meta property="og:description" content="Creates a criterion that optimizes a multi-class multi-classification hinge loss
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-is a 2D Tensor of target class indices)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Multilabel_margin_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Multilabel_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_multilabel_margin_loss.Rd</code></div>
     </div>
 
@@ -101,15 +83,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -117,7 +101,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
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@@ -126,14 +110,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
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diff --git a/reference/nnf_multilabel_soft_margin_loss.html b/reference/nnf_multilabel_soft_margin_loss.html
index 07f1ac7c45..e82a87c13e 100644
--- a/reference/nnf_multilabel_soft_margin_loss.html
+++ b/reference/nnf_multilabel_soft_margin_loss.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Multilabel_soft_margin_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Multilabel_soft_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_multilabel_soft_margin_loss.Rd</code></div>
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@@ -103,19 +85,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>weight tensor to apply on the loss.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -127,7 +111,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>It takes a one hot encoded target vector as input.</p>
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diff --git a/reference/nnf_nll_loss.html b/reference/nnf_nll_loss.html
index a544d7bc0d..06cc240d20 100644
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+++ b/reference/nnf_nll_loss.html
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-      <img src="" class="logo" alt=""><h1>Nll_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Nll_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_nll_loss.Rd</code></div>
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@@ -101,28 +83,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
 case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
 the case of K-dimensional loss.</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
 or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor, optional) a manual rescaling weight given to each class.
 If given, has to be a Tensor of size <code>C</code></p></dd>
 
 
-<dt>ignore_index</dt>
+<dt id="arg-ignore-index">ignore_index<a class="anchor" aria-label="anchor" href="#arg-ignore-index"></a></dt>
 <dd><p>(int, optional) Specifies a target value that is ignored and
 does not contribute to the input gradient.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -130,7 +114,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -139,14 +123,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_normalize.html b/reference/nnf_normalize.html
index 62732dec42..86425dd197 100644
--- a/reference/nnf_normalize.html
+++ b/reference/nnf_normalize.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Normalize</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
+
+      <h1>Normalize</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-normalization.R" class="external-link"><code>R/nnf-normalization.R</code></a></small>
       <div class="d-none name"><code>nnf_normalize.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of any shape</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(float) the exponent value in the norm formulation. Default: 2</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce. Default: 1</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float) small value to avoid division by zero. Default: 1e-12</p></dd>
 
 
-<dt>out</dt>
+<dt id="arg-out">out<a class="anchor" aria-label="anchor" href="#arg-out"></a></dt>
 <dd><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></dd>
 
 </dl></div>
@@ -126,7 +110,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 dimension \(1\) for normalization.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
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diff --git a/reference/nnf_one_hot.html b/reference/nnf_one_hot.html
index cb62c3d57d..26eeffd66c 100644
--- a/reference/nnf_one_hot.html
+++ b/reference/nnf_one_hot.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Takes LongTensor with index values of shape (*) and returns a tensor
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>One_hot — nnf_one_hot • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="One_hot — nnf_one_hot"><meta name="description" content="Takes LongTensor with index values of shape (*) and returns a tensor
 of shape (*, num_classes) that have zeros everywhere except where the
 index of last dimension matches the corresponding value of the input tensor,
-in which case it will be 1."><title>One_hot — nnf_one_hot • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="One_hot — nnf_one_hot"><meta property="og:description" content="Takes LongTensor with index values of shape (*) and returns a tensor
+in which case it will be 1."><meta property="og:description" content="Takes LongTensor with index values of shape (*) and returns a tensor
 of shape (*, num_classes) that have zeros everywhere except where the
 index of last dimension matches the corresponding value of the input tensor,
-in which case it will be 1."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+in which case it will be 1."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>One_hot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
+
+      <h1>One_hot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-embedding.R" class="external-link"><code>R/nnf-embedding.R</code></a></small>
       <div class="d-none name"><code>nnf_one_hot.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>(LongTensor) class values of any shape.</p></dd>
 
 
-<dt>num_classes</dt>
+<dt id="arg-num-classes">num_classes<a class="anchor" aria-label="anchor" href="#arg-num-classes"></a></dt>
 <dd><p>(int) Total number of classes. If set to -1, the number
 of classes will be inferred as one greater than the largest class value in
 the input tensor.</p></dd>
@@ -119,7 +103,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     <p>One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -128,14 +112,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/nnf_pad.html b/reference/nnf_pad.html
index 9314dd3297..c7f33e015b 100644
--- a/reference/nnf_pad.html
+++ b/reference/nnf_pad.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Pads tensor."><title>Pad — nnf_pad • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pad — nnf_pad"><meta property="og:description" content="Pads tensor."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pad — nnf_pad • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pad — nnf_pad"><meta name="description" content="Pads tensor."><meta property="og:description" content="Pads tensor."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+      <h1>Pad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-padding.R" class="external-link"><code>R/nnf-padding.R</code></a></small>
       <div class="d-none name"><code>nnf_pad.Rd</code></div>
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@@ -95,26 +77,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) N-dimensional tensor</p></dd>
 
 
-<dt>pad</dt>
+<dt id="arg-pad">pad<a class="anchor" aria-label="anchor" href="#arg-pad"></a></dt>
 <dd><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
 and \(m\) is even.</p></dd>
 
 
-<dt>mode</dt>
+<dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>fill value for 'constant' padding. Default: 0.</p></dd>
 
 </dl></div>
     <div class="section level2">
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-    
+
 
 
 <p>The padding size by which to pad some dimensions of <code>input</code>
@@ -134,7 +118,7 @@ <h2 id="padding-size">Padding size<a class="anchor" aria-label="anchor" href="#p
     </div>
     <div class="section level2">
     <h2 id="padding-mode">Padding mode<a class="anchor" aria-label="anchor" href="#padding-mode"></a></h2>
-    
+
 
 
 <p>See <code>nn_constant_pad_2d</code>, <code>nn_reflection_pad_2d</code>, and
@@ -145,7 +129,7 @@ <h2 id="padding-mode">Padding mode<a class="anchor" aria-label="anchor" href="#p
 dimensions of 4D input tensor, or the last dimension of 3D input tensor.</p>
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diff --git a/reference/nnf_pairwise_distance.html b/reference/nnf_pairwise_distance.html
index 116b0e1b50..277266d480 100644
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+++ b/reference/nnf_pairwise_distance.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Pairwise_distance</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-distance.R" class="external-link"><code>R/nnf-distance.R</code></a></small>
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@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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-    <dl><dt>x1</dt>
+
+
+<dl><dt id="arg-x-">x1<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) First input.</p></dd>
 
 
-<dt>x2</dt>
+<dt id="arg-x-">x2<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) Second input (of size matching x1).</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>the norm degree. Default: 2</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional) Small value to avoid division by zero.
 Default: 1e-8</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>Determines whether or not to keep the vector dimension. Default: False</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -126,14 +110,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_pdist.html b/reference/nnf_pdist.html
index 105c2f1dda..a54cd5fcb9 100644
--- a/reference/nnf_pdist.html
+++ b/reference/nnf_pdist.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Computes the p-norm distance between every pair of row vectors in the input.
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pdist — nnf_pdist • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pdist — nnf_pdist"><meta name="description" content="Computes the p-norm distance between every pair of row vectors in the input.
 This is identical to the upper triangular portion, excluding the diagonal, of
 torch_norm(input[:, None] - input, dim=2, p=p). This function will be faster
-if the rows are contiguous."><title>Pdist — nnf_pdist • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pdist — nnf_pdist"><meta property="og:description" content="Computes the p-norm distance between every pair of row vectors in the input.
+if the rows are contiguous."><meta property="og:description" content="Computes the p-norm distance between every pair of row vectors in the input.
 This is identical to the upper triangular portion, excluding the diagonal, of
 torch_norm(input[:, None] - input, dim=2, p=p). This function will be faster
-if the rows are contiguous."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -16,77 +13,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Pdist</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-distance.R" class="external-link"><code>R/nnf-distance.R</code></a></small>
+
+      <h1>Pdist</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-distance.R" class="external-link"><code>R/nnf-distance.R</code></a></small>
       <div class="d-none name"><code>nnf_pdist.Rd</code></div>
     </div>
 
@@ -104,11 +86,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \(N \times M\).</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>p value for the p-norm distance to calculate between each vector pair
 \(\in [0, \infty]\).</p></dd>
 
@@ -119,7 +103,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 \(\frac{1}{2} N (N - 1)\).</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -128,14 +112,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_pixel_shuffle.html b/reference/nnf_pixel_shuffle.html
index 14a911e5ef..9023f8ad55 100644
--- a/reference/nnf_pixel_shuffle.html
+++ b/reference/nnf_pixel_shuffle.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
-tensor of shape \((*, C, H \times r, W \times r)\)."><title>Pixel_shuffle — nnf_pixel_shuffle • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pixel_shuffle — nnf_pixel_shuffle"><meta property="og:description" content="Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
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-      <img src="" class="logo" alt=""><h1>Pixel_shuffle</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-pixelshuffle.R" class="external-link"><code>R/nnf-pixelshuffle.R</code></a></small>
+
+      <h1>Pixel_shuffle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-pixelshuffle.R" class="external-link"><code>R/nnf-pixelshuffle.R</code></a></small>
       <div class="d-none name"><code>nnf_pixel_shuffle.Rd</code></div>
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@@ -98,16 +80,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
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+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>upscale_factor</dt>
+<dt id="arg-upscale-factor">upscale_factor<a class="anchor" aria-label="anchor" href="#arg-upscale-factor"></a></dt>
 <dd><p>(int) factor to increase spatial resolution by</p></dd>
 
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index 39b6a811d3..fc79cacfbb 100644
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-      <img src="" class="logo" alt=""><h1>Poisson_nll_loss</h1>
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+
+      <h1>Poisson_nll_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
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@@ -102,31 +84,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>log_input</dt>
+<dt id="arg-log-input">log_input<a class="anchor" aria-label="anchor" href="#arg-log-input"></a></dt>
 <dd><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
 if <code>FALSE</code> then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
 Default: <code>TRUE</code>.</p></dd>
 
 
-<dt>full</dt>
+<dt id="arg-full">full<a class="anchor" aria-label="anchor" href="#arg-full"></a></dt>
 <dd><p>whether to compute full loss, i. e. to add the Stirling approximation
 term. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
 <code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -134,7 +118,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/nnf_prelu.html b/reference/nnf_prelu.html
index c94cb7f41f..d0b2a55bcc 100644
--- a/reference/nnf_prelu.html
+++ b/reference/nnf_prelu.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies element-wise the function
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 \(PReLU(x) = max(0,x) + weight * min(0,x)\)
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+where weight is a learnable parameter."><meta property="og:description" content="Applies element-wise the function
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-where weight is a learnable parameter."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Prelu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Prelu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_prelu.Rd</code></div>
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@@ -101,17 +83,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(Tensor) the learnable weights</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -120,14 +104,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_relu.html b/reference/nnf_relu.html
index 82aaa5f54b..dfaab34fa0 100644
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diff --git a/reference/nnf_relu6.html b/reference/nnf_relu6.html
index 448b586a08..3e8f726b74 100644
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+
+      <h1>Relu6</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
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-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
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diff --git a/reference/nnf_relu_.html b/reference/nnf_relu_.html
new file mode 100644
index 0000000000..6b4f236c99
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index e6307256f3..82f654912c 100644
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@@ -97,29 +79,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>lower</dt>
+<dt id="arg-lower">lower<a class="anchor" aria-label="anchor" href="#arg-lower"></a></dt>
 <dd><p>lower bound of the uniform distribution. Default: 1/8</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>upper bound of the uniform distribution. Default: 1/3</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>bool wether it's a training pass. DEfault: FALSE</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -128,14 +112,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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diff --git a/reference/nnf_rrelu_.html b/reference/nnf_rrelu_.html
new file mode 100644
index 0000000000..9794a3df37
--- /dev/null
+++ b/reference/nnf_rrelu_.html
@@ -0,0 +1,8 @@
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diff --git a/reference/nnf_selu.html b/reference/nnf_selu.html
index ffa65c65e4..422178e7f2 100644
--- a/reference/nnf_selu.html
+++ b/reference/nnf_selu.html
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 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies element-wise,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Selu — nnf_selu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Selu — nnf_selu"><meta name="description" content="Applies element-wise,
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+\(scale=1.0507009873554804934193349852946\)."><meta property="og:description" content="Applies element-wise,
 $$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -106,12 +88,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/nnf_selu_.html b/reference/nnf_selu_.html
new file mode 100644
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@@ -0,0 +1,8 @@
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diff --git a/reference/nnf_sigmoid.html b/reference/nnf_sigmoid.html
index 4fc123f051..e11cdeda98 100644
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-      <img src="" class="logo" alt=""><h1>Sigmoid</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Sigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_sigmoid.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
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diff --git a/reference/nnf_silu.html b/reference/nnf_silu.html
index 949410c68c..736ecfe054 100644
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@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See <code><a href="nn_silu.html">nn_silu()</a></code> for more information.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See <code><a href="nn_silu.html">nn_silu()</a></code> for more information.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_silu.Rd</code></div>
     </div>
 
@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
@@ -112,7 +96,7 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
     <div class="dont-index"><p><code><a href="nn_silu.html">nn_silu()</a></code>.</p></div>
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 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nnf_smooth_l1_loss.html b/reference/nnf_smooth_l1_loss.html
index 370c055603..1e707e0501 100644
--- a/reference/nnf_smooth_l1_loss.html
+++ b/reference/nnf_smooth_l1_loss.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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+
+      <h1>Smooth_l1_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_smooth_l1_loss.Rd</code></div>
     </div>
 
@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -114,7 +98,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_soft_margin_loss.html b/reference/nnf_soft_margin_loss.html
index eab91382a7..7504c4bea3 100644
--- a/reference/nnf_soft_margin_loss.html
+++ b/reference/nnf_soft_margin_loss.html
@@ -1,10 +1,7 @@
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+between input tensor x and target tensor y (containing 1 or -1)."><meta property="og:description" content="Creates a criterion that optimizes a two-class classification logistic loss
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
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@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>tensor (N,*) where ** means, any number of additional dimensions</p></dd>
 
 
-<dt>target</dt>
+<dt id="arg-target">target<a class="anchor" aria-label="anchor" href="#arg-target"></a></dt>
 <dd><p>tensor (N,*) , same shape as the input</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -114,7 +98,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_softmax.html b/reference/nnf_softmax.html
index c201cf6b2d..ecf92393b0 100644
--- a/reference/nnf_softmax.html
+++ b/reference/nnf_softmax.html
@@ -1,8 +1,5 @@
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Softmax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Softmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_softmax.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) input</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) A dimension along which softmax will be computed.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed. This is useful for preventing data type overflows.
 Default: NULL.</p></dd>
 
@@ -116,7 +100,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 lie in the range <code>[0, 1]</code> and sum to 1.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -125,14 +109,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_softmin.html b/reference/nnf_softmin.html
index 4b16df1888..8f77170f4f 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Softmin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Softmin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_softmin.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) input</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) A dimension along which softmin will be computed
 (so every slice        along dim will sum to 1).</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
 This is useful for preventing data type overflows. Default: NULL.</p></dd>
 
@@ -116,7 +100,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <p>See <a href="nnf_softmax.html">nnf_softmax</a> definition for mathematical formula.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -125,14 +109,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
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 </div>
 
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-  
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diff --git a/reference/nnf_softplus.html b/reference/nnf_softplus.html
index ca3f373439..3d9c190d3b 100644
--- a/reference/nnf_softplus.html
+++ b/reference/nnf_softplus.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\)."><title>Softplus — nnf_softplus • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softplus — nnf_softplus"><meta property="og:description" content="Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Softplus</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Softplus</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_softplus.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>the beta value for the Softplus formulation. Default: 1</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>values above this revert to a linear function. Default: 20</p></dd>
 
 </dl></div>
@@ -114,7 +98,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 when \(input * \beta &gt; threshold\).</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -123,14 +107,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/nnf_softshrink.html b/reference/nnf_softshrink.html
index 93c386b01e..fd53a6a9a5 100644
--- a/reference/nnf_softshrink.html
+++ b/reference/nnf_softshrink.html
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-      <img src="" class="logo" alt=""><h1>Softshrink</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Softshrink</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -95,18 +77,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>lambd</dt>
+<dt id="arg-lambd">lambd<a class="anchor" aria-label="anchor" href="#arg-lambd"></a></dt>
 <dd><p>the lambda (must be no less than zero) value for the Softshrink
 formulation. Default: 0.5</p></dd>
 
 </dl></div>
 
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diff --git a/reference/nnf_softsign.html b/reference/nnf_softsign.html
index 9cd71f32b9..1fbc2c3d90 100644
--- a/reference/nnf_softsign.html
+++ b/reference/nnf_softsign.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies element-wise, the function \(SoftSign(x) = x/(1 + |x|\)"><title>Softsign — nnf_softsign • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Softsign — nnf_softsign"><meta property="og:description" content="Applies element-wise, the function \(SoftSign(x) = x/(1 + |x|\)"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
+      <h1>Softsign</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
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+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_tanhshrink.html b/reference/nnf_tanhshrink.html
index b89d5b01e6..7bbc17e5eb 100644
--- a/reference/nnf_tanhshrink.html
+++ b/reference/nnf_tanhshrink.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Tanhshrink</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
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+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
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diff --git a/reference/nnf_threshold.html b/reference/nnf_threshold.html
index 5341caecce..38b6c9cc73 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Threshold</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
+
+      <h1>Threshold</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-activation.R" class="external-link"><code>R/nnf-activation.R</code></a></small>
       <div class="d-none name"><code>nnf_threshold.Rd</code></div>
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@@ -97,25 +79,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(N,*) tensor, where * means, any number of additional
 dimensions</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>The value to threshold at</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>The value to replace with</p></dd>
 
 
-<dt>inplace</dt>
+<dt id="arg-inplace">inplace<a class="anchor" aria-label="anchor" href="#arg-inplace"></a></dt>
 <dd><p>can optionally do the operation in-place. Default: FALSE</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/nnf_threshold_.html b/reference/nnf_threshold_.html
new file mode 100644
index 0000000000..2645eb0f55
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+++ b/reference/nnf_threshold_.html
@@ -0,0 +1,8 @@
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diff --git a/reference/nnf_triplet_margin_loss.html b/reference/nnf_triplet_margin_loss.html
index 347602b644..500f18e5a4 100644
--- a/reference/nnf_triplet_margin_loss.html
+++ b/reference/nnf_triplet_margin_loss.html
@@ -1,16 +1,13 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Creates a criterion that measures the triplet loss given an input tensors x1 ,
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Triplet_margin_loss — nnf_triplet_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet_margin_loss — nnf_triplet_margin_loss"><meta name="description" content="Creates a criterion that measures the triplet loss given an input tensors x1 ,
 x2 , x3 and a margin with a value greater than 0 . This is used for measuring
 a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
 anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D)."><title>Triplet_margin_loss — nnf_triplet_margin_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet_margin_loss — nnf_triplet_margin_loss"><meta property="og:description" content="Creates a criterion that measures the triplet loss given an input tensors x1 ,
+input tensors should be (N, D)."><meta property="og:description" content="Creates a criterion that measures the triplet loss given an input tensors x1 ,
 x2 , x3 and a margin with a value greater than 0 . This is used for measuring
 a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
 anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Triplet_margin_loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Triplet_margin_loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
       <div class="d-none name"><code>nnf_triplet_margin_loss.Rd</code></div>
     </div>
 
@@ -116,37 +98,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>anchor</dt>
+
+
+<dl><dt id="arg-anchor">anchor<a class="anchor" aria-label="anchor" href="#arg-anchor"></a></dt>
 <dd><p>the anchor input tensor</p></dd>
 
 
-<dt>positive</dt>
+<dt id="arg-positive">positive<a class="anchor" aria-label="anchor" href="#arg-positive"></a></dt>
 <dd><p>the positive input tensor</p></dd>
 
 
-<dt>negative</dt>
+<dt id="arg-negative">negative<a class="anchor" aria-label="anchor" href="#arg-negative"></a></dt>
 <dd><p>the negative input tensor</p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>Default: 1.</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>The norm degree for pairwise distance. Default: 2.</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional) Small value to avoid division by zero.</p></dd>
 
 
-<dt>swap</dt>
+<dt id="arg-swap">swap<a class="anchor" aria-label="anchor" href="#arg-swap"></a></dt>
 <dd><p>The distance swap is described in detail in the paper Learning shallow
 convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
 Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -154,7 +138,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/nnf_triplet_margin_with_distance_loss.html b/reference/nnf_triplet_margin_with_distance_loss.html
index 113caa5125..c39e39880c 100644
--- a/reference/nnf_triplet_margin_with_distance_loss.html
+++ b/reference/nnf_triplet_margin_with_distance_loss.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss"><meta name="description" content="See nn_triplet_margin_with_distance_loss()"><meta property="og:description" content="See nn_triplet_margin_with_distance_loss()"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Triplet margin with distance loss</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
+
+      <h1>Triplet margin with distance loss</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-loss.R" class="external-link"><code>R/nnf-loss.R</code></a></small>
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@@ -103,35 +85,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>anchor</dt>
+
+
+<dl><dt id="arg-anchor">anchor<a class="anchor" aria-label="anchor" href="#arg-anchor"></a></dt>
 <dd><p>the anchor input tensor</p></dd>
 
 
-<dt>positive</dt>
+<dt id="arg-positive">positive<a class="anchor" aria-label="anchor" href="#arg-positive"></a></dt>
 <dd><p>the positive input tensor</p></dd>
 
 
-<dt>negative</dt>
+<dt id="arg-negative">negative<a class="anchor" aria-label="anchor" href="#arg-negative"></a></dt>
 <dd><p>the negative input tensor</p></dd>
 
 
-<dt>distance_function</dt>
+<dt id="arg-distance-function">distance_function<a class="anchor" aria-label="anchor" href="#arg-distance-function"></a></dt>
 <dd><p>(callable, optional): A nonnegative, real-valued function that
 quantifies the closeness of two tensors. If not specified,
 <code><a href="nn_pairwise_distance.html">nn_pairwise_distance()</a></code> will be used.  Default: <code>None</code></p></dd>
 
 
-<dt>margin</dt>
+<dt id="arg-margin">margin<a class="anchor" aria-label="anchor" href="#arg-margin"></a></dt>
 <dd><p>Default: 1.</p></dd>
 
 
-<dt>swap</dt>
+<dt id="arg-swap">swap<a class="anchor" aria-label="anchor" href="#arg-swap"></a></dt>
 <dd><p>The distance swap is described in detail in the paper Learning shallow
 convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
 Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>reduction</dt>
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
 <dd><p>(string, optional) – Specifies the reduction to apply to the
 output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
 the sum of the output will be divided by the number of elements in the output,
@@ -139,7 +123,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
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index 292b6a942f..3f32e591ae 100644
--- a/reference/nnf_unfold.html
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Unfold</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/nnf-fold.R" class="external-link"><code>R/nnf-fold.R</code></a></small>
+
+      <h1>Unfold</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/nnf-fold.R" class="external-link"><code>R/nnf-fold.R</code></a></small>
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@@ -95,32 +77,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the sliding blocks</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>a parameter that controls the stride of elements within the
 neighborhood. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero padding to be added on both sides of input.
 Default: 0</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the sliding blocks in the input spatial dimensions.
 Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 
 <p>More than one element of the unfolded tensor may refer to a single
@@ -129,7 +113,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 to the tensor, please clone it first.</p>
     </div>
 
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diff --git a/reference/optim_adadelta.html b/reference/optim_adadelta.html
index da53aee749..931eedb977 100644
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+++ b/reference/optim_adadelta.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Adadelta optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-adadelta.R" class="external-link"><code>R/optim-adadelta.R</code></a></small>
+
+      <h1>Adadelta optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-adadelta.R" class="external-link"><code>R/optim-adadelta.R</code></a></small>
       <div class="d-none name"><code>optim_adadelta.Rd</code></div>
     </div>
 
@@ -95,26 +77,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): list of parameters to optimize or list defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
 
 
-<dt>rho</dt>
+<dt id="arg-rho">rho<a class="anchor" aria-label="anchor" href="#arg-rho"></a></dt>
 <dd><p>(float, optional): coefficient used for computing a running average
 of squared gradients (default: 0.9)</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): term added to the denominator to improve
 numerical stability (default: 1e-6)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
 
 </dl></div>
@@ -150,16 +134,16 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_adadelta</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="va">parameters</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -168,14 +152,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/optim_adagrad.html b/reference/optim_adagrad.html
index 6bade157b8..88472c9aa4 100644
--- a/reference/optim_adagrad.html
+++ b/reference/optim_adagrad.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Adagrad optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-adagrad.R" class="external-link"><code>R/optim-adagrad.R</code></a></small>
+
+      <h1>Adagrad optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-adagrad.R" class="external-link"><code>R/optim-adagrad.R</code></a></small>
       <div class="d-none name"><code>optim_adagrad.Rd</code></div>
     </div>
 
@@ -102,23 +84,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): list of parameters to optimize or list parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
 
 
-<dt>lr_decay</dt>
+<dt id="arg-lr-decay">lr_decay<a class="anchor" aria-label="anchor" href="#arg-lr-decay"></a></dt>
 <dd><p>(float, optional): learning rate decay (default: 0)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
 
 
-<dt>initial_accumulator_value</dt>
+<dt id="arg-initial-accumulator-value">initial_accumulator_value<a class="anchor" aria-label="anchor" href="#arg-initial-accumulator-value"></a></dt>
 <dd><p>the initial value for the accumulator. (default: 0)</p>
 <p>Adagrad is an especially good optimizer for sparse data.
 It individually modifies learning rate for every single parameter,
@@ -128,7 +112,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 getting small too fast, so that at some point a model cannot learn anymore.</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): term added to the denominator to improve
 numerical stability (default: 1e-10)</p></dd>
 
@@ -153,7 +137,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 creation and usage.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -162,14 +146,14 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/optim_adam.html b/reference/optim_adam.html
index ecebf33af3..593c892a6c 100644
--- a/reference/optim_adam.html
+++ b/reference/optim_adam.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Implements Adam algorithm.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-adam.R" class="external-link"><code>R/optim-adam.R</code></a></small>
+
+      <h1>Implements Adam algorithm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-adam.R" class="external-link"><code>R/optim-adam.R</code></a></small>
       <div class="d-none name"><code>optim_adam.Rd</code></div>
     </div>
 
@@ -102,30 +84,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or dicts defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
 
 
-<dt>betas</dt>
+<dt id="arg-betas">betas<a class="anchor" aria-label="anchor" href="#arg-betas"></a></dt>
 <dd><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
 running averages of gradient and its square (default: (0.9, 0.999))</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): term added to the denominator to improve
 numerical stability (default: 1e-8)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
 
 
-<dt>amsgrad</dt>
+<dt id="arg-amsgrad">amsgrad<a class="anchor" aria-label="anchor" href="#arg-amsgrad"></a></dt>
 <dd><p>(boolean, optional): whether to use the AMSGrad variant of this
 algorithm from the paper <a href="https://openreview.net/forum?id=ryQu7f-RZ" class="external-link">On the Convergence of Adam and Beyond</a>
 (default: FALSE)</p></dd>
@@ -144,17 +128,17 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_adam</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/optim_adamw.html b/reference/optim_adamw.html
index cdeb3ca28f..be7f28f60a 100644
--- a/reference/optim_adamw.html
+++ b/reference/optim_adamw.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="For further details regarding the algorithm we refer to
-Decoupled Weight Decay Regularization"><title>Implements AdamW algorithm — optim_adamw • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Implements AdamW algorithm — optim_adamw"><meta property="og:description" content="For further details regarding the algorithm we refer to
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Implements AdamW algorithm</h1>
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+
+      <h1>Implements AdamW algorithm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-adamw.R" class="external-link"><code>R/optim-adamw.R</code></a></small>
       <div class="d-none name"><code>optim_adamw.Rd</code></div>
     </div>
 
@@ -105,37 +87,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or dicts defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
 
 
-<dt>betas</dt>
+<dt id="arg-betas">betas<a class="anchor" aria-label="anchor" href="#arg-betas"></a></dt>
 <dd><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
 running averages of gradient and its square (default: (0.9, 0.999))</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): term added to the denominator to improve
 numerical stability (default: 1e-8)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
 
 
-<dt>amsgrad</dt>
+<dt id="arg-amsgrad">amsgrad<a class="anchor" aria-label="anchor" href="#arg-amsgrad"></a></dt>
 <dd><p>(boolean, optional): whether to use the AMSGrad variant of this
 algorithm from the paper <a href="https://openreview.net/forum?id=ryQu7f-RZ" class="external-link">On the Convergence of Adam and Beyond</a>
 (default: FALSE)</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -144,14 +128,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/optim_asgd.html b/reference/optim_asgd.html
index e37dcba8b3..50147b212a 100644
--- a/reference/optim_asgd.html
+++ b/reference/optim_asgd.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Proposed in Acceleration of stochastic approximation by averaging"><title>Averaged Stochastic Gradient Descent optimizer — optim_asgd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Averaged Stochastic Gradient Descent optimizer — optim_asgd"><meta property="og:description" content="Proposed in Acceleration of stochastic approximation by averaging"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Averaged Stochastic Gradient Descent optimizer — optim_asgd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Averaged Stochastic Gradient Descent optimizer — optim_asgd"><meta name="description" content="Proposed in Acceleration of stochastic approximation by averaging"><meta property="og:description" content="Proposed in Acceleration of stochastic approximation by averaging"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Averaged Stochastic Gradient Descent optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-asgd.R" class="external-link"><code>R/optim-asgd.R</code></a></small>
+
+      <h1>Averaged Stochastic Gradient Descent optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-asgd.R" class="external-link"><code>R/optim-asgd.R</code></a></small>
       <div class="d-none name"><code>optim_asgd.Rd</code></div>
     </div>
 
@@ -102,28 +84,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or lists defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float): learning rate</p></dd>
 
 
-<dt>lambda</dt>
+<dt id="arg-lambda">lambda<a class="anchor" aria-label="anchor" href="#arg-lambda"></a></dt>
 <dd><p>(float, optional): decay term (default: 1e-4)</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(float, optional): power for eta update (default: 0.75)</p></dd>
 
 
-<dt>t0</dt>
+<dt id="arg-t-">t0<a class="anchor" aria-label="anchor" href="#arg-t-"></a></dt>
 <dd><p>(float, optional): point at which to start averaging (default: 1e6)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
 
 </dl></div>
@@ -140,17 +124,17 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_asgd</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/optim_ignite_adagrad.html b/reference/optim_ignite_adagrad.html
new file mode 100644
index 0000000000..cb27929e28
--- /dev/null
+++ b/reference/optim_ignite_adagrad.html
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+<!DOCTYPE html>
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+
+      <h1>LibTorch implementation of Adagrad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>optim_ignite_adagrad.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Proposed in <a href="https://jmlr.org/papers/v12/duchi11a.html" class="external-link">Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a></p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">optim_ignite_adagrad</span><span class="op">(</span></span>
+<span>  <span class="va">params</span>,</span>
+<span>  lr <span class="op">=</span> <span class="fl">0.01</span>,</span>
+<span>  lr_decay <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  weight_decay <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  initial_accumulator_value <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  eps <span class="op">=</span> <span class="fl">1e-10</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
+<dd><p>(iterable): list of parameters to optimize or list parameter groups</p></dd>
+
+
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
+<dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
+
+
+<dt id="arg-lr-decay">lr_decay<a class="anchor" aria-label="anchor" href="#arg-lr-decay"></a></dt>
+<dd><p>(float, optional): learning rate decay (default: 0)</p></dd>
+
+
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+
+
+<dt id="arg-initial-accumulator-value">initial_accumulator_value<a class="anchor" aria-label="anchor" href="#arg-initial-accumulator-value"></a></dt>
+<dd><p>the initial value for the accumulator. (default: 0)</p>
+<p>Adagrad is an especially good optimizer for sparse data.
+It individually modifies learning rate for every single parameter,
+dividing the original learning rate value by sum of the squares of the gradients.
+It causes that the rarely occurring features get greater learning rates.
+The main downside of this method is the fact that learning rate may be
+getting small too fast, so that at some point a model cannot learn anymore.</p></dd>
+
+
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-10)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="fields-and-methods">Fields and Methods<a class="anchor" aria-label="anchor" href="#fields-and-methods"></a></h2>
+
+
+<p>See <code><a href="OptimizerIgnite.html">OptimizerIgnite</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_ignite_adagrad</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/optim_ignite_adam.html b/reference/optim_ignite_adam.html
new file mode 100644
index 0000000000..9337bc58e2
--- /dev/null
+++ b/reference/optim_ignite_adam.html
@@ -0,0 +1,156 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LibTorch implementation of Adam — optim_ignite_adam • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LibTorch implementation of Adam — optim_ignite_adam"><meta name="description" content="It has been proposed in Adam: A Method for Stochastic Optimization."><meta property="og:description" content="It has been proposed in Adam: A Method for Stochastic Optimization."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+
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+
+
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>LibTorch implementation of Adam</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>optim_ignite_adam.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>It has been proposed in <a href="https://arxiv.org/abs/1412.6980" class="external-link">Adam: A Method for Stochastic Optimization</a>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">optim_ignite_adam</span><span class="op">(</span></span>
+<span>  <span class="va">params</span>,</span>
+<span>  lr <span class="op">=</span> <span class="fl">0.001</span>,</span>
+<span>  betas <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.9</span>, <span class="fl">0.999</span><span class="op">)</span>,</span>
+<span>  eps <span class="op">=</span> <span class="fl">1e-08</span>,</span>
+<span>  weight_decay <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  amsgrad <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
+<dd><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></dd>
+
+
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
+<dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
+
+
+<dt id="arg-betas">betas<a class="anchor" aria-label="anchor" href="#arg-betas"></a></dt>
+<dd><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
+running averages of gradient and its square (default: (0.9, 0.999))</p></dd>
+
+
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></dd>
+
+
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+
+
+<dt id="arg-amsgrad">amsgrad<a class="anchor" aria-label="anchor" href="#arg-amsgrad"></a></dt>
+<dd><p>(boolean, optional): whether to use the AMSGrad variant of this
+algorithm from the paper <a href="https://openreview.net/forum?id=ryQu7f-RZ" class="external-link">On the Convergence of Adam and Beyond</a>
+(default: FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="fields-and-methods">Fields and Methods<a class="anchor" aria-label="anchor" href="#fields-and-methods"></a></h2>
+
+
+<p>See <code><a href="OptimizerIgnite.html">OptimizerIgnite</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_ignite_adam</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
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diff --git a/reference/optim_ignite_adamw.html b/reference/optim_ignite_adamw.html
new file mode 100644
index 0000000000..d29409be3a
--- /dev/null
+++ b/reference/optim_ignite_adamw.html
@@ -0,0 +1,159 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LibTorch implementation of AdamW — optim_ignite_adamw • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LibTorch implementation of AdamW — optim_ignite_adamw"><meta name="description" content="For further details regarding the algorithm we refer to
+Decoupled Weight Decay Regularization"><meta property="og:description" content="For further details regarding the algorithm we refer to
+Decoupled Weight Decay Regularization"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>LibTorch implementation of AdamW</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>optim_ignite_adamw.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>For further details regarding the algorithm we refer to
+<a href="https://arxiv.org/abs/1711.05101" class="external-link">Decoupled Weight Decay Regularization</a></p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">optim_ignite_adamw</span><span class="op">(</span></span>
+<span>  <span class="va">params</span>,</span>
+<span>  lr <span class="op">=</span> <span class="fl">0.001</span>,</span>
+<span>  betas <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.9</span>, <span class="fl">0.999</span><span class="op">)</span>,</span>
+<span>  eps <span class="op">=</span> <span class="fl">1e-08</span>,</span>
+<span>  weight_decay <span class="op">=</span> <span class="fl">0.01</span>,</span>
+<span>  amsgrad <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
+<dd><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></dd>
+
+
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
+<dd><p>(float, optional): learning rate (default: 1e-3)</p></dd>
+
+
+<dt id="arg-betas">betas<a class="anchor" aria-label="anchor" href="#arg-betas"></a></dt>
+<dd><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
+running averages of gradient and its square (default: (0.9, 0.999))</p></dd>
+
+
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></dd>
+
+
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+
+
+<dt id="arg-amsgrad">amsgrad<a class="anchor" aria-label="anchor" href="#arg-amsgrad"></a></dt>
+<dd><p>(boolean, optional): whether to use the AMSGrad variant of this
+algorithm from the paper <a href="https://openreview.net/forum?id=ryQu7f-RZ" class="external-link">On the Convergence of Adam and Beyond</a>
+(default: FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="fields-and-methods">Fields and Methods<a class="anchor" aria-label="anchor" href="#fields-and-methods"></a></h2>
+
+
+<p>See <code><a href="OptimizerIgnite.html">OptimizerIgnite</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_ignite_adamw</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown-footer-right">
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diff --git a/reference/optim_ignite_rmsprop.html b/reference/optim_ignite_rmsprop.html
new file mode 100644
index 0000000000..10a84ce032
--- /dev/null
+++ b/reference/optim_ignite_rmsprop.html
@@ -0,0 +1,159 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LibTorch implementation of RMSprop — optim_ignite_rmsprop • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LibTorch implementation of RMSprop — optim_ignite_rmsprop"><meta name="description" content="Proposed by G. Hinton in his course."><meta property="og:description" content="Proposed by G. Hinton in his course."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>LibTorch implementation of RMSprop</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>optim_ignite_rmsprop.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Proposed by G. Hinton in his course.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">optim_ignite_rmsprop</span><span class="op">(</span></span>
+<span>  <span class="va">params</span>,</span>
+<span>  lr <span class="op">=</span> <span class="fl">0.01</span>,</span>
+<span>  alpha <span class="op">=</span> <span class="fl">0.99</span>,</span>
+<span>  eps <span class="op">=</span> <span class="fl">1e-08</span>,</span>
+<span>  weight_decay <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  momentum <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  centered <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
+<dd><p>(iterable): iterable of parameters to optimize or list defining parameter groups</p></dd>
+
+
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
+<dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
+
+
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
+<dd><p>(float, optional): smoothing constant (default: 0.99)</p></dd>
+
+
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
+<dd><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></dd>
+
+
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
+<dd><p>optional weight decay penalty. (default: 0)</p></dd>
+
+
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
+<dd><p>(float, optional): momentum factor (default: 0)</p></dd>
+
+
+<dt id="arg-centered">centered<a class="anchor" aria-label="anchor" href="#arg-centered"></a></dt>
+<dd><p>(bool, optional) : if <code>TRUE</code>, compute the centered RMSProp,
+the gradient is normalized by an estimation of its variance
+weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="fields-and-methods">Fields and Methods<a class="anchor" aria-label="anchor" href="#fields-and-methods"></a></h2>
+
+
+<p>See <code><a href="OptimizerIgnite.html">OptimizerIgnite</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_ignite_rmsprop</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
+
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+
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+
diff --git a/reference/optim_ignite_sgd.html b/reference/optim_ignite_sgd.html
new file mode 100644
index 0000000000..7cf2291ed5
--- /dev/null
+++ b/reference/optim_ignite_sgd.html
@@ -0,0 +1,158 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LibTorch implementation of SGD — optim_ignite_sgd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LibTorch implementation of SGD — optim_ignite_sgd"><meta name="description" content="Implements stochastic gradient descent (optionally with momentum).
+Nesterov momentum is based on the formula from
+On the importance of initialization and momentum in deep learning."><meta property="og:description" content="Implements stochastic gradient descent (optionally with momentum).
+Nesterov momentum is based on the formula from
+On the importance of initialization and momentum in deep learning."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>LibTorch implementation of SGD</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>optim_ignite_sgd.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Implements stochastic gradient descent (optionally with momentum).
+Nesterov momentum is based on the formula from
+On the importance of initialization and momentum in deep learning.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">optim_ignite_sgd</span><span class="op">(</span></span>
+<span>  <span class="va">params</span>,</span>
+<span>  lr <span class="op">=</span> <span class="fu"><a href="optim_required.html">optim_required</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  momentum <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  dampening <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  weight_decay <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  nesterov <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
+<dd><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></dd>
+
+
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
+<dd><p>(float): learning rate</p></dd>
+
+
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
+<dd><p>(float, optional): momentum factor (default: 0)</p></dd>
+
+
+<dt id="arg-dampening">dampening<a class="anchor" aria-label="anchor" href="#arg-dampening"></a></dt>
+<dd><p>(float, optional): dampening for momentum (default: 0)</p></dd>
+
+
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
+<dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
+
+
+<dt id="arg-nesterov">nesterov<a class="anchor" aria-label="anchor" href="#arg-nesterov"></a></dt>
+<dd><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="fields-and-methods">Fields and Methods<a class="anchor" aria-label="anchor" href="#fields-and-methods"></a></h2>
+
+
+<p>See <code><a href="OptimizerIgnite.html">OptimizerIgnite</a></code>.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_ignite_sgd</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
+
+    </footer></div>
+
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+
+
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+
diff --git a/reference/optim_lbfgs.html b/reference/optim_lbfgs.html
index 09a6a1cc2a..66e8398eb1 100644
--- a/reference/optim_lbfgs.html
+++ b/reference/optim_lbfgs.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Implements L-BFGS algorithm, heavily inspired by
-minFunc"><title>LBFGS optimizer — optim_lbfgs • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LBFGS optimizer — optim_lbfgs"><meta property="og:description" content="Implements L-BFGS algorithm, heavily inspired by
-minFunc"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LBFGS optimizer — optim_lbfgs • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LBFGS optimizer — optim_lbfgs"><meta name="description" content="Implements L-BFGS algorithm, heavily inspired by
+minFunc"><meta property="og:description" content="Implements L-BFGS algorithm, heavily inspired by
+minFunc"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
   window.dataLayer = window.dataLayer || [];
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   gtag('js', new Date());
@@ -12,77 +9,62 @@
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>LBFGS optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-lbfgs.R" class="external-link"><code>R/optim-lbfgs.R</code></a></small>
+
+      <h1>LBFGS optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-lbfgs.R" class="external-link"><code>R/optim-lbfgs.R</code></a></small>
       <div class="d-none name"><code>optim_lbfgs.Rd</code></div>
     </div>
 
@@ -107,40 +89,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or dicts defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float): learning rate (default: 1)</p></dd>
 
 
-<dt>max_iter</dt>
+<dt id="arg-max-iter">max_iter<a class="anchor" aria-label="anchor" href="#arg-max-iter"></a></dt>
 <dd><p>(int): maximal number of iterations per optimization step
 (default: 20)</p></dd>
 
 
-<dt>max_eval</dt>
+<dt id="arg-max-eval">max_eval<a class="anchor" aria-label="anchor" href="#arg-max-eval"></a></dt>
 <dd><p>(int): maximal number of function evaluations per optimization
 step (default: max_iter * 1.25).</p></dd>
 
 
-<dt>tolerance_grad</dt>
+<dt id="arg-tolerance-grad">tolerance_grad<a class="anchor" aria-label="anchor" href="#arg-tolerance-grad"></a></dt>
 <dd><p>(float): termination tolerance on first order optimality
 (default: 1e-5).</p></dd>
 
 
-<dt>tolerance_change</dt>
+<dt id="arg-tolerance-change">tolerance_change<a class="anchor" aria-label="anchor" href="#arg-tolerance-change"></a></dt>
 <dd><p>(float): termination tolerance on function
 value/parameter changes (default: 1e-9).</p></dd>
 
 
-<dt>history_size</dt>
+<dt id="arg-history-size">history_size<a class="anchor" aria-label="anchor" href="#arg-history-size"></a></dt>
 <dd><p>(int): update history size (default: 100).</p></dd>
 
 
-<dt>line_search_fn</dt>
+<dt id="arg-line-search-fn">line_search_fn<a class="anchor" aria-label="anchor" href="#arg-line-search-fn"></a></dt>
 <dd><p>(str): either 'strong_wolfe' or None (default: None).</p></dd>
 
 </dl></div>
@@ -158,7 +142,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 
 <p>This optimizer doesn't support per-parameter options and parameter
@@ -183,7 +167,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="va">x2</span> <span class="op">&lt;-</span> <span class="va">x</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span></span></span>
 <span class="r-in"><span>  <span class="op">(</span><span class="va">a</span> <span class="op">-</span> <span class="va">x1</span><span class="op">)</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">b</span> <span class="op">*</span> <span class="op">(</span><span class="va">x2</span> <span class="op">-</span> <span class="va">x1</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span><span class="op">^</span><span class="fl">2</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span> </span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, requires_grad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_lbfgs</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
@@ -193,22 +177,22 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="va">value</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="va">value</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span>  </span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">num_iterations</span> <span class="op">&lt;-</span> <span class="fl">2</span></span></span>
 <span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="va">num_iterations</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span>  <span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="va">calc_loss</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-in"><span>    </span></span>
+<span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">rosenbrock</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1e-12 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.7554</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4.5475</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ][ grad_fn = &lt;AddBackward0&gt; ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -217,14 +201,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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index ed245fcd50..6a24387eb5 100644
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+++ b/reference/optim_required.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dummy value indicating a required value. — optim_required • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dummy value indicating a required value. — optim_required"><meta name="description" content="Dummy value indicating a required value."><meta property="og:description" content="Dummy value indicating a required value."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Dummy value indicating a required value.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
+
+      <h1>Dummy value indicating a required value.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
       <div class="d-none name"><code>optim_required.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>export</p>
+    <p>Dummy value indicating a required value.</p>
     </div>
 
     <div class="section level2">
@@ -102,14 +84,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/optim_rmsprop.html b/reference/optim_rmsprop.html
index 2c16acdf3c..cfcbd13ced 100644
--- a/reference/optim_rmsprop.html
+++ b/reference/optim_rmsprop.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Proposed by G. Hinton in his course."><title>RMSprop optimizer — optim_rmsprop • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="RMSprop optimizer — optim_rmsprop"><meta property="og:description" content="Proposed by G. Hinton in his course."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>RMSprop optimizer — optim_rmsprop • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="RMSprop optimizer — optim_rmsprop"><meta name="description" content="Proposed by G. Hinton in his course."><meta property="og:description" content="Proposed by G. Hinton in his course."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   gtag('js', new Date());
@@ -10,77 +7,62 @@
   gtag('config', 'G-9ZJSKW3L0N');
 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/installation.html">Installation</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>RMSprop optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-rmsprop.R" class="external-link"><code>R/optim-rmsprop.R</code></a></small>
+
+      <h1>RMSprop optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-rmsprop.R" class="external-link"><code>R/optim-rmsprop.R</code></a></small>
       <div class="d-none name"><code>optim_rmsprop.Rd</code></div>
     </div>
 
@@ -103,32 +85,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or list defining parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(float, optional): smoothing constant (default: 0.99)</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional): term added to the denominator to improve
 numerical stability (default: 1e-8)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>optional weight decay penalty. (default: 0)</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>(float, optional): momentum factor (default: 0)</p></dd>
 
 
-<dt>centered</dt>
+<dt id="arg-centered">centered<a class="anchor" aria-label="anchor" href="#arg-centered"></a></dt>
 <dd><p>(bool, optional) : if <code>TRUE</code>, compute the centered RMSProp,
 the gradient is normalized by an estimation of its variance
 weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
@@ -158,7 +142,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 creation and usage.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -167,14 +151,14 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/optim_rprop.html b/reference/optim_rprop.html
index b22e9287d0..41f4d84673 100644
--- a/reference/optim_rprop.html
+++ b/reference/optim_rprop.html
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-      <img src="" class="logo" alt=""><h1>Implements the resilient backpropagation algorithm.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-rprop.R" class="external-link"><code>R/optim-rprop.R</code></a></small>
+
+      <h1>Implements the resilient backpropagation algorithm.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-rprop.R" class="external-link"><code>R/optim-rprop.R</code></a></small>
       <div class="d-none name"><code>optim_rprop.Rd</code></div>
     </div>
 
@@ -95,22 +77,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or lists defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float, optional): learning rate (default: 1e-2)</p></dd>
 
 
-<dt>etas</dt>
+<dt id="arg-etas">etas<a class="anchor" aria-label="anchor" href="#arg-etas"></a></dt>
 <dd><p>(Tuple(float, float), optional): pair of (etaminus, etaplis), that
 are multiplicative increase and decrease factors
 (default: (0.5, 1.2))</p></dd>
 
 
-<dt>step_sizes</dt>
+<dt id="arg-step-sizes">step_sizes<a class="anchor" aria-label="anchor" href="#arg-step-sizes"></a></dt>
 <dd><p>(vector(float, float), optional): a pair of minimal and
 maximal allowed step sizes (default: (1e-6, 50))</p></dd>
 
@@ -128,16 +112,16 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_rprop</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/optim_sgd.html b/reference/optim_sgd.html
index a14672bb45..fb184aed70 100644
--- a/reference/optim_sgd.html
+++ b/reference/optim_sgd.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Implements stochastic gradient descent (optionally with momentum).
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>SGD optimizer — optim_sgd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="SGD optimizer — optim_sgd"><meta name="description" content="Implements stochastic gradient descent (optionally with momentum).
 Nesterov momentum is based on the formula from
-On the importance of initialization and momentum in deep learning."><title>SGD optimizer — optim_sgd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="SGD optimizer — optim_sgd"><meta property="og:description" content="Implements stochastic gradient descent (optionally with momentum).
+On the importance of initialization and momentum in deep learning."><meta property="og:description" content="Implements stochastic gradient descent (optionally with momentum).
 Nesterov momentum is based on the formula from
-On the importance of initialization and momentum in deep learning."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>SGD optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim-sgd.R" class="external-link"><code>R/optim-sgd.R</code></a></small>
+
+      <h1>SGD optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim-sgd.R" class="external-link"><code>R/optim-sgd.R</code></a></small>
       <div class="d-none name"><code>optim_sgd.Rd</code></div>
     </div>
 
@@ -108,34 +90,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>params</dt>
+
+
+<dl><dt id="arg-params">params<a class="anchor" aria-label="anchor" href="#arg-params"></a></dt>
 <dd><p>(iterable): iterable of parameters to optimize or dicts defining
 parameter groups</p></dd>
 
 
-<dt>lr</dt>
+<dt id="arg-lr">lr<a class="anchor" aria-label="anchor" href="#arg-lr"></a></dt>
 <dd><p>(float): learning rate</p></dd>
 
 
-<dt>momentum</dt>
+<dt id="arg-momentum">momentum<a class="anchor" aria-label="anchor" href="#arg-momentum"></a></dt>
 <dd><p>(float, optional): momentum factor (default: 0)</p></dd>
 
 
-<dt>dampening</dt>
+<dt id="arg-dampening">dampening<a class="anchor" aria-label="anchor" href="#arg-dampening"></a></dt>
 <dd><p>(float, optional): dampening for momentum (default: 0)</p></dd>
 
 
-<dt>weight_decay</dt>
+<dt id="arg-weight-decay">weight_decay<a class="anchor" aria-label="anchor" href="#arg-weight-decay"></a></dt>
 <dd><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></dd>
 
 
-<dt>nesterov</dt>
+<dt id="arg-nesterov">nesterov<a class="anchor" aria-label="anchor" href="#arg-nesterov"></a></dt>
 <dd><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
-    
+
 
 
 <p>The implementation of SGD with Momentum-Nesterov subtly differs from
@@ -172,17 +156,17 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span> <span class="op">&lt;-</span> <span class="fu">optim_sgd</span><span class="op">(</span><span class="va">model</span><span class="op">$</span><span class="fu">parameters</span><span class="op">(</span><span class="op">)</span>, lr <span class="op">=</span> <span class="fl">0.1</span>, momentum <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">loss_fn</span><span class="op">(</span><span class="fu">model</span><span class="op">(</span><span class="va">input</span><span class="op">)</span>, <span class="va">target</span><span class="op">)</span><span class="op">$</span><span class="fu">backward</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">optimizer</span><span class="op">$</span><span class="fu">step</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -191,14 +175,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/optimizer.html b/reference/optimizer.html
index 8eefbd94de..d8f1437ff2 100644
--- a/reference/optimizer.html
+++ b/reference/optimizer.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="When implementing custom optimizers you will usually need to implement
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Creates a custom optimizer — optimizer • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Creates a custom optimizer — optimizer"><meta name="description" content="When implementing custom optimizers you will usually need to implement
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-      <img src="" class="logo" alt=""><h1>Creates a custom optimizer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
+
+      <h1>Creates a custom optimizer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/optim.R" class="external-link"><code>R/optim.R</code></a></small>
       <div class="d-none name"><code>optimizer.Rd</code></div>
     </div>
 
@@ -108,29 +90,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>name</dt>
+
+
+<dl><dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>(optional) name of the optimizer</p></dd>
 
 
-<dt>inherit</dt>
+<dt id="arg-inherit">inherit<a class="anchor" aria-label="anchor" href="#arg-inherit"></a></dt>
 <dd><p>(optional) you can inherit from other optimizers to re-use
 some methods.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Pass any number of fields or methods. You should at least define
 the <code>initialize</code> and <code>step</code> methods. See the examples section.</p></dd>
 
 
-<dt>private</dt>
+<dt id="arg-private">private<a class="anchor" aria-label="anchor" href="#arg-private"></a></dt>
 <dd><p>(optional) a list of private methods for the optimizer.</p></dd>
 
 
-<dt>active</dt>
+<dt id="arg-active">active<a class="anchor" aria-label="anchor" href="#arg-active"></a></dt>
 <dd><p>(optional) a list of active methods for the optimizer.</p></dd>
 
 
-<dt>parent_env</dt>
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
 <dd><p>used to capture the right environment to define the class.
 The default is fine for most situations.</p></dd>
 
@@ -190,7 +174,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -199,14 +183,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/optimizer_ignite.html b/reference/optimizer_ignite.html
new file mode 100644
index 0000000000..6c666de735
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+++ b/reference/optimizer_ignite.html
@@ -0,0 +1,130 @@
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+      <h1>Abstract Base Class for LibTorch Optimizers</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/ignite.R" class="external-link"><code>R/ignite.R</code></a></small>
+      <div class="d-none name"><code>optimizer_ignite.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Abstract base class for wrapping LibTorch C++ optimizers.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">optimizer_ignite</span><span class="op">(</span></span>
+<span>  name <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">...</span>,</span>
+<span>  private <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  active <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  parent_env <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sys.parent.html" class="external-link">parent.frame</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
+<dd><p>(optional) name of the optimizer</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
+<dd><p>Pass any number of fields or methods. You should at least define
+the <code>initialize</code> and <code>step</code> methods. See the examples section.</p></dd>
+
+
+<dt id="arg-private">private<a class="anchor" aria-label="anchor" href="#arg-private"></a></dt>
+<dd><p>(optional) a list of private methods for the optimizer.</p></dd>
+
+
+<dt id="arg-active">active<a class="anchor" aria-label="anchor" href="#arg-active"></a></dt>
+<dd><p>(optional) a list of active methods for the optimizer.</p></dd>
+
+
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
+<dd><p>used to capture the right environment to define the class.
+The default is fine for most situations.</p></dd>
+
+</dl></div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/reference/pipe.html b/reference/pipe.html
index d294b0aab0..e040d4232e 100644
--- a/reference/pipe.html
+++ b/reference/pipe.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="See magrittr::%&amp;gt;% for details."><title>Pipe operator — %&gt;% • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pipe operator — %&gt;%"><meta property="og:description" content="See magrittr::%&amp;gt;% for details."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pipe operator — %&gt;% • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pipe operator — %&gt;%"><meta name="description" content="See magrittr::%&amp;gt;% for details."><meta property="og:description" content="See magrittr::%&amp;gt;% for details."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   gtag('js', new Date());
@@ -10,77 +7,62 @@
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-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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-      <img src="" class="logo" alt=""><h1>Pipe operator</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-pipe.R" class="external-link"><code>R/utils-pipe.R</code></a></small>
+
+      <h1>Pipe operator</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-pipe.R" class="external-link"><code>R/utils-pipe.R</code></a></small>
       <div class="d-none name"><code>pipe.Rd</code></div>
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@@ -102,14 +84,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
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diff --git a/reference/reexports.html b/reference/reexports.html
index 08c51e5b7c..ab2fb55103 100644
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+++ b/reference/reexports.html
@@ -1,22 +1,19 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="These objects are imported from other packages. Follow the links
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Re-exporting the as_iterator function. — reexports • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Re-exporting the as_iterator function. — reexports"><meta name="description" content="These objects are imported from other packages. Follow the links
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-      <img src="" class="logo" alt=""><h1>Re-exporting the as_iterator function.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
+
+      <h1>Re-exporting the as_iterator function.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data-dataloader.R" class="external-link"><code>R/utils-data-dataloader.R</code></a></small>
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@@ -102,7 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
     <p>These objects are imported from other packages. Follow the links
 below to see their documentation.</p>
 <dl><dt>coro</dt>
-<dd><p><code><a href="https://rdrr.io/pkg/coro/man/as_iterator.html" class="external-link">as_iterator</a></code>, <code><a href="https://rdrr.io/pkg/coro/man/collect.html" class="external-link">loop</a></code>, <code><a href="https://rdrr.io/pkg/coro/man/yield.html" class="external-link">yield</a></code></p></dd>
+<dd><p><code><a href="https://coro.r-lib.org/reference/as_iterator.html" class="external-link">as_iterator</a></code>, <code><a href="https://coro.r-lib.org/reference/collect.html" class="external-link">loop</a></code>, <code><a href="https://coro.r-lib.org/reference/yield.html" class="external-link">yield</a></code></p></dd>
 
 
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diff --git a/reference/sampler.html b/reference/sampler.html
index 24b74c7b4f..a06f7f9fa7 100644
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@@ -1,10 +1,7 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a new Sampler</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/utils-data-sampler.R" class="external-link"><code>R/utils-data-sampler.R</code></a></small>
+
+      <h1>Creates a new Sampler</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/utils-data-sampler.R" class="external-link"><code>R/utils-data-sampler.R</code></a></small>
       <div class="d-none name"><code>sampler.Rd</code></div>
     </div>
 
@@ -105,29 +87,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>name</dt>
+
+
+<dl><dt id="arg-name">name<a class="anchor" aria-label="anchor" href="#arg-name"></a></dt>
 <dd><p>(optional) name of the sampler</p></dd>
 
 
-<dt>inherit</dt>
+<dt id="arg-inherit">inherit<a class="anchor" aria-label="anchor" href="#arg-inherit"></a></dt>
 <dd><p>(optional) you can inherit from other samplers to re-use
 some methods.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Pass any number of fields or methods. You should at least define
 the <code>initialize</code> and <code>step</code> methods. See the examples section.</p></dd>
 
 
-<dt>private</dt>
+<dt id="arg-private">private<a class="anchor" aria-label="anchor" href="#arg-private"></a></dt>
 <dd><p>(optional) a list of private methods for the sampler</p></dd>
 
 
-<dt>active</dt>
+<dt id="arg-active">active<a class="anchor" aria-label="anchor" href="#arg-active"></a></dt>
 <dd><p>(optional) a list of active methods for the sampler.</p></dd>
 
 
-<dt>parent_env</dt>
+<dt id="arg-parent-env">parent_env<a class="anchor" aria-label="anchor" href="#arg-parent-env"></a></dt>
 <dd><p>used to capture the right environment to define the class.
 The default is fine for most situations.</p></dd>
 
@@ -140,7 +124,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 that sampler.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -149,14 +133,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/set_autocast.html b/reference/set_autocast.html
new file mode 100644
index 0000000000..c511f185a8
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+++ b/reference/set_autocast.html
@@ -0,0 +1,8 @@
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diff --git a/reference/slc.html b/reference/slc.html
index b2414aed7f..f32aea7c39 100644
--- a/reference/slc.html
+++ b/reference/slc.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a slice</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/indexing.R" class="external-link"><code>R/indexing.R</code></a></small>
+
+      <h1>Creates a slice</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/indexing.R" class="external-link"><code>R/indexing.R</code></a></small>
       <div class="d-none name"><code>slc.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>start</dt>
+
+
+<dl><dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>(integer) starting index.</p></dd>
 
 
-<dt>end</dt>
+<dt id="arg-end">end<a class="anchor" aria-label="anchor" href="#arg-end"></a></dt>
 <dd><p>(integer) the last selected index.</p></dd>
 
 
-<dt>step</dt>
+<dt id="arg-step">step<a class="anchor" aria-label="anchor" href="#arg-step"></a></dt>
 <dd><p>(integer) the step between indexes.</p></dd>
 
 </dl></div>
@@ -116,13 +100,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1161</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1016</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2852</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2878</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3140</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -131,14 +115,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/tensor_dataset.html b/reference/tensor_dataset.html
index d55a2b109e..739a3d0da9 100644
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diff --git a/reference/threads.html b/reference/threads.html
index 605c4eea8b..8d358d8b22 100644
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Number of threads</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/threads.R" class="external-link"><code>R/threads.R</code></a></small>
+
+      <h1>Number of threads</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/threads.R" class="external-link"><code>R/threads.R</code></a></small>
       <div class="d-none name"><code>threads.Rd</code></div>
     </div>
 
@@ -101,7 +83,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>num_threads</dt>
+
+
+<dl><dt id="arg-num-threads">num_threads<a class="anchor" aria-label="anchor" href="#arg-num-threads"></a></dt>
 <dd><p>number of threads to set.</p></dd>
 
 </dl></div>
@@ -115,7 +99,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>torch_set_threads do not work on macOS system as it must be 1.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -124,14 +108,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index ca27c40086..e45eb87c86 100644
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Abs — torch_abs • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Abs — torch_abs"><meta name="description" content="Abs"><meta property="og:description" content="Abs"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Abs</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Abs</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_abs.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="abs-input-gt-tensor-">abs(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#abs-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise absolute value of the given <code>input</code> tensor.</p>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_absolute.html b/reference/torch_absolute.html
index 382ddf684a..7da0fdcb85 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Absolute</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Absolute</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_absolute.Rd</code></div>
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@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="absolute-input-out-none-gt-tensor">absolute(input, *, out=None) -&gt; Tensor<a class="anchor" aria-label="anchor" href="#absolute-input-out-none-gt-tensor"></a></h2>
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+
 
 
 <p>Alias for <code><a href="torch_abs.html">torch_abs()</a></code></p>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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     </nav></aside></div>
 
 
@@ -116,14 +100,14 @@ <h2 id="absolute-input-out-none-gt-tensor">absolute(input, *, out=None) -&gt; Te
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/torch_acos.html b/reference/torch_acos.html
index 697e218c24..b42b4f232f 100644
--- a/reference/torch_acos.html
+++ b/reference/torch_acos.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Acos</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Acos</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_acos.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="acos-input-gt-tensor-">acos(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#acos-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the arccosine  of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_acos</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5559</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2260</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5170</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9419</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_acosh.html b/reference/torch_acosh.html
index b4b00b3c6d..cf7fd19c18 100644
--- a/reference/torch_acosh.html
+++ b/reference/torch_acosh.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Acosh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Acosh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_acosh.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="acosh-input-out-none-gt-tensor-">acosh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#acosh-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the inverse hyperbolic cosine of the elements of <code>input</code>.</p>
@@ -124,14 +108,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_acosh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5761</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8503</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6869</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5337</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6603</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7640</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2929</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index d8623d547f..8358e5ba40 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Adaptive_avg_pool1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Adaptive_avg_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_adaptive_avg_pool1d.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>the target output size (single integer)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="adaptive-avg-pool-d-input-output-size-gt-tensor-">adaptive_avg_pool1d(input, output_size) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 1D adaptive average pooling over an input signal composed of
@@ -113,7 +97,7 @@ <h2 id="adaptive-avg-pool-d-input-output-size-gt-tensor-">adaptive_avg_pool1d(in
 <p>See <code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> for details and output shape.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -122,14 +106,14 @@ <h2 id="adaptive-avg-pool-d-input-output-size-gt-tensor-">adaptive_avg_pool1d(in
 </div>
 
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diff --git a/reference/torch_add.html b/reference/torch_add.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Add</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Add</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_add.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor/Number) the second input tensor/number.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(Number) the scalar multiplier for <code>other</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="add-input-other-out-null-">add(input, other, out=NULL) <a class="anchor" aria-label="anchor" href="#add-input-other-out-null-"></a></h2>
-    
+
 
 
 <p>Adds the scalar <code>other</code> to each element of the input <code>input</code>
@@ -122,7 +106,7 @@ <h2 id="add-input-other-out-null-">add(input, other, out=NULL) <a class="anchor"
     </div>
     <div class="section level2">
     <h2 id="add-input-other-alpha-out-null-">add(input, other, *, alpha=1, out=NULL) <a class="anchor" aria-label="anchor" href="#add-input-other-alpha-out-null-"></a></h2>
-    
+
 
 
 <p>Each element of the tensor <code>other</code> is multiplied by the scalar
@@ -153,14 +137,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_add</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9521 -1.7848 -1.0599 -1.0725</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3564  0.5238  1.2487  1.2360</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8335 -0.6662  0.0587  0.0461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5819 -0.4146  0.3103  0.2977</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6399 -0.7199  1.3798  0.6940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2243 -0.1355  1.9642  1.2784</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1605 -1.5202  0.5795 -0.1063</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3106 -2.6704 -0.5707 -1.2565</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -169,14 +153,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_addbmm.html b/reference/torch_addbmm.html
index a845afcb95..8b91fc7e63 100644
--- a/reference/torch_addbmm.html
+++ b/reference/torch_addbmm.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Addbmm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Addbmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_addbmm.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) matrix to be added</p></dd>
 
 
-<dt>batch1</dt>
+<dt id="arg-batch-">batch1<a class="anchor" aria-label="anchor" href="#arg-batch-"></a></dt>
 <dd><p>(Tensor) the first batch of matrices to be multiplied</p></dd>
 
 
-<dt>batch2</dt>
+<dt id="arg-batch-">batch2<a class="anchor" aria-label="anchor" href="#arg-batch-"></a></dt>
 <dd><p>(Tensor) the second batch of matrices to be multiplied</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-">addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a batch matrix-matrix product of matrices stored
@@ -148,13 +132,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_addbmm</span><span class="op">(</span><span class="va">M</span>, <span class="va">batch1</span>, <span class="va">batch2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -7.4262 -4.7504 -1.8705  7.8178 -5.9200</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  7.8648 -0.8338  1.4864 -9.0308 -8.3291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4138 -3.4227  3.5947 -0.8622 -5.4645</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -9.8463  -3.5710  -2.3026  -5.3469  -5.8927</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.8985  -1.8813  -0.5713   1.0045  12.3332</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -11.9484  -9.7646   4.3405  -2.6854  -6.1667</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 47184c2741..e011b3cf64 100644
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@@ -1,8 +1,5 @@
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Addcdiv</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Addcdiv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_addcdiv.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to be added</p></dd>
 
 
-<dt>tensor1</dt>
+<dt id="arg-tensor-">tensor1<a class="anchor" aria-label="anchor" href="#arg-tensor-"></a></dt>
 <dd><p>(Tensor) the numerator tensor</p></dd>
 
 
-<dt>tensor2</dt>
+<dt id="arg-tensor-">tensor2<a class="anchor" aria-label="anchor" href="#arg-tensor-"></a></dt>
 <dd><p>(Tensor) the denominator tensor</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-">addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs the element-wise division of <code>tensor1</code> by <code>tensor2</code>,
@@ -121,7 +105,7 @@ <h2 id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-">addcdiv(input, te
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>Integer division with addcdiv is deprecated, and in a future release
 addcdiv will perform a true division of <code>tensor1</code> and <code>tensor2</code>.
@@ -152,13 +136,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_addcdiv</span><span class="op">(</span><span class="va">t</span>, <span class="va">t1</span>, <span class="va">t2</span>, <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4138 -3.3908 -0.0976</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1676 -0.6541 -0.6481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3376 -2.5442 -0.2679</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3026  0.0542  0.0819</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1963  0.2301 -1.2194</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0807  0.1325 -0.4968</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -167,14 +151,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/torch_addcmul.html b/reference/torch_addcmul.html
index 38027545b4..3642f1877d 100644
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@@ -1,8 +1,5 @@
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Addcmul</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Addcmul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_addcmul.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to be added</p></dd>
 
 
-<dt>tensor1</dt>
+<dt id="arg-tensor-">tensor1<a class="anchor" aria-label="anchor" href="#arg-tensor-"></a></dt>
 <dd><p>(Tensor) the tensor to be multiplied</p></dd>
 
 
-<dt>tensor2</dt>
+<dt id="arg-tensor-">tensor2<a class="anchor" aria-label="anchor" href="#arg-tensor-"></a></dt>
 <dd><p>(Tensor) the tensor to be multiplied</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="addcmul-input-tensor-tensor-value-out-null-gt-tensor-">addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addcmul-input-tensor-tensor-value-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs the element-wise multiplication of <code>tensor1</code>
@@ -138,13 +122,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_addcmul</span><span class="op">(</span><span class="va">t</span>, <span class="va">t1</span>, <span class="va">t2</span>, <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0627  2.9080 -0.3882</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6749  2.6368 -0.3886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7420  2.6837 -0.3886</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4208 -0.4834  1.9171</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4852 -0.4345  1.9426</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4563 -0.4564  1.9312</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -153,14 +137,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_addmm.html b/reference/torch_addmm.html
index c73420ee21..704e3d0c4f 100644
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+++ b/reference/torch_addmm.html
@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Addmm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Addmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_addmm.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) matrix to be added</p></dd>
 
 
-<dt>mat1</dt>
+<dt id="arg-mat-">mat1<a class="anchor" aria-label="anchor" href="#arg-mat-"></a></dt>
 <dd><p>(Tensor) the first matrix to be multiplied</p></dd>
 
 
-<dt>mat2</dt>
+<dt id="arg-mat-">mat2<a class="anchor" aria-label="anchor" href="#arg-mat-"></a></dt>
 <dd><p>(Tensor) the second matrix to be multiplied</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-">addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a matrix multiplication of the matrices <code>mat1</code> and <code>mat2</code>.
@@ -145,12 +129,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_addmm</span><span class="op">(</span><span class="va">M</span>, <span class="va">mat1</span>, <span class="va">mat2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8938 -1.2562 -0.4730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.5750  0.6293  1.7723</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2046  0.9396 -2.2487</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1475  0.1011 -1.0476</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_addmv.html b/reference/torch_addmv.html
index 040818b51b..aa3698c072 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Addmv</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Addmv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_addmv.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) vector to be added</p></dd>
 
 
-<dt>mat</dt>
+<dt id="arg-mat">mat<a class="anchor" aria-label="anchor" href="#arg-mat"></a></dt>
 <dd><p>(Tensor) matrix to be multiplied</p></dd>
 
 
-<dt>vec</dt>
+<dt id="arg-vec">vec<a class="anchor" aria-label="anchor" href="#arg-vec"></a></dt>
 <dd><p>(Tensor) vector to be multiplied</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-">addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a matrix-vector product of the matrix <code>mat</code> and
@@ -146,12 +130,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_addmv</span><span class="op">(</span><span class="va">M</span>, <span class="va">mat</span>, <span class="va">vec</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4158</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.7403</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.1906</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_addr.html b/reference/torch_addr.html
index 944674950a..288fa64dd2 100644
--- a/reference/torch_addr.html
+++ b/reference/torch_addr.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Addr</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Addr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_addr.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) matrix to be added</p></dd>
 
 
-<dt>vec1</dt>
+<dt id="arg-vec-">vec1<a class="anchor" aria-label="anchor" href="#arg-vec-"></a></dt>
 <dd><p>(Tensor) the first vector of the outer product</p></dd>
 
 
-<dt>vec2</dt>
+<dt id="arg-vec-">vec2<a class="anchor" aria-label="anchor" href="#arg-vec-"></a></dt>
 <dd><p>(Tensor) the second vector of the outer product</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="addr-input-vec-vec-beta-alpha-out-null-gt-tensor-">addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs the outer-product of vectors <code>vec1</code> and <code>vec2</code>
@@ -153,7 +137,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -162,14 +146,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_allclose.html b/reference/torch_allclose.html
index 5af1524a2f..31c3204c38 100644
--- a/reference/torch_allclose.html
+++ b/reference/torch_allclose.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Allclose</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Allclose</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_allclose.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) first tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) second tensor to compare</p></dd>
 
 
-<dt>rtol</dt>
+<dt id="arg-rtol">rtol<a class="anchor" aria-label="anchor" href="#arg-rtol"></a></dt>
 <dd><p>(float, optional) relative tolerance. Default: 1e-05</p></dd>
 
 
-<dt>atol</dt>
+<dt id="arg-atol">atol<a class="anchor" aria-label="anchor" href="#arg-atol"></a></dt>
 <dd><p>(float, optional) absolute tolerance. Default: 1e-08</p></dd>
 
 
-<dt>equal_nan</dt>
+<dt id="arg-equal-nan">equal_nan<a class="anchor" aria-label="anchor" href="#arg-equal-nan"></a></dt>
 <dd><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-">allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool <a class="anchor" aria-label="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a></h2>
-    
+
 
 
 <p>This function checks if all <code>input</code> and <code>other</code> satisfy the condition:</p>
@@ -140,7 +124,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
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diff --git a/reference/torch_amax.html b/reference/torch_amax.html
index a0a29ce54c..2f1aa8dc99 100644
--- a/reference/torch_amax.html
+++ b/reference/torch_amax.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Amax — torch_amax • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Amax — torch_amax"><meta name="description" content="Amax"><meta property="og:description" content="Amax"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Amax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Amax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_amax.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
@@ -118,7 +102,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="amax-input-dim-keepdim-false-out-none-gt-tensor-">amax(input, dim, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#amax-input-dim-keepdim-false-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the maximum value of each slice of the <code>input</code> tensor in the given
@@ -134,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_amax</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7694</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8770</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3539</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0024</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6182</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2393</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1946</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_amin.html b/reference/torch_amin.html
index 11c028efad..000e03958c 100644
--- a/reference/torch_amin.html
+++ b/reference/torch_amin.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Amin — torch_amin • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Amin — torch_amin"><meta name="description" content="Amin"><meta property="og:description" content="Amin"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Amin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Amin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_amin.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
@@ -121,7 +105,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="amin-input-dim-keepdim-false-out-none-gt-tensor-">amin(input, dim, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#amin-input-dim-keepdim-false-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the minimum value of each slice of the <code>input</code> tensor in the given
@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_amin</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8406</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5179</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9084</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.1076</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6590</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5597</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.0884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3251</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -153,14 +137,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_angle.html b/reference/torch_angle.html
index 1eb7688f32..a466cf4c3e 100644
--- a/reference/torch_angle.html
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Angle</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Angle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_angle.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="angle-input-gt-tensor-">angle(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#angle-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise angle (in radians) of the given <code>input</code> tensor.</p>
@@ -113,14 +97,14 @@ <h2 id="angle-input-gt-tensor-">angle(input) -&gt; Tensor <a class="anchor" aria
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu">torch_angle</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">*</span><span class="fl">180</span><span class="op">/</span><span class="fl">3.14159</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -129,14 +113,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_arange.html b/reference/torch_arange.html
index a9a24a1a1a..d3bd71ad4c 100644
--- a/reference/torch_arange.html
+++ b/reference/torch_arange.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Arange</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Arange</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_arange.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>start</dt>
+
+
+<dl><dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></dd>
 
 
-<dt>end</dt>
+<dt id="arg-end">end<a class="anchor" aria-label="anchor" href="#arg-end"></a></dt>
 <dd><p>(Number) the ending value for the set of points</p></dd>
 
 
-<dt>step</dt>
+<dt id="arg-step">step<a class="anchor" aria-label="anchor" href="#arg-step"></a></dt>
 <dd><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a 1-D tensor of size \(\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil\)
@@ -163,7 +147,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -172,14 +156,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_arccos.html b/reference/torch_arccos.html
index 11a946a83e..1ed7d1a6bc 100644
--- a/reference/torch_arccos.html
+++ b/reference/torch_arccos.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Arccos</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Arccos</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_arccos.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="arccos-input-out-none-gt-tensor-">arccos(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arccos-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_acos.html">torch_acos()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -116,14 +100,14 @@ <h2 id="arccos-input-out-none-gt-tensor-">arccos(input, *, out=None) -&gt; Tenso
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
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diff --git a/reference/torch_arccosh.html b/reference/torch_arccosh.html
index 9e5b3fd209..bc0b31b47d 100644
--- a/reference/torch_arccosh.html
+++ b/reference/torch_arccosh.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Arccosh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Arccosh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_arccosh.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="arccosh-input-out-none-gt-tensor-">arccosh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arccosh-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_acosh.html">torch_acosh()</a></code>.</p>
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@@ -116,14 +100,14 @@ <h2 id="arccosh-input-out-none-gt-tensor-">arccosh(input, *, out=None) -&gt; Ten
 </div>
 
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diff --git a/reference/torch_arcsin.html b/reference/torch_arcsin.html
index 93a3719558..8f8b2f2cd0 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Arcsin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Arcsin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_arcsin.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="arcsin-input-out-none-gt-tensor-">arcsin(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arcsin-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_asin.html">torch_asin()</a></code>.</p>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -116,14 +100,14 @@ <h2 id="arcsin-input-out-none-gt-tensor-">arcsin(input, *, out=None) -&gt; Tenso
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_arcsinh.html b/reference/torch_arcsinh.html
index a4d63f8ae0..bdbf946991 100644
--- a/reference/torch_arcsinh.html
+++ b/reference/torch_arcsinh.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Arcsinh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Arcsinh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_arcsinh.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="arcsinh-input-out-none-gt-tensor-">arcsinh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arcsinh-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_asinh.html">torch_asinh()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="arcsinh-input-out-none-gt-tensor-">arcsinh(input, *, out=None) -&gt; Ten
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_arctan.html b/reference/torch_arctan.html
index 396139b390..387f265997 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Arctan</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Arctan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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     <div class="section level2">
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-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
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-    
+
 
 
 <p>Alias for <code><a href="torch_atan.html">torch_atan()</a></code>.</p>
     </div>
 
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="arctan-input-out-none-gt-tensor-">arctan(input, *, out=None) -&gt; Tenso
 </div>
 
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-      <img src="" class="logo" alt=""><h1>Arctanh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Arctanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_arctanh.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="arctanh-input-out-none-gt-tensor-">arctanh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#arctanh-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_atanh.html">torch_atanh()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="arctanh-input-out-none-gt-tensor-">arctanh(input, *, out=None) -&gt; Ten
 </div>
 
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diff --git a/reference/torch_argmax.html b/reference/torch_argmax.html
index d428024fc7..aff17bad64 100644
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Argmax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Argmax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_argmax.Rd</code></div>
     </div>
 
@@ -91,21 +73,23 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce. If <code>NULL</code>, the argmax of the flattened input is returned.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="argmax-input-gt-longtensor-">argmax(input) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmax-input-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the maximum value of all elements in the <code>input</code> tensor.</p>
@@ -114,7 +98,7 @@ <h2 id="argmax-input-gt-longtensor-">argmax(input) -&gt; LongTensor <a class="an
     </div>
     <div class="section level2">
     <h2 id="argmax-input-dim-keepdim-false-gt-longtensor-">argmax(input, dim, keepdim=False) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the maximum values of a tensor across a dimension.</p>
@@ -126,11 +110,11 @@ <h2 id="argmax-input-dim-keepdim-false-gt-longtensor-">argmax(input, dim, keepdi
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
 <span class="r-in"><span><span class="fu">torch_argmax</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></span>
@@ -138,14 +122,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_argmax</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -154,14 +138,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_argmin.html b/reference/torch_argmin.html
index c04e0a75a5..969e357579 100644
--- a/reference/torch_argmin.html
+++ b/reference/torch_argmin.html
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Argmin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Argmin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_argmin.Rd</code></div>
     </div>
 
@@ -91,21 +73,23 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce. If <code>NULL</code>, the argmin of the flattened input is returned.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="argmin-input-gt-longtensor-">argmin(input) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmin-input-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the minimum value of all elements in the <code>input</code> tensor.</p>
@@ -114,7 +98,7 @@ <h2 id="argmin-input-gt-longtensor-">argmin(input) -&gt; LongTensor <a class="an
     </div>
     <div class="section level2">
     <h2 id="argmin-input-dim-keepdim-false-out-null-gt-longtensor-">argmin(input, dim, keepdim=False, out=NULL) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argmin-input-dim-keepdim-false-out-null-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the minimum values of a tensor across a dimension.</p>
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_argmin</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +136,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_argsort.html b/reference/torch_argsort.html
index 051978d745..caf75281d4 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Argsort</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/with-indices.R" class="external-link"><code>R/with-indices.R</code></a></small>
+
+      <h1>Argsort</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/with-indices.R" class="external-link"><code>R/with-indices.R</code></a></small>
       <div class="d-none name"><code>torch_argsort.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) the dimension to sort along</p></dd>
 
 
-<dt>descending</dt>
+<dt id="arg-descending">descending<a class="anchor" aria-label="anchor" href="#arg-descending"></a></dt>
 <dd><p>(bool, optional) controls the sorting order (ascending or descending)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="argsort-input-dim-descending-false-gt-longtensor-">argsort(input, dim=-1, descending=False) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices that sort a tensor along a given dimension in ascending
@@ -127,14 +111,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_argsort</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1  2  1  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  3  1  4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  2  4  2  1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3  3  4  4</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  4  1  3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4  1  3  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  2  4  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_as_strided.html b/reference/torch_as_strided.html
index 594669ad59..1853e4bf9b 100644
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+++ b/reference/torch_as_strided.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>As_strided</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>As_strided</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_as_strided.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(tuple or ints) the shape of the output tensor</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(tuple or ints) the stride of the output tensor</p></dd>
 
 
-<dt>storage_offset</dt>
+<dt id="arg-storage-offset">storage_offset<a class="anchor" aria-label="anchor" href="#arg-storage-offset"></a></dt>
 <dd><p>(int, optional) the offset in the underlying storage of the output tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="as-strided-input-size-stride-storage-offset-gt-tensor-">as_strided(input, size, stride, storage_offset=0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Create a view of an existing <code>torch_Tensor</code> <code>input</code> with specified
@@ -121,7 +105,7 @@ <h2 id="as-strided-input-size-stride-storage-offset-gt-tensor-">as_strided(input
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>More than one element of a created tensor may refer to a single memory
 location. As a result, in-place operations (especially ones that are
@@ -145,12 +129,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">t</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0386  0.9525</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9742 -0.9887</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1008  1.8645</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5921  1.5321</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_asin.html b/reference/torch_asin.html
index b7e9773495..42a8181407 100644
--- a/reference/torch_asin.html
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@@ -1,8 +1,5 @@
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+
+      <h1>Asin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_asin.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="asin-input-out-null-gt-tensor-">asin(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#asin-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the arcsine  of the elements of <code>input</code>.</p>
@@ -119,14 +103,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_asin</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9477</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2969</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.01 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  7.9097</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -9.9271</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0865</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
+
+
 
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diff --git a/reference/torch_asinh.html b/reference/torch_asinh.html
index 73a5bcb588..100c5dd767 100644
--- a/reference/torch_asinh.html
+++ b/reference/torch_asinh.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Asinh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Asinh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_asinh.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="asinh-input-out-none-gt-tensor-">asinh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#asinh-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the inverse hyperbolic sine of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_asinh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6463</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0318</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6190</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2788</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3991</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0148</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3608</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/torch_atan.html b/reference/torch_atan.html
index 016e15f52b..aaadf036d7 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Atan</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Atan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_atan.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="atan-input-out-null-gt-tensor-">atan(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#atan-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the arctangent  of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_atan</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3500</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0859</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1293</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3648</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8036</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5295</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7657</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_atan2.html b/reference/torch_atan2.html
index d9f1a42ce1..e36d0e7433 100644
--- a/reference/torch_atan2.html
+++ b/reference/torch_atan2.html
@@ -1,8 +1,5 @@
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Atan2</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Atan2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_atan2.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="atan-input-other-out-null-gt-tensor-">atan2(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#atan-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
@@ -127,14 +111,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_atan2</span><span class="op">(</span><span class="va">a</span>, <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0465</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.7011</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.7868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5454</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1075</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4082</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4862</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9404</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_atanh.html b/reference/torch_atanh.html
index 2192d1ed2b..032399ac47 100644
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+++ b/reference/torch_atanh.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Atanh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Atanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_atanh.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="atanh-input-out-none-gt-tensor-">atanh(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#atanh-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the inverse hyperbolic tangent of the elements of <code>input</code>.</p>
@@ -125,14 +109,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_atanh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0134</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1849</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9015</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.6633</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3951</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2666</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7613</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_atleast_1d.html b/reference/torch_atleast_1d.html
index a23cdd9ddb..eb354b817c 100644
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+++ b/reference/torch_atleast_1d.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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+Input tensors with one or more dimensions are returned as-is."><meta property="og:description" content="Returns a 1-dimensional view of each input tensor with zero dimensions.
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-      <img src="" class="logo" alt=""><h1>Atleast_1d</h1>
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+
+      <h1>Atleast_1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_atleast_1d.Rd</code></div>
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+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
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 </dl></div>
@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_atleast_2d.html b/reference/torch_atleast_2d.html
index d2ded70545..f250732ba0 100644
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@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>Atleast_2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Atleast_2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_atleast_2d.Rd</code></div>
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@@ -98,7 +80,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
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 </dl></div>
@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
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diff --git a/reference/torch_atleast_3d.html b/reference/torch_atleast_3d.html
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-      <img src="" class="logo" alt=""><h1>Atleast_3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Atleast_3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_atleast_3d.Rd</code></div>
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@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor or list of Tensors)</p></dd>
 
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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index 2e5948aa42..c832a58caf 100644
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-      <img src="" class="logo" alt=""><h1>Avg_pool1d</h1>
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+
+      <h1>Avg_pool1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_avg_pool1d.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></dd>
 
 
-<dt>kernel_size</dt>
+<dt id="arg-kernel-size">kernel_size<a class="anchor" aria-label="anchor" href="#arg-kernel-size"></a></dt>
 <dd><p>the size of the window. Can be a single number or a tuple <code>(kW,)</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the window. Can be a single number or a tuple <code>(sW,)</code>. Default: <code>kernel_size</code></p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit zero paddings on both sides of the input. Can be a single number or a tuple <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>ceil_mode</dt>
+<dt id="arg-ceil-mode">ceil_mode<a class="anchor" aria-label="anchor" href="#arg-ceil-mode"></a></dt>
 <dd><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape. Default: <code>FALSE</code></p></dd>
 
 
-<dt>count_include_pad</dt>
+<dt id="arg-count-include-pad">count_include_pad<a class="anchor" aria-label="anchor" href="#arg-count-include-pad"></a></dt>
 <dd><p>when <code>TRUE</code>, will include the zero-padding in the averaging calculation. Default: <code>TRUE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-">avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 1D average pooling over an input signal composed of several
@@ -136,7 +120,7 @@ <h2 id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-i
 <p>See <code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> for details and output shape.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -145,14 +129,14 @@ <h2 id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-i
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_baddbmm.html b/reference/torch_baddbmm.html
index 68d6f4cc19..5f367b685b 100644
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+++ b/reference/torch_baddbmm.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Baddbmm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Baddbmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_baddbmm.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to be added</p></dd>
 
 
-<dt>batch1</dt>
+<dt id="arg-batch-">batch1<a class="anchor" aria-label="anchor" href="#arg-batch-"></a></dt>
 <dd><p>(Tensor) the first batch of matrices to be multiplied</p></dd>
 
 
-<dt>batch2</dt>
+<dt id="arg-batch-">batch2<a class="anchor" aria-label="anchor" href="#arg-batch-"></a></dt>
 <dd><p>(Tensor) the second batch of matrices to be multiplied</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-">baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a batch matrix-matrix product of matrices in <code>batch1</code>
@@ -149,40 +133,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5111 -3.1852  3.7126 -1.3524  3.9963</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.7965 -0.0369 -0.3263 -1.2753  0.0556</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.3793  0.1486  0.1915 -0.4350  1.2126</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.6742 -0.7577  2.6754  0.8191 -0.7530</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.3995 -1.2198 -2.3838 -1.5699 -0.1833</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.8331 -1.6828 -0.1383  2.6218  0.2945</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.5858 -1.5813 -0.6729 -1.2245 -1.2957</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.2479  0.2300  0.6143  1.7530  0.5099</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.0699  2.4970  0.9025 -0.7765  1.3482</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.0776  2.4758 -2.4423  0.0407  1.0181</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9602 -0.7632 -1.9180 -1.5237  2.1434</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.3002 -4.8091 -1.5533  1.2892  0.8208</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8821  0.1650  2.2522 -0.3192 -0.3841</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.8732 -1.4290  0.6768 -1.7308  3.1746</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3767 -0.8012 -0.3291  0.9759 -1.7650</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8408 -0.2855 -1.1812 -0.8825  0.3052</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.4125 -1.0249  2.2428  1.0103  1.4386</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8179  2.3056  0.6533 -0.3723  0.8718</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0757  1.7507  2.5247 -2.5280  0.1248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.0840  1.4858  0.4577 -1.2264  1.5897</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.7945  0.7971 -0.6055 -0.2607  0.7605</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.3023  10.9624   1.4803   0.7257  -3.6036</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.6867   4.3710   2.7404  -0.6179  -0.0689</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    3.8225   4.8994   2.5229   0.5117  -1.4507</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (5,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7051  0.2898  1.5222 -0.2621 -2.5265</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4269 -1.8950 -2.1202  1.3455  0.5589</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3.2963  5.4668  4.0805 -4.1564 -2.0503</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0914 -1.7040  0.5731 -3.0537 -2.3071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.6430 -0.9083 -0.2146 -1.2739  1.5534</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8725  1.0120 -0.3942 -0.9003 -3.1054</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (6,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5190 -0.5352 -1.4169 -0.1021  2.8163</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1018  0.0950  1.8032 -1.3999 -1.6487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2259  2.6865  4.0377  1.6081  1.1882</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.5916 -2.2534  3.0997  1.6804  3.2010</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3672 -0.5168 -3.0351  3.4030  5.5081</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6120 -1.9197 -4.6837  2.6320  1.9936</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -191,14 +175,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 8c0680cd36..b7ea710379 100644
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+++ b/reference/torch_bartlett_window.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Bartlett_window</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Bartlett_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_bartlett_window.Rd</code></div>
     </div>
 
@@ -102,27 +84,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>window_length</dt>
+
+
+<dl><dt id="arg-window-length">window_length<a class="anchor" aria-label="anchor" href="#arg-window-length"></a></dt>
 <dd><p>(int) the size of returned window</p></dd>
 
 
-<dt>periodic</dt>
+<dt id="arg-periodic">periodic<a class="anchor" aria-label="anchor" href="#arg-periodic"></a></dt>
 <dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -132,7 +116,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Bartlett window function.</p>
@@ -154,7 +138,7 @@ <h2 id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-stri
 <code>torch_bartlett_window(L + 1, periodic=False)[:-1])</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-stri
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_bernoulli.html b/reference/torch_bernoulli.html
index d41557f317..ee634b68dd 100644
--- a/reference/torch_bernoulli.html
+++ b/reference/torch_bernoulli.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Bernoulli</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bernoulli</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bernoulli.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of probability values for the Bernoulli
 distribution</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(Number) a probability value. If <code>p</code> is passed than it's used instead of
 the values in <code>self</code> tensor.</p></dd>
 
 
-<dt>generator</dt>
+<dt id="arg-generator">generator<a class="anchor" aria-label="anchor" href="#arg-generator"></a></dt>
 <dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="bernoulli-input-generator-null-out-null-gt-tensor-">bernoulli(input, *, generator=NULL, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bernoulli-input-generator-null-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p>
@@ -150,7 +134,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_bincount.html b/reference/torch_bincount.html
index 6606acef7f..72b7561c89 100644
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+++ b/reference/torch_bincount.html
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-      <img src="" class="logo" alt=""><h1>Bincount</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Bincount</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_bincount.Rd</code></div>
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@@ -91,21 +73,23 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) 1-d int tensor</p></dd>
 
 
-<dt>weights</dt>
+<dt id="arg-weights">weights<a class="anchor" aria-label="anchor" href="#arg-weights"></a></dt>
 <dd><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></dd>
 
 
-<dt>minlength</dt>
+<dt id="arg-minlength">minlength<a class="anchor" aria-label="anchor" href="#arg-minlength"></a></dt>
 <dd><p>(int) optional, minimum number of bins. Should be non-negative.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="bincount-input-weights-null-minlength-gt-tensor-">bincount(input, weights=NULL, minlength=0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bincount-input-weights-null-minlength-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Count the frequency of each value in an array of non-negative ints.</p>
@@ -134,13 +118,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2500</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7500</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_bitwise_and.html b/reference/torch_bitwise_and.html
index 29c501c8f2..f679147122 100644
--- a/reference/torch_bitwise_and.html
+++ b/reference/torch_bitwise_and.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Bitwise_and</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bitwise_and</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bitwise_and.Rd</code></div>
     </div>
 
@@ -95,24 +77,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>NA the first input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>NA the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="bitwise-and-input-other-out-null-gt-tensor-">bitwise_and(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-and-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the bitwise AND of <code>input</code> and <code>other</code>. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical AND.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -121,14 +105,14 @@ <h2 id="bitwise-and-input-other-out-null-gt-tensor-">bitwise_and(input, other, o
 </div>
 
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index 33be5ecec1..ed0dc79a66 100644
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-      <img src="" class="logo" alt=""><h1>Bitwise_not</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bitwise_not</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bitwise_not.Rd</code></div>
     </div>
 
@@ -95,20 +77,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="bitwise-not-input-out-null-gt-tensor-">bitwise_not(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-not-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the bitwise NOT of the given input tensor. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical NOT.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -117,14 +101,14 @@ <h2 id="bitwise-not-input-out-null-gt-tensor-">bitwise_not(input, out=NULL) -&gt
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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index 90adb02c33..e18861d35a 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Bitwise_or</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bitwise_or</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bitwise_or.Rd</code></div>
     </div>
 
@@ -95,24 +77,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>NA the first input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>NA the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="bitwise-or-input-other-out-null-gt-tensor-">bitwise_or(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-or-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the bitwise OR of <code>input</code> and <code>other</code>. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical OR.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -121,14 +105,14 @@ <h2 id="bitwise-or-input-other-out-null-gt-tensor-">bitwise_or(input, other, out
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_bitwise_xor.html b/reference/torch_bitwise_xor.html
index 3a86a9409b..483a40704d 100644
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+++ b/reference/torch_bitwise_xor.html
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-      <img src="" class="logo" alt=""><h1>Bitwise_xor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bitwise_xor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bitwise_xor.Rd</code></div>
     </div>
 
@@ -95,24 +77,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>NA the first input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>NA the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="bitwise-xor-input-other-out-null-gt-tensor-">bitwise_xor(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bitwise-xor-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the bitwise XOR of <code>input</code> and <code>other</code>. The input tensor must be of
 integral or Boolean types. For bool tensors, it computes the logical XOR.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -121,14 +105,14 @@ <h2 id="bitwise-xor-input-other-out-null-gt-tensor-">bitwise_xor(input, other, o
 </div>
 
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index c2bd5e4996..adce2a8858 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Blackman_window</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Blackman_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_blackman_window.Rd</code></div>
     </div>
 
@@ -102,27 +84,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>window_length</dt>
+
+
+<dl><dt id="arg-window-length">window_length<a class="anchor" aria-label="anchor" href="#arg-window-length"></a></dt>
 <dd><p>(int) the size of returned window</p></dd>
 
 
-<dt>periodic</dt>
+<dt id="arg-periodic">periodic<a class="anchor" aria-label="anchor" href="#arg-periodic"></a></dt>
 <dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -132,7 +116,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Blackman window function.</p>
@@ -150,7 +134,7 @@ <h2 id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-stri
 <code>torch_blackman_window(L + 1, periodic=False)[:-1])</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +143,14 @@ <h2 id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-stri
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 385088fc13..697ee58612 100644
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-      <img src="" class="logo" alt=""><h1>Block_diag</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Block_diag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_block_diag.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(list of tensors) One or more tensors with 0, 1, or 2
 dimensions.</p></dd>
 
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{9,10} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -134,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_bmm.html b/reference/torch_bmm.html
index 8955444807..706d04e1e0 100644
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@@ -1,8 +1,5 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Bmm — torch_bmm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Bmm — torch_bmm"><meta name="description" content="Bmm"><meta property="og:description" content="Bmm"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Bmm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bmm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bmm.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first batch of matrices to be multiplied</p></dd>
 
 
-<dt>mat2</dt>
+<dt id="arg-mat-">mat2<a class="anchor" aria-label="anchor" href="#arg-mat-"></a></dt>
 <dd><p>(Tensor) the second batch of matrices to be multiplied</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="bmm-input-mat-out-null-gt-tensor-">bmm(input, mat2, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bmm-input-mat-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a batch matrix-matrix product of matrices stored in <code>input</code>
@@ -136,40 +120,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7765 -0.1350  0.8466  0.8478 -0.7594</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6370 -0.2408  0.3351  2.2860 -0.6668</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9089  0.2050 -1.6219 -2.5549  1.7960</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3372 -0.9793 -0.9178  1.6752 -3.9694</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6731  0.4739  0.6238 -0.8550  1.3649</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.2408 -1.9492  4.0812 -0.5648  1.4745</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.8824  2.0253 -2.1206 -1.3713  0.0871</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.8014 -0.8317 -0.6866 -1.2809  2.3431</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1755 -3.8833  0.2988  7.5040 -3.6713</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4185 -1.6425  0.8958 -0.5948 -0.4059</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3336 -0.8206  1.6552  1.8691 -3.3935</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.9448 -1.6547  1.8130 -3.0308  0.8444</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8480 -0.0850  1.4366  2.9513 -0.3949</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9064 -0.8385  1.3575 -7.3166 -7.2062</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5430 -0.3684  0.4283  0.9606  2.1168</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7618 -0.0694  0.8885 -0.2286  4.5873</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   2.8709  1.3536  0.6855  1.7678 -1.4571</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.9785  2.2843  2.3418 -2.1916  5.1376</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.3839  1.7347  1.1756 -1.6514  0.6408</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6447 -1.2290  1.8483  1.1605 -3.7812</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8861  0.1105  0.4390  1.3200 -0.8549</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0374  1.4050  0.1721  0.9362  1.3186</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2310  0.5466  3.3942  2.2200 -3.1539</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9582 -0.0251 -0.6099 -0.8545 -0.6797</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (5,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4684  1.1384  1.3886  1.1070 -1.7678</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3.1111  0.6010  1.0678 -0.0603 -2.4754</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0265 -0.3575  0.2944 -1.0106 -2.2023</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7116  1.7785 -0.6294  4.2205 -0.1129</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2466  0.0895 -1.5727  5.1407  0.3615</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1041 -1.8901  1.4343 -5.1313 -0.3243</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (6,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3164 -1.3392  0.5580 -1.6123  5.0994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6859 -1.7957 -0.2757  0.7590  0.4473</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4207  0.6214  0.0980  0.1298 -0.7558</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9272 -0.6083  1.1190  2.8363 -0.7331</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1927  0.3642  0.0112  0.2584  0.7779</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9377 -0.2305 -2.8036 -3.4465 -0.5447</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -178,14 +162,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_bool.html b/reference/torch_bool.html
new file mode 100644
index 0000000000..b522acdaf8
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index cfc2506644..836421cb1f 100644
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Broadcast_tensors</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Broadcast_tensors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_broadcast_tensors.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>a list containing any number of tensors of the same type</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="broadcast-tensors-tensors-gt-list-of-tensors-">broadcast_tensors(tensors) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a></h2>
-    
+
 
 
 <p>Broadcasts the given tensors according to broadcasting-semantics.</p>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_bucketize.html b/reference/torch_bucketize.html
index e38f2199e3..16ee0215c6 100644
--- a/reference/torch_bucketize.html
+++ b/reference/torch_bucketize.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Bucketize</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Bucketize</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_bucketize.Rd</code></div>
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@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).</p></dd>
 
 
-<dt>boundaries</dt>
+<dt id="arg-boundaries">boundaries<a class="anchor" aria-label="anchor" href="#arg-boundaries"></a></dt>
 <dd><p>(Tensor) 1-D tensor, must contain a monotonically increasing sequence.</p></dd>
 
 
-<dt>out_int32</dt>
+<dt id="arg-out-int-">out_int32<a class="anchor" aria-label="anchor" href="#arg-out-int-"></a></dt>
 <dd><p>(bool, optional) – indicate the output data type. <code><a href="torch_dtype.html">torch_int32()</a></code>
 if True, <code><a href="torch_dtype.html">torch_int64()</a></code> otherwise. Default value is FALSE, i.e. default output
 data type is <code><a href="torch_dtype.html">torch_int64()</a></code>.</p></dd>
 
 
-<dt>right</dt>
+<dt id="arg-right">right<a class="anchor" aria-label="anchor" href="#arg-right"></a></dt>
 <dd><p>(bool, optional) – if False, return the first suitable location
 that is found. If True, return the last such index. If no suitable index found,
 return 0 for non-numerical value (eg. nan, inf) or the size of boundaries
@@ -120,7 +104,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-">bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the buckets to which each value in the <code>input</code> belongs, where the
@@ -145,7 +129,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -154,14 +138,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_can_cast.html b/reference/torch_can_cast.html
index c28c051378..fd6aa2df23 100644
--- a/reference/torch_can_cast.html
+++ b/reference/torch_can_cast.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Can_cast</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Can_cast</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_can_cast.Rd</code></div>
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@@ -90,22 +72,24 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">torch_can_cast</span><span class="op">(</span><span class="va">from</span>, <span class="va">to</span><span class="op">)</span></span></code></pre></div>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">torch_can_cast</span><span class="op">(</span><span class="va">from_</span>, <span class="va">to</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>from</dt>
+
+
+<dl><dt id="arg-from-">from_<a class="anchor" aria-label="anchor" href="#arg-from-"></a></dt>
 <dd><p>(dtype) The original <code>torch_dtype</code>.</p></dd>
 
 
-<dt>to</dt>
+<dt id="arg-to">to<a class="anchor" aria-label="anchor" href="#arg-to"></a></dt>
 <dd><p>(dtype) The target <code>torch_dtype</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="can-cast-from-to-gt-bool-">can_cast(from, to) -&gt; bool <a class="anchor" aria-label="anchor" href="#can-cast-from-to-gt-bool-"></a></h2>
-    
+
 
 
 <p>Determines if a type conversion is allowed under PyTorch casting rules
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
 </code></pre></div>
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index 8c95fe5d26..d97285b05d 100644
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@@ -1,8 +1,5 @@
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cartesian_prod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cartesian_prod.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>a list containing any number of 1 dimensional tensors.</p></dd>
 
 </dl></div>
@@ -120,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -129,14 +113,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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index 9df1d8f751..4e862fd5d4 100644
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cat — torch_cat • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cat — torch_cat"><meta name="description" content="Cat"><meta property="og:description" content="Cat"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cat</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cat</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cat.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) the dimension over which the tensors are concatenated</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cat-tensors-dim-out-null-gt-tensor-">cat(tensors, dim=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cat-tensors-dim-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Concatenates the given sequence of <code>seq</code> tensors in the given dimension.
@@ -126,12 +110,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_cat</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">x</span>, <span class="va">x</span><span class="op">)</span>, <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3147 -0.6072 -1.1446  0.3147 -0.6072 -1.1446  0.3147 -0.6072 -1.1446</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2502  0.6148  0.3265 -0.2502  0.6148  0.3265 -0.2502  0.6148  0.3265</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0014 -1.5512 -1.0807 -0.0014 -1.5512 -1.0807 -0.0014 -1.5512 -1.0807</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5571 -0.8866 -1.3969  0.5571 -0.8866 -1.3969  0.5571 -0.8866 -1.3969</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,9} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -95,31 +77,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>x1</dt>
+
+
+<dl><dt id="arg-x-">x1<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></dd>
 
 
-<dt>x2</dt>
+<dt id="arg-x-">x2<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></dd>
 
 
-<dt>compute_mode</dt>
+<dt id="arg-compute-mode">compute_mode<a class="anchor" aria-label="anchor" href="#arg-compute-mode"></a></dt>
 <dd><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
     </div>
 
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diff --git a/reference/torch_cdouble.html b/reference/torch_cdouble.html
new file mode 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Ceil</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Ceil</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_ceil.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="ceil-input-out-null-gt-tensor-">ceil(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ceil-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the ceil of the elements of <code>input</code>,
@@ -121,13 +105,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_celu.html b/reference/torch_celu.html
index 06de25001e..e817f8b313 100644
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@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Celu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Celu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_celu.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the alpha value for the CELU formulation. Default: 1.0</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="celu-input-alpha-gt-tensor-">celu(input, alpha=1.) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#celu-input-alpha-gt-tensor-"></a></h2>
-    
+
 
 
 <p>See <code><a href="nnf_celu.html">nnf_celu()</a></code> for more info.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -120,14 +104,14 @@ <h2 id="celu-input-alpha-gt-tensor-">celu(input, alpha=1.) -&gt; Tensor <a class
 </div>
 
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diff --git a/reference/torch_celu_.html b/reference/torch_celu_.html
index 877c52d54b..54c696ef01 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Celu_</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Celu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_celu_.Rd</code></div>
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@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the alpha value for the CELU formulation. Default: 1.0</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="celu-input-alpha-gt-tensor-">celu_(input, alpha=1.) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#celu-input-alpha-gt-tensor-"></a></h2>
-    
+
 
 
 <p>In-place version of <code><a href="torch_celu.html">torch_celu()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -120,14 +104,14 @@ <h2 id="celu-input-alpha-gt-tensor-">celu_(input, alpha=1.) -&gt; Tensor <a clas
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_cfloat.html b/reference/torch_cfloat.html
new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000000..b522acdaf8
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@@ -0,0 +1,8 @@
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diff --git a/reference/torch_chain_matmul.html b/reference/torch_chain_matmul.html
index 0535748b73..676927d15e 100644
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+++ b/reference/torch_chain_matmul.html
@@ -1,8 +1,5 @@
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Chain_matmul</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Chain_matmul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_chain_matmul.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>matrices</dt>
+
+
+<dl><dt id="arg-matrices">matrices<a class="anchor" aria-label="anchor" href="#arg-matrices"></a></dt>
 <dd><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Returns the matrix product of the \(N\) 2-D tensors. This product is efficiently computed
@@ -122,13 +106,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_chain_matmul</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, <span class="va">c</span>, <span class="va">d</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.9114  11.1095  -3.5044  14.9431  17.5463  -4.6954  -8.2067</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.2924   2.9680  -1.0878   3.3883   7.5599  -2.4557  -3.2995</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   5.2357  -3.2431   5.0502  -2.0051  -8.0577  -1.0972   1.1601</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -12.2407  23.0458   2.8975  -6.9677   3.9015   1.8938  -0.3433</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -7.4472  14.6260   3.6509  -2.8358   5.9263  -0.0407   1.2311</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -9.6405  -5.4312   9.8422  27.1031  12.3670   9.2524   6.2635</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,7} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_chalf.html b/reference/torch_chalf.html
new file mode 100644
index 0000000000..b522acdaf8
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@@ -0,0 +1,8 @@
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diff --git a/reference/torch_channel_shuffle.html b/reference/torch_channel_shuffle.html
index 4a6a755cd4..f7d5f5d4b7 100644
--- a/reference/torch_channel_shuffle.html
+++ b/reference/torch_channel_shuffle.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Channel_shuffle"><title>Channel_shuffle — torch_channel_shuffle • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Channel_shuffle — torch_channel_shuffle"><meta property="og:description" content="Channel_shuffle"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Channel_shuffle</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Channel_shuffle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_channel_shuffle.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>(int) number of groups to divide channels in and rearrange.</p></dd>
 
 </dl></div>
@@ -122,41 +106,41 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7996 -0.4875</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1402  0.6430</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7662  0.9961</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.3459 -0.3866</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6013 -2.8596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6372  0.1704</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0192 -0.6689</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0240  1.2698</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8334  1.1602</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2836  0.8798</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1894  0.9790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8418  0.3206</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5182 -1.2047</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9831  0.0413</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1993 -0.6290</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7123 -0.5530</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4,2,2} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7996 -0.4875</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1402  0.6430</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7662  0.9961</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.3459 -0.3866</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8334  1.1602</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2836  0.8798</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1894  0.9790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8418  0.3206</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.6013 -2.8596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.6372  0.1704</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0192 -0.6689</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0240  1.2698</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.5182 -1.2047</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9831  0.0413</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1993 -0.6290</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7123 -0.5530</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4,2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -165,14 +149,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_channels_last_format.html b/reference/torch_channels_last_format.html
new file mode 100644
index 0000000000..eb6204681e
--- /dev/null
+++ b/reference/torch_channels_last_format.html
@@ -0,0 +1,8 @@
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diff --git a/reference/torch_cholesky.html b/reference/torch_cholesky.html
index 84feeb9dda..ca66808f3d 100644
--- a/reference/torch_cholesky.html
+++ b/reference/torch_cholesky.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Cholesky"><title>Cholesky — torch_cholesky • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cholesky — torch_cholesky"><meta property="og:description" content="Cholesky"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cholesky</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cholesky</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cholesky.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code>*</code> is zero or more
 batch dimensions consisting of symmetric positive-definite matrices.</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>(bool, optional) flag that indicates whether to return a
 upper or lower triangular matrix. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cholesky-input-upper-false-out-null-gt-tensor-">cholesky(input, upper=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cholesky-input-upper-false-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the Cholesky decomposition of a symmetric positive-definite
@@ -140,16 +124,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">l</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">l</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">a</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1e-03</span> <span class="co"># make symmetric positive-definite</span></span></span>
 <span class="r-in"><span><span class="va">l</span> <span class="op">=</span> <span class="fu">torch_cholesky</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">z</span> <span class="op">=</span> <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">l</span>, <span class="va">l</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="op">-</span><span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="torch_max.html">torch_max</a></span><span class="op">(</span><span class="fu"><a href="torch_abs.html">torch_abs</a></span><span class="op">(</span><span class="va">z</span> <span class="op">-</span> <span class="va">a</span><span class="op">)</span><span class="op">)</span> <span class="co"># Max non-zero</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +142,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/torch_cholesky_inverse.html b/reference/torch_cholesky_inverse.html
index ed4cd1300e..04aec14aa2 100644
--- a/reference/torch_cholesky_inverse.html
+++ b/reference/torch_cholesky_inverse.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cholesky_inverse</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cholesky_inverse</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cholesky_inverse.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cholesky-inverse-input-upper-false-out-null-gt-tensor-">cholesky_inverse(input, upper=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cholesky-inverse-input-upper-false-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the inverse of a symmetric positive-definite matrix \(A\) using its
@@ -127,18 +111,18 @@ <h2 id="cholesky-inverse-input-upper-false-out-null-gt-tensor-">cholesky_inverse
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">a</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1e-05</span> <span class="op">*</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">3</span><span class="op">)</span> <span class="co"># make symmetric positive definite</span></span></span>
 <span class="r-in"><span><span class="va">u</span> <span class="op">=</span> <span class="fu"><a href="torch_cholesky.html">torch_cholesky</a></span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
 <span class="r-in"><span><span class="fu">torch_cholesky_inverse</span><span class="op">(</span><span class="va">u</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span><span class="op">$</span><span class="fu">inverse</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -147,14 +131,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
 
-  
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   </body></html>
 
diff --git a/reference/torch_cholesky_solve.html b/reference/torch_cholesky_solve.html
index 366690d91a..f2277fe719 100644
--- a/reference/torch_cholesky_solve.html
+++ b/reference/torch_cholesky_solve.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cholesky_solve</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cholesky_solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cholesky_solve.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></dd>
 
 
-<dt>input2</dt>
+<dt id="arg-input-">input2<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cholesky-solve-input-input-upper-false-out-null-gt-tensor-">cholesky_solve(input, input2, upper=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cholesky-solve-input-input-upper-false-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Solves a linear system of equations with a positive semidefinite
@@ -143,13 +127,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">inverse</span><span class="op">(</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.7628  2.1661</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8217  0.5086</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -6.3133 -1.1261</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2588 -0.0557</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1410  0.0636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3984  0.9358</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +142,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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 </nav><div class="container template-reference-topic">
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-      <img src="" class="logo" alt=""><h1>Chunk</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Chunk</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_chunk.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to split</p></dd>
 
 
-<dt>chunks</dt>
+<dt id="arg-chunks">chunks<a class="anchor" aria-label="anchor" href="#arg-chunks"></a></dt>
 <dd><p>(int) number of chunks to return</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension along which to split the tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="chunk-input-chunks-dim-gt-list-of-tensors-">chunk(input, chunks, dim=0) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a></h2>
-    
+
 
 
 <p>Splits a tensor into a specific number of chunks. Each chunk is a view of
@@ -118,7 +102,7 @@ <h2 id="chunk-input-chunks-dim-gt-list-of-tensors-">chunk(input, chunks, dim=0)
 <code>dim</code> is not divisible by <code>chunks</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -127,14 +111,14 @@ <h2 id="chunk-input-chunks-dim-gt-list-of-tensors-">chunk(input, chunks, dim=0)
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_clamp.html b/reference/torch_clamp.html
index a7846bbd61..d411e2fb45 100644
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@@ -1,8 +1,5 @@
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-examples" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Examples</button>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Clamp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Clamp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_clamp.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>min</dt>
+<dt id="arg-min">min<a class="anchor" aria-label="anchor" href="#arg-min"></a></dt>
 <dd><p>(Number) lower-bound of the range to be clamped to</p></dd>
 
 
-<dt>max</dt>
+<dt id="arg-max">max<a class="anchor" aria-label="anchor" href="#arg-max"></a></dt>
 <dd><p>(Number) upper-bound of the range to be clamped to</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="clamp-input-min-max-out-null-gt-tensor-">clamp(input, min, max, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clamp-input-min-max-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Clamp all elements in <code>input</code> into the range <code>[</code> <code>min</code>, <code>max</code> <code>]</code> and return
@@ -127,7 +111,7 @@ <h2 id="clamp-input-min-max-out-null-gt-tensor-">clamp(input, min, max, out=NULL
     </div>
     <div class="section level2">
     <h2 id="clamp-input-min-out-null-gt-tensor-">clamp(input, *, min, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clamp-input-min-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Clamps all elements in <code>input</code> to be larger or equal <code>min</code>.</p>
@@ -136,7 +120,7 @@ <h2 id="clamp-input-min-out-null-gt-tensor-">clamp(input, *, min, out=NULL) -&gt
     </div>
     <div class="section level2">
     <h2 id="clamp-input-max-out-null-gt-tensor-">clamp(input, *, max, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clamp-input-max-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Clamps all elements in <code>input</code> to be smaller or equal <code>max</code>.</p>
@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_clamp</span><span class="op">(</span><span class="va">a</span>, max<span class="op">=</span><span class="fl">0.5</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3931</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7677</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.5000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3415</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5366</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3665</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -179,14 +163,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_clip.html b/reference/torch_clip.html
index c14b47ff39..7f46c2de67 100644
--- a/reference/torch_clip.html
+++ b/reference/torch_clip.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Clip"><title>Clip — torch_clip • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Clip — torch_clip"><meta property="og:description" content="Clip"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Clip — torch_clip • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Clip — torch_clip"><meta name="description" content="Clip"><meta property="og:description" content="Clip"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   gtag('js', new Date());
@@ -10,77 +7,62 @@
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Clip</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Clip</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_clip.Rd</code></div>
     </div>
 
@@ -95,27 +77,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>min</dt>
+<dt id="arg-min">min<a class="anchor" aria-label="anchor" href="#arg-min"></a></dt>
 <dd><p>(Number) lower-bound of the range to be clamped to</p></dd>
 
 
-<dt>max</dt>
+<dt id="arg-max">max<a class="anchor" aria-label="anchor" href="#arg-max"></a></dt>
 <dd><p>(Number) upper-bound of the range to be clamped to</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="clip-input-min-max-out-none-gt-tensor-">clip(input, min, max, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clip-input-min-max-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_clamp.html">torch_clamp()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -124,14 +108,14 @@ <h2 id="clip-input-min-max-out-none-gt-tensor-">clip(input, min, max, *, out=Non
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/torch_clone.html b/reference/torch_clone.html
index 7186a79ec6..7d28243f4d 100644
--- a/reference/torch_clone.html
+++ b/reference/torch_clone.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Clone"><title>Clone — torch_clone • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Clone — torch_clone"><meta property="og:description" content="Clone"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Clone</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Clone</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_clone.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>a torch memory format. see <code><a href="torch_memory_format.html">torch_preserve_format()</a></code>.</p></dd>
 
 </dl></div>
@@ -111,13 +95,13 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="clone-input-memory-format-torch-preserve-format-gt-tensor-">clone(input, *, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#clone-input-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a copy of <code>input</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -126,14 +110,14 @@ <h2 id="clone-input-memory-format-torch-preserve-format-gt-tensor-">clone(input,
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
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diff --git a/reference/torch_combinations.html b/reference/torch_combinations.html
index 1de2c9d177..05b370f683 100644
--- a/reference/torch_combinations.html
+++ b/reference/torch_combinations.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Combinations"><title>Combinations — torch_combinations • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Combinations — torch_combinations"><meta property="og:description" content="Combinations"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Combinations</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Combinations</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_combinations.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) 1D vector.</p></dd>
 
 
-<dt>r</dt>
+<dt id="arg-r">r<a class="anchor" aria-label="anchor" href="#arg-r"></a></dt>
 <dd><p>(int, optional) number of elements to combine</p></dd>
 
 
-<dt>with_replacement</dt>
+<dt id="arg-with-replacement">with_replacement<a class="anchor" aria-label="anchor" href="#arg-with-replacement"></a></dt>
 <dd><p>(boolean, optional) whether to allow duplication in combination</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="combinations-input-r-with-replacement-false-gt-seq-">combinations(input, r=2, with_replacement=False) -&gt; seq <a class="anchor" aria-label="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a></h2>
-    
+
 
 
 <p>Compute combinations of length \(r\) of the given tensor. The behavior is similar to
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_complex.html b/reference/torch_complex.html
index 6dfd5016dd..05c25a4cdd 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Complex</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Complex</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_complex.Rd</code></div>
     </div>
 
@@ -95,18 +77,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>real</dt>
+
+
+<dl><dt id="arg-real">real<a class="anchor" aria-label="anchor" href="#arg-real"></a></dt>
 <dd><p>(Tensor) The real part of the complex tensor. Must be float or double.</p></dd>
 
 
-<dt>imag</dt>
+<dt id="arg-imag">imag<a class="anchor" aria-label="anchor" href="#arg-imag"></a></dt>
 <dd><p>(Tensor) The imaginary part of the complex tensor. Must be same dtype
 as <code>real</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="complex-real-imag-out-none-gt-tensor-">complex(real, imag, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#complex-real-imag-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Constructs a complex tensor with its real part equal to <code>real</code> and its
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_ComplexFloat</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_conj.html b/reference/torch_conj.html
index 24b68fced7..58fa23ecfa 100644
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@@ -1,8 +1,5 @@
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Conj — torch_conj • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conj — torch_conj"><meta name="description" content="Conj"><meta property="og:description" content="Conj"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conj</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conj</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conj.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conj-input-gt-tensor-">conj(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conj-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise conjugate of the given <code>input</code> tensor.</p>
@@ -113,13 +97,13 @@ <h2 id="conj-input-gt-tensor-">conj(input) -&gt; Tensor <a class="anchor" aria-l
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu">torch_conj</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -128,14 +112,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
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diff --git a/reference/torch_contiguous_format.html b/reference/torch_contiguous_format.html
new file mode 100644
index 0000000000..eb6204681e
--- /dev/null
+++ b/reference/torch_contiguous_format.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
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diff --git a/reference/torch_conv1d.html b/reference/torch_conv1d.html
index 5689891d1b..f36bd2480e 100644
--- a/reference/torch_conv1d.html
+++ b/reference/torch_conv1d.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Conv1d"><title>Conv1d — torch_conv1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv1d — torch_conv1d"><meta property="og:description" content="Conv1d"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Conv1d — torch_conv1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv1d — torch_conv1d"><meta name="description" content="Conv1d"><meta property="og:description" content="Conv1d"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conv1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv1d.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-">conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 1D convolution over an input signal composed of several input
@@ -151,40 +135,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 6 -8.3169e+00 -2.1014e+00 -5.1744e+00  4.7219e+00 -7.2245e+00 -3.7793e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.3550e+00  3.5967e-01 -5.9571e+00  1.9716e+00 -2.8001e+00  9.2921e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.9458e+00  7.0693e+00 -2.3263e+00 -4.4576e+00 -3.0706e+00 -3.6137e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   7.6318e+00  7.6674e+00  6.7905e-01 -1.0191e+00  2.8939e+00  3.7213e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3.5952e+00 -1.5817e+01  9.1722e+00  8.3184e-01  1.4853e+00 -1.3252e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.0351e+00  4.5588e-01  4.6643e+00 -5.1794e+00  2.4778e+00 -3.6634e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -7.6635e+00  2.3136e+00  1.3166e+00  1.3124e+00 -3.7686e+00 -1.1524e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.7977e+00  3.8666e+00  7.2739e+00 -1.3060e+01 -1.6958e+00  9.6115e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.3698e+01 -6.0547e+00  1.0270e+01  1.7839e+01 -4.4191e+00 -5.6308e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.7233e+00 -2.1186e+00 -1.1272e+00 -4.6070e+00 -1.3363e+00  1.4993e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.1939e+00 -1.2392e+01  3.6945e+00  7.5041e+00  7.6516e+00 -7.6661e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.6416e+00 -8.4228e+00 -2.2767e+00  1.5158e+00  6.5058e+00 -7.3223e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9778e+00 -2.0790e+00  1.6602e+00  9.3931e+00  2.3396e+00  1.5080e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   5.6369e+00 -4.9868e+00  8.2276e+00 -1.1036e+01  3.5321e+00  2.3121e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8445e+00 -9.2048e-01 -4.2860e+00 -5.7858e+00 -2.1423e+00  1.3416e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -8.5335e-01 -9.2716e+00 -4.5451e+00 -5.0947e+00  3.1179e-01  5.0687e-01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.1218e+00  3.3808e+00  6.7575e-02  1.9638e+00 -6.4383e-02  4.6061e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -9.4345e+00 -1.2909e+01  4.5463e+00  5.7057e+00  7.9532e+00  1.0342e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.0564e+01  3.4541e+00  5.7038e-01 -1.0550e+01  6.2890e+00  2.9685e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   8.4836e+00  1.0381e+01 -4.9445e+00 -6.4267e-01 -8.5755e+00  3.4221e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   7.6088e-01  8.3236e+00 -9.5267e+00  4.5688e-01 -2.2607e+00  7.8716e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   7.3898e+00 -4.1470e+00  1.9449e+00 -1.8083e+00  1.9612e-01  1.6873e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.0546e+00  3.4497e+00 -3.6739e+00  3.2975e+00 -4.9923e+00  6.4785e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0131e+01  1.1753e+00 -9.1447e+00 -6.6979e+00  2.5119e+00  3.9920e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.9422e+00  1.0326e+01 -1.5198e+00 -1.6618e+00 -1.2997e+01 -1.6538e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.5410e+00  1.7281e+00  2.8887e+00  7.1734e+00 -1.5699e-02  1.3118e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.5259e+00 -5.7793e+00  1.5204e+00 -4.8937e-04 -1.0205e+00 -4.2960e+00</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.3303e+00  6.4505e+00 -5.2870e+00  6.7430e-01 -1.5892e+00  1.6843e+01</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.7452e+00 -3.9635e+00 -6.0927e+00  4.8368e+00 -1.0148e+00 -1.2588e+01</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 8  -4.2799  -4.4055  -7.6185  -3.6413  -7.5131   4.4503  12.4302  -0.6246</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.7820  11.2815  15.3058   2.5497   7.3234   0.0027 -10.2557   6.1521</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.7319  -7.6383   0.4122  -3.1282   0.5867 -13.2591  -3.8448  -5.2541</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -4.5234   5.8615   5.2040   2.6400   1.5504  -5.1288   7.8689   9.1657</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.5921  -9.1527  -7.8412   6.6396   4.7601  -1.1723  -6.4607  -3.6257</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.3255  -7.3895   0.9591  -0.0180  -4.1197   6.3202   6.3130   8.4903</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.0993   6.3749   8.7024  12.7569   6.9660  -6.5842   3.9414   0.4054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    7.9684   7.1598  -9.6735  -0.4639  -6.5569   2.3603   9.3425   2.4212</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -7.4006  -0.8673 -11.1602   3.0148   5.2737   1.9895  18.0035  -7.5815</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.1510   0.0882  -0.8894   2.7853  -6.1612  -4.3666  15.8519   6.8035</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.0551  -5.5998  -5.5718  -5.0208  -2.6086  -1.5798   3.4159   3.6958</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.9326   3.3454   4.7694 -20.7927  -4.5259  -8.8677  -6.1775   0.2884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -9.2756 -11.0536  10.1570  -8.5320   7.1997   7.5796 -13.5387   9.1202</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    7.6942   2.6654   7.1931  -2.1197  -5.2211 -10.6524   1.2795  -2.3806</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.9804   1.1636 -11.7477  -0.4564   5.4912   4.7557   7.8871  -5.5879</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.3042  -0.0151  -3.8278  -9.6159 -13.3710  -5.5168   3.3523   4.1783</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.7552 -10.0832  -3.2173   2.9196   7.2266   9.8423   1.2781  -2.5732</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.1371  -0.0244   7.5641  -0.5036   4.0956  -6.5192  -2.8360   7.7989</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.2528   9.6603  10.6675   8.4282  -6.9757 -14.2541  -8.1621  -6.9910</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    8.8926  -2.7399 -10.1791   0.7602   0.3328  -1.9903   2.8115  -7.6494</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.0896   0.4523  -3.8656  -1.9457  -2.5374  -2.1337   6.4756   1.8318</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.5806  -7.6214  -0.0251 -15.2107   0.9737  -5.4552  -1.9320   3.1738</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.2481   0.0856   7.8390   9.7375  -8.8718  -7.1358  -5.8276  -3.8133</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.9938 -11.1520   2.7522   5.5041  -8.3037  -6.4006  -7.6681  -6.7562</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -11.1665   5.2830  -4.3023  -1.4715   0.5730  -2.5748  -8.8762   6.7086</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.3044  -1.7767   2.1370   0.3335  -4.1531  -8.9707 -10.8596  -5.2838</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.3987  -4.4244  -1.9234  -7.0934   2.9753   3.3248   1.1382   3.2683</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   14.1857   9.1710   8.3437 -10.4215  -4.2963 -12.3506   5.2811   5.7495</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    8.5927   4.8413  -5.3993 -11.4106 -12.2129  -2.8460   5.5478   3.4733</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{20,33,48} ]</span>
 </code></pre></div>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conv2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv2d.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-">conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 2D convolution over an input image composed of several input
@@ -152,40 +136,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -6.2293  3.6368 -6.9950 -3.2092  4.0164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.4499  5.6002  5.9233  0.9858  4.2309</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.9799 -12.2515 -1.1408  5.2784  4.7481</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3.5944  0.3573  1.3755 -0.4545 -0.4302</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.4743  3.5464  2.9807  3.5504  3.6378</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.6762  -1.4722  -1.6436   4.5126  -2.3594</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -6.1267  -0.6607   0.5919   9.5704  -3.9356</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.7615   6.3741  -2.8814   1.8651   2.6326</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.0181   6.3555  10.1203  -8.3985  -4.0173</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.3111  -0.7174   2.8917  -3.1391  -8.9127</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -14.1458  -2.4284  -1.6412  -4.7466  -0.5833</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.7556  -2.9063 -13.3437  -1.0216   0.0556</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    8.1979  -9.5774  -2.3079  -9.8105  -6.1516</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.3297   1.0058   3.2910  -4.9661 -10.2056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.9389   7.1504  -4.6618   3.7112   0.9689</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    8.9800   1.7654   7.7636  -6.3208  -8.4194</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.8093   6.0707   9.6270  -3.9311   1.4963</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   11.6820  -7.1384 -13.1026   0.6878   2.1252</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.5556  -5.7388   4.2203  -0.1439   0.9164</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.1838  -2.2137  -5.6184  -1.2443   0.6594</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.0213  4.8348 -0.0753 -5.5559  2.1641</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   7.3397  5.3186 -0.5203 -10.4046 -2.4127</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.8926 -5.7183  7.3088  1.4437 -0.2859</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1150  1.8809 -4.1334  4.5544  1.0229</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.4993  0.7790 -2.1356  1.6472 -3.0782</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7132  0.6092 -5.2210  7.3686  1.3817</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.1427  3.6031 -8.0537  2.5274 -4.5634</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.1523  2.8623 -1.0692 -6.0115 -5.0826</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.1303 -2.6635  0.9652 -4.9996 -1.5039</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   4.5513 -6.7425  5.8636  0.2757 -5.1578</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -8.0488   0.3474   9.3235  -1.6900  -3.2958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -8.0323  -9.7287  -6.8925   4.5854  10.4326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   10.2315 -12.7851  -6.9631 -11.5428  -1.7736</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    1.9657   0.0843  -5.3749  -8.2050  -2.8523</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.6009   3.4971   2.5930   0.7100   5.0198</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.9208   7.2958   1.0516   2.1344   0.6837</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.1177   2.2499   3.0147  -1.1611  -3.3016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.3033  13.4223   2.4072  -9.5020   0.6424</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.1416  -0.7583  -7.2147   8.3988   3.6592</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.6494   1.9372  -3.8303  -5.4349  -1.8648</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,5,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.1366   2.1507  -1.2642   1.5051  -3.3853</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.0466 -6.5147  2.8607 -4.0111  1.6115</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,8,5,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -194,14 +178,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
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-  
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index 65ba4a7724..c954d17504 100644
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+++ b/reference/torch_conv3d.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Conv3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conv3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv3d.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-">conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 3D convolution over an input image composed of several input
@@ -152,7 +136,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> NULL</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -161,14 +145,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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index 7feb7752dd..0be00ba3c5 100644
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+
+      <h1>Conv_tbc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv_tbc.Rd</code></div>
     </div>
 
@@ -95,32 +77,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>NA bias of shape (\(\mbox{out\_channels}\))</p></dd>
 
 
-<dt>pad</dt>
+<dt id="arg-pad">pad<a class="anchor" aria-label="anchor" href="#arg-pad"></a></dt>
 <dd><p>NA number of timesteps to pad. Default: 0</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Applies a 1-dimensional sequence convolution over an input sequence.
 Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -129,14 +113,14 @@ <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_conv_transpose1d.html b/reference/torch_conv_transpose1d.html
index 94a55474b2..6711d6595d 100644
--- a/reference/torch_conv_transpose1d.html
+++ b/reference/torch_conv_transpose1d.html
@@ -1,8 +1,5 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Conv_transpose1d"><title>Conv_transpose1d — torch_conv_transpose1d • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Conv_transpose1d — torch_conv_transpose1d"><meta property="og:description" content="Conv_transpose1d"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Conv_transpose1d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conv_transpose1d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv_transpose1d.Rd</code></div>
     </div>
 
@@ -104,41 +86,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-">conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 1D transposed convolution operator over an input signal
@@ -156,40 +140,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 8  -4.1502   5.2662   1.2746  -3.9347   2.2134   7.8490  -1.4314   5.7363</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.8787  -3.6590  -5.1717  -6.2001  12.6397  13.7569   6.9527   0.8596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.2104   0.7274   0.4443  -5.9108   7.5095  -6.3658  -6.4068  13.9326</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.9152  -6.9704   7.5327   3.7642  -1.7677  -5.2399  15.3008  -8.7244</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    0.4521  -6.3098  -9.1746   6.6661  14.1740  15.3086  -8.1402   0.9600</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    2.2716   0.8837  -0.1830  -4.7564  14.5371  14.8455  -4.3501  -9.0601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.5410  11.1980   9.0446  -3.0310  -1.8131  15.7361 -12.3093  16.3265</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.2634   8.4169 -10.9854  -3.6416   5.5400   3.7850   5.8189  -1.2514</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    1.1178  11.4405  -1.1431  -8.9672  -6.1387  14.0108  -1.0153   5.5063</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    2.8625   4.9656   6.2678  -7.2279  -0.8833   5.9117   9.8635   0.9836</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    2.9008   9.2096  -5.7410   4.0162   1.7477   0.0787  -0.9875  14.4767</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    7.4777  -2.6968   7.8789  10.3786  -0.9959  15.2169  12.7871   1.1561</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -6.8383   8.0303   3.1514  11.9488  -1.3620  -9.9862   0.0746  -4.0376</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.2166   1.9659  -5.3781 -14.7103  -3.3162   4.7723   2.4020  -8.3331</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    7.2071   7.2636  12.6062  -0.7760   9.4554  -7.9184 -11.9570   5.5830</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    2.1695   6.4469   7.0870  -0.3204   2.3094  -6.7242  -1.6000   3.1596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    3.3520   4.1517  -2.1789   4.5343   2.5825   2.1492   1.1830  -4.4321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    0.0922  -1.3383  -4.0789   7.5349   0.3219   5.2433  -2.6360   3.7869</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.7933   3.3512  -4.5051  -2.1901  22.3008  18.8490   7.5580  -5.6750</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    7.3938 -11.1678  -2.0972  -5.7141 -11.4733  -7.5001   3.0933  -9.5548</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -9.2641 -12.2349   1.5176  -5.4936   4.9133  -1.5024 -10.7774 -11.2684</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    3.2107   1.9521  11.6881  10.0457  12.0771   5.5904   2.8542 -15.3766</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    3.3805  -0.6111  -3.0381   4.6090  -6.2199   7.1722  -3.4843  -3.2336</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.0108   6.0233   4.3482 -10.2952  -0.5250   1.0059   6.7320  12.2936</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    6.2232   0.8173   2.2507   4.4085   6.0984 -12.9619  -7.8572   1.1984</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    6.3426 -11.2770   4.4595  -5.8399  -1.1147  -5.2786 -11.4394  -7.1858</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.6621  -2.3163   4.1256  -9.2621   0.6303   0.6374  11.0537   8.0226</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -4.4188   2.5024   7.9690 -10.1976  -2.7284  -1.7297 -14.5485  -1.5143</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.2679  -4.1840   6.4311   6.7153  11.8356   8.8752   0.9989   1.3909</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  Columns 1 to 8  -2.7172  -5.5210   7.9374   3.0906  21.3580 -12.6189  -4.3258   5.3093</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.3885 -12.0791 -11.1986   7.9128   2.8482   1.6760  -3.1183   7.3039</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.4756  -7.9809  -6.5201  12.1861   7.5359  -6.6329  11.6449  -1.0432</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.4642   0.9392   2.3865  -3.0983  -3.7857  -5.9992  -3.8601 -10.1172</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.1430   0.7860  -7.5414  -4.1419  -4.2946  -0.9784   3.2830   3.0879</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    9.2455  -0.1982   1.3323  -4.7387   5.7670   1.2136   3.1590  -2.7698</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.5656  -3.8974   2.4592  -3.4115  11.9612   2.9927  -4.9330  10.8563</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.8006   2.4989  -4.9836  10.0608  -0.5890   1.9800   8.0270   0.5575</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.9505   3.0838  -2.3031   7.4922   3.3415   4.7355   1.3100   6.0895</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.1837  -7.9399   1.3173   3.6236   0.7248  -6.4314  -0.7245  -9.9812</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.5216  -1.5727   1.0716  -8.4569  -6.8553  -6.1901   1.4440   4.1978</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.6952  -8.3790  -6.1057 -13.4262  -2.9027  -8.2298   7.7478  -5.3998</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.6673  -4.3327  -0.4105   3.7569 -10.2672   4.3275  17.1337  15.0232</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.9419  -4.5694 -11.3853  12.0499   3.9334  -5.0898  -1.2279   0.6043</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -6.8327  -3.8372   1.8230 -21.0385  -7.1487   1.6563  -4.2593  -0.3652</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.9802   1.3503   3.7497   0.7901   2.9292   1.3497   7.8661  -0.7266</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.8863  -7.5690   4.4432 -14.6916   5.3799 -15.6941 -14.4122 -15.9588</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -4.2796  -2.7611  -6.4002   4.9127 -12.0253  -4.3114  -5.5047  -1.5682</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.5966  -1.8511  -7.2233  -8.8994   3.2151  -0.4425  -8.4078  -1.6743</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.8360  -0.9185  -5.6618  24.6701   2.6244  -3.7442   7.8791 -10.1859</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.5336   0.0708   2.7975   5.0025  -4.7435   2.2739   5.3512  -0.6133</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    3.8462  -2.4170  14.4165  -5.7716   0.6435  -3.8622   2.6614  -0.1407</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.0673  -2.9854   3.5136  11.7400  11.0300  -6.6886  -7.5463   7.8790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.8618  -6.4786  -1.9332  -5.9230  -0.6521  -9.2760  17.0369  10.8966</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.3522  -4.3344  11.2488   4.6717   0.6802   2.8945   3.6235 -13.5183</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -1.6061   3.7639  -4.4445  -6.9303   8.4169   1.2716  -0.9037   6.4759</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.1588  -0.3700  -5.0320  -6.8052   8.9850  -7.2337 -15.6611   2.8269</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.8823   0.5136  17.5939   3.1880  13.8826  20.2181  -2.5064   2.9825</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    6.7815   0.2294  -6.3192   7.3882  -2.7959   5.7683 -16.8174   3.9043</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{20,33,54} ]</span>
 </code></pre></div>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv_transpose2d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conv_transpose2d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv_transpose2d.Rd</code></div>
     </div>
 
@@ -104,41 +86,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-">conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 2D transposed convolution operator over an input image
@@ -157,40 +141,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9863 -2.1015 -0.7686  4.6569  4.3497</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.9922  3.4184  0.0460 -0.6890  7.4952</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   5.5314 -1.1805 -0.0253  5.8615 -2.7114</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.1113  3.0726  7.1237 -2.0755 -3.5216</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   4.6665 -5.3079 -2.6928 -0.7921  1.6514</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.9030   2.3719   3.2671  -6.3717   4.9661</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -9.3490  -2.2151   2.7751  -1.1339   1.3596</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.3151   2.1332  11.8538  -1.7507  -3.1123</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.8214  -5.5401   5.5321  -2.6120   4.0258</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -7.6614   4.7752   2.8485   7.5207  -3.6628</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.4506 -14.0902  0.0557  9.6182  1.5526</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7919  0.8676  4.5834  4.1394 -0.5979</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.8559  2.2970  4.3644 -14.7250 -1.2473</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.2405 -2.2707 -2.3420  0.0843  3.2389</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.5088 -0.2471  2.9301  6.5253  4.5815</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    5.2193   2.6121  -1.2951   5.8279 -10.5490</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.0212   3.0566   5.3418  -0.5528 -19.0809</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.4759   5.2707  -1.3049 -11.1098  -0.6705</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.8664   7.7155  -7.3916  13.1146   3.6660</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    3.2671  -6.9516   8.2204   4.8681  -4.0088</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -8.1272  -3.7910   1.7024  10.5755   1.1526</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    5.3352   0.1813   7.7843   1.7031   2.8132</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    5.1791  -4.5619   4.4660  -2.8693   7.4604</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    2.2076   1.4190  -2.2263  -2.5487  -3.6389</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    2.8728  -2.9609   4.4764  -3.8207   4.7798</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -8.2957   5.5987   3.3467  -3.6443   1.0645</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    8.2492  -3.3375  -6.9863  -6.0146  -8.4941</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    3.4540  -3.0300  -5.0501   5.6115   1.7198</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    7.3158  -2.3231   0.1430  -0.7191  10.1638</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -0.5320  -0.4238  -2.4003   2.9976   3.9288</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    7.5384   5.1471   3.5030  -6.6308   2.5658</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -3.0810  -7.5815  -5.2492   8.1604  -2.4829</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    4.6761  -3.1848   3.4074   0.4728  -0.2657</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -5.8688  13.5493   3.0594 -13.3597   0.1405</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   -2.6346  -4.1050   1.9900   6.8615   1.5911</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    8.2648   3.0973  10.2399   3.8716  -6.8306</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.4585   3.1623  -1.6594   3.6279   1.1162</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    2.4186  -4.9536   4.4081  -3.1237  -8.9828</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   -7.3744  -2.3638   0.2175   8.9855   1.6173</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    1.3701   3.9888   3.5332   2.9809   0.5901</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,5,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.4595 -6.9167 -9.4588  4.4856  3.8772</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.8535 -3.2569  7.7058 -1.4514 -1.5325</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,8,5,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -199,14 +183,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 2133a9abbf..87734e8823 100644
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+++ b/reference/torch_conv_transpose3d.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Conv_transpose3d</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Conv_transpose3d</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_conv_transpose3d.Rd</code></div>
     </div>
 
@@ -104,41 +86,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></dd>
 
 
-<dt>bias</dt>
+<dt id="arg-bias">bias<a class="anchor" aria-label="anchor" href="#arg-bias"></a></dt>
 <dd><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></dd>
 
 
-<dt>padding</dt>
+<dt id="arg-padding">padding<a class="anchor" aria-label="anchor" href="#arg-padding"></a></dt>
 <dd><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></dd>
 
 
-<dt>output_padding</dt>
+<dt id="arg-output-padding">output_padding<a class="anchor" aria-label="anchor" href="#arg-output-padding"></a></dt>
 <dd><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></dd>
 
 
-<dt>groups</dt>
+<dt id="arg-groups">groups<a class="anchor" aria-label="anchor" href="#arg-groups"></a></dt>
 <dd><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></dd>
 
 
-<dt>dilation</dt>
+<dt id="arg-dilation">dilation<a class="anchor" aria-label="anchor" href="#arg-dilation"></a></dt>
 <dd><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-">conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Applies a 3D transposed convolution operator over an input image
@@ -149,15 +133,15 @@ <h2 id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-gr
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">inputs</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">16</span>, <span class="fl">50</span>, <span class="fl">10</span>, <span class="fl">20</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">weights</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">16</span>, <span class="fl">33</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d</a></span><span class="op">(</span><span class="va">inputs</span>, <span class="va">weights</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -166,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/torch_cos.html b/reference/torch_cos.html
index de1f17ebef..a88560cbe9 100644
--- a/reference/torch_cos.html
+++ b/reference/torch_cos.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Cos"><title>Cos — torch_cos • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cos — torch_cos"><meta property="og:description" content="Cos"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cos — torch_cos • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cos — torch_cos"><meta name="description" content="Cos"><meta property="og:description" content="Cos"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cos</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cos</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cos.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cos-input-out-null-gt-tensor-">cos(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cos-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the cosine  of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_cos</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5691</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9922</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6951</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9991</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9324</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9880</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_cosh.html b/reference/torch_cosh.html
index af69d865da..1fd3f83861 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cosh — torch_cosh • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cosh — torch_cosh"><meta name="description" content="Cosh"><meta property="og:description" content="Cosh"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cosh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cosh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cosh.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cosh-input-out-null-gt-tensor-">cosh(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cosh-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the hyperbolic cosine  of the elements of
@@ -120,14 +104,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_cosh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1358</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.7650</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0465</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.4691</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.8436</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3.7721</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2416</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0714</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
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     </nav></aside></div>
 
 
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_cosine_similarity.html b/reference/torch_cosine_similarity.html
index 4504f3b9aa..05b048a264 100644
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+++ b/reference/torch_cosine_similarity.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Cosine_similarity"><title>Cosine_similarity — torch_cosine_similarity • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cosine_similarity — torch_cosine_similarity"><meta property="og:description" content="Cosine_similarity"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cosine_similarity</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cosine_similarity</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cosine_similarity.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x1</dt>
+
+
+<dl><dt id="arg-x-">x1<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) First input.</p></dd>
 
 
-<dt>x2</dt>
+<dt id="arg-x-">x2<a class="anchor" aria-label="anchor" href="#arg-x-"></a></dt>
 <dd><p>(Tensor) Second input (of size matching x1).</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) Dimension of vectors. Default: 1</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cosine-similarity-x-x-dim-eps-e-gt-tensor-">cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns cosine similarity between x1 and x2, computed along dim.</p>
@@ -132,41 +116,41 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">output</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0305</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0182</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0420</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1624</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0298</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1531</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0605</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0160</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0241</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0530</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0665</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0217</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0156</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1495</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> -0.0518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0479</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0701</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0126</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0778</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0188</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0288</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0711</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0695</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0328</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1419</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0098</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1353</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0055</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1350</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0106</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0236</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0152</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0359</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0339</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0344</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0024</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0932</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0735</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0522</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0258</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0779</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1295</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0928</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0746</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0432</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0355</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0251</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0303</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0159</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1192</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0507</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1229</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0884</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0657</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{100} ]</span>
 </code></pre></div>
     </div>
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index 3b8c9cff11..b302b7a606 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Count_nonzero</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Count_nonzero</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_count_nonzero.Rd</code></div>
     </div>
 
@@ -95,18 +77,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints, optional) Dim or tuple of dims along which
 to count non-zeros.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="count-nonzero-input-dim-none-gt-tensor-">count_nonzero(input, dim=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#count-nonzero-input-dim-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Counts the number of non-zero values in the tensor <code>input</code> along the given <code>dim</code>.
@@ -124,13 +108,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_count_nonzero</span><span class="op">(</span><span class="va">x</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 937285e6f8..44c679fd97 100644
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cross</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cross</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cross.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) the dimension to take the cross-product in.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cross-input-other-dim-out-null-gt-tensor-">cross(input, other, dim=-1, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cross-input-other-dim-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the cross product of vectors in dimension <code>dim</code> of <code>input</code>
@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_cross</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0327  0.1124 -0.1158</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0779  0.3301 -0.3027</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3833 -0.0304 -0.8002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6250  0.9997  0.1767</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0034 -0.4132 -3.8712</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6889  1.9374  2.2741</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7125 -0.7639 -0.0354</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4060  0.7154  1.5142</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_cummax.html b/reference/torch_cummax.html
index be3a8106d6..605899533a 100644
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+++ b/reference/torch_cummax.html
@@ -1,8 +1,5 @@
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+    <li><a class="dropdown-item" href="../articles/loading-data.html">Loading Data</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cummax</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cummax</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cummax.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to do the operation over</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cummax-input-dim-gt-tensor-longtensor-">cummax(input, dim) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#cummax-input-dim-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative maximum of
@@ -126,35 +110,35 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2996</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0764</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4207</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5863</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5016</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{10} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_cummin.html b/reference/torch_cummin.html
index 7782cf296d..6e8825a29e 100644
--- a/reference/torch_cummin.html
+++ b/reference/torch_cummin.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Cummin"><title>Cummin — torch_cummin • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cummin — torch_cummin"><meta property="og:description" content="Cummin"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
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+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cummin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cummin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cummin.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to do the operation over</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cummin-input-dim-gt-tensor-longtensor-">cummin(input, dim) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#cummin-input-dim-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative minimum of
@@ -126,36 +110,35 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.01 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5115</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5115</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5115</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5115</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5115</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5.5115</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -120.6017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -120.6017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -120.6017</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -120.6017</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1865</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1865</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5402</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  6</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{10} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -164,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_cumprod.html b/reference/torch_cumprod.html
index f76fb64670..11fbde172a 100644
--- a/reference/torch_cumprod.html
+++ b/reference/torch_cumprod.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Cumprod</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cumprod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cumprod.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to do the operation over</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cumprod-input-dim-out-null-dtype-null-gt-tensor-">cumprod(input, dim, out=NULL, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cumprod-input-dim-out-null-dtype-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the cumulative product of elements of <code>input</code> in the dimension
@@ -130,20 +114,20 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_cumprod</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7135</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2467</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4431</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2452</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2581</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3817</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2863</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4371</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5322</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1767</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6083</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3658</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3418</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4265</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0020</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0025</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0005</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0006</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0005</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0005</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +136,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_cumsum.html b/reference/torch_cumsum.html
index 9a988f840a..aa46ed3862 100644
--- a/reference/torch_cumsum.html
+++ b/reference/torch_cumsum.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Cumsum"><title>Cumsum — torch_cumsum • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cumsum — torch_cumsum"><meta property="og:description" content="Cumsum"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cumsum — torch_cumsum • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Cumsum — torch_cumsum"><meta name="description" content="Cumsum"><meta property="og:description" content="Cumsum"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cumsum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Cumsum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_cumsum.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to do the operation over</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="cumsum-input-dim-out-null-dtype-null-gt-tensor-">cumsum(input, dim, out=NULL, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#cumsum-input-dim-out-null-dtype-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the cumulative sum of elements of <code>input</code> in the dimension
@@ -130,20 +114,20 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_cumsum</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7570</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2946</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2459</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1427</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1599</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7696</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.9541</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5709</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.4159</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2499</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6899</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.9677</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.2579</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.8257</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.3672</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0859</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2341</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0627</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1228</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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index d4f37befa0..170d382b04 100644
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-      <img src="" class="logo" alt=""><h1>Deg2rad</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Deg2rad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_deg2rad.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="deg-rad-input-out-none-gt-tensor-">deg2rad(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#deg-rad-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with each of the elements of <code>input</code>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -131,14 +115,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_dequantize.html b/reference/torch_dequantize.html
index 2dda3d656e..c56981eb4d 100644
--- a/reference/torch_dequantize.html
+++ b/reference/torch_dequantize.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Dequantize"><title>Dequantize — torch_dequantize • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Dequantize — torch_dequantize"><meta property="og:description" content="Dequantize"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Dequantize</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Dequantize</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_dequantize.Rd</code></div>
     </div>
 
@@ -95,26 +77,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor</dt>
+
+
+<dl><dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>(Tensor) A quantized Tensor or a list oof quantized tensors</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="dequantize-tensor-gt-tensor-">dequantize(tensor) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dequantize-tensor-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns an fp32 Tensor by dequantizing a quantized Tensor</p>
     </div>
     <div class="section level2">
     <h2 id="dequantize-tensors-gt-sequence-of-tensors-">dequantize(tensors) -&gt; sequence of Tensors <a class="anchor" aria-label="anchor" href="#dequantize-tensors-gt-sequence-of-tensors-"></a></h2>
-    
+
 
 
 <p>Given a list of quantized Tensors, dequantize them and return a list of fp32 Tensors</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -123,14 +107,14 @@ <h2 id="dequantize-tensors-gt-sequence-of-tensors-">dequantize(tensors) -&gt; se
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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index 790433d636..14e2caee9b 100644
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Det</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Det</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_det.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></dd>
 
 </dl></div>
@@ -108,7 +92,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="det-input-gt-tensor-">det(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#det-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Calculates determinant of a square matrix or batches of square matrices.</p>
@@ -125,13 +109,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">A</span><span class="op">$</span><span class="fu">det</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3.3801</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1298</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6516</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0722</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1292</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_device.html b/reference/torch_device.html
index aef96b8f8d..897b191fcb 100644
--- a/reference/torch_device.html
+++ b/reference/torch_device.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A torch_device  is an object representing the device on which a torch_tensor
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Create a Device object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/device.R" class="external-link"><code>R/device.R</code></a></small>
+
+      <h1>Create a Device object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/device.R" class="external-link"><code>R/device.R</code></a></small>
       <div class="d-none name"><code>torch_device.Rd</code></div>
     </div>
 
@@ -98,11 +80,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>type</dt>
+
+
+<dl><dt id="arg-type">type<a class="anchor" aria-label="anchor" href="#arg-type"></a></dt>
 <dd><p>(character) a device type <code>"cuda"</code> or <code>"cpu"</code></p></dd>
 
 
-<dt>index</dt>
+<dt id="arg-index">index<a class="anchor" aria-label="anchor" href="#arg-index"></a></dt>
 <dd><p>(integer) optional device ordinal for the device type.  If the device ordinal
 is not present, this object will always represent the current device for the device
 type, even after <code>torch_cuda_set_device()</code> is called; e.g., a <code>torch_tensor</code> constructed
@@ -128,7 +112,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_device(type='cpu', index=0) </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_diag.html b/reference/torch_diag.html
index cec87c5284..243232a054 100644
--- a/reference/torch_diag.html
+++ b/reference/torch_diag.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Diag</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Diag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_diag.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>diagonal</dt>
+<dt id="arg-diagonal">diagonal<a class="anchor" aria-label="anchor" href="#arg-diagonal"></a></dt>
 <dd><p>(int, optional) the diagonal to consider</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="diag-input-diagonal-out-null-gt-tensor-">diag(input, diagonal=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diag-input-diagonal-out-null-gt-tensor-"></a></h2>
-    
+
 
 <ul><li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
 with the elements of <code>input</code> as the diagonal.</p></li>
@@ -116,7 +100,7 @@ <h2 id="diag-input-diagonal-out-null-gt-tensor-">diag(input, diagonal=0, out=NUL
 <li><p>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -125,14 +109,14 @@ <h2 id="diag-input-diagonal-out-null-gt-tensor-">diag(input, diagonal=0, out=NUL
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
 
-  
+
+
 
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diff --git a/reference/torch_diag_embed.html b/reference/torch_diag_embed.html
index 6a45f9e911..9a22b726a6 100644
--- a/reference/torch_diag_embed.html
+++ b/reference/torch_diag_embed.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Diag_embed"><title>Diag_embed — torch_diag_embed • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Diag_embed — torch_diag_embed"><meta property="og:description" content="Diag_embed"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Diag_embed</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Diag_embed</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_diag_embed.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></dd>
 
 
-<dt>offset</dt>
+<dt id="arg-offset">offset<a class="anchor" aria-label="anchor" href="#arg-offset"></a></dt>
 <dd><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></dd>
 
 
-<dt>dim1</dt>
+<dt id="arg-dim-">dim1<a class="anchor" aria-label="anchor" href="#arg-dim-"></a></dt>
 <dd><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></dd>
 
 
-<dt>dim2</dt>
+<dt id="arg-dim-">dim2<a class="anchor" aria-label="anchor" href="#arg-dim-"></a></dt>
 <dd><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="diag-embed-input-offset-dim-dim-gt-tensor-">diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Creates a tensor whose diagonals of certain 2D planes (specified by
@@ -144,16 +128,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  1.4648  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000 -0.1176  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000 -0.1034  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000 -1.9597  0.0000  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000 -0.0131  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000 -0.6292  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.4405  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000 -0.2237  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000  0.2110</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000 -0.2680</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000 -0.1494</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.0000  0.0000  0.0000 -0.4696</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   0  0  0  0</span>
@@ -161,7 +145,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,2,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -170,14 +154,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_diagflat.html b/reference/torch_diagflat.html
index 5f358120f7..8980f8d673 100644
--- a/reference/torch_diagflat.html
+++ b/reference/torch_diagflat.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Diagflat — torch_diagflat • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Diagflat — torch_diagflat"><meta name="description" content="Diagflat"><meta property="og:description" content="Diagflat"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Diagflat</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Diagflat</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_diagflat.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>offset</dt>
+<dt id="arg-offset">offset<a class="anchor" aria-label="anchor" href="#arg-offset"></a></dt>
 <dd><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="diagflat-input-offset-gt-tensor-">diagflat(input, offset=0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diagflat-input-offset-gt-tensor-"></a></h2>
-    
+
 
 <ul><li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
 with the elements of <code>input</code> as the diagonal.</p></li>
@@ -129,14 +113,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_diagflat</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1981  0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.3182  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.0107  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.0000 -0.2125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7448  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000 -0.9938  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.6135  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.0000 -0.7422</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_diagonal.html b/reference/torch_diagonal.html
index f60fff490c..6815de8a03 100644
--- a/reference/torch_diagonal.html
+++ b/reference/torch_diagonal.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Diagonal"><title>Diagonal — torch_diagonal • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Diagonal — torch_diagonal"><meta property="og:description" content="Diagonal"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Diagonal — torch_diagonal • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Diagonal — torch_diagonal"><meta name="description" content="Diagonal"><meta property="og:description" content="Diagonal"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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-      <img src="" class="logo" alt=""><h1>Diagonal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Diagonal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_diagonal.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></dd>
 
 
-<dt>outdim</dt>
+<dt id="arg-outdim">outdim<a class="anchor" aria-label="anchor" href="#arg-outdim"></a></dt>
 <dd><p>dimension name if <code>self</code> is a named tensor.</p></dd>
 
 
-<dt>dim1</dt>
+<dt id="arg-dim-">dim1<a class="anchor" aria-label="anchor" href="#arg-dim-"></a></dt>
 <dd><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></dd>
 
 
-<dt>dim2</dt>
+<dt id="arg-dim-">dim2<a class="anchor" aria-label="anchor" href="#arg-dim-"></a></dt>
 <dd><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></dd>
 
 
-<dt>offset</dt>
+<dt id="arg-offset">offset<a class="anchor" aria-label="anchor" href="#arg-offset"></a></dt>
 <dd><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="diagonal-input-offset-dim-dim-gt-tensor-">diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a partial view of <code>input</code> with the its diagonal elements
@@ -145,24 +129,24 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4361</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5322</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.7543</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5912</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.4098</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1068</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1799</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7287</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.1374</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0536</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2354</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4116</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3274</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4074</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4167</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2596</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,2,1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -171,14 +155,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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index a837eb5069..d4323f2663 100644
--- a/reference/torch_diff.html
+++ b/reference/torch_diff.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-Higher-order differences are calculated by using torch_diff() recursively."><title>Computes the n-th forward difference along the given dimension. — torch_diff • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the n-th forward difference along the given dimension. — torch_diff"><meta property="og:description" content="The first-order differences are given by out[i] = input[i + 1] - input[i].
-Higher-order differences are calculated by using torch_diff() recursively."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Computes the n-th forward difference along the given dimension. — torch_diff • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Computes the n-th forward difference along the given dimension. — torch_diff"><meta name="description" content="The first-order differences are given by out[i] = input[i + 1] - input[i].
+Higher-order differences are calculated by using torch_diff() recursively."><meta property="og:description" content="The first-order differences are given by out[i] = input[i + 1] - input[i].
+Higher-order differences are calculated by using torch_diff() recursively."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Computes the n-th forward difference along the given dimension.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Computes the n-th forward difference along the given dimension.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_diff.Rd</code></div>
     </div>
 
@@ -98,25 +80,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the tensor to compute the differences on</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>the number of times to recursively compute the difference</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>the dimension to compute the difference along. Default is the last dimension.</p></dd>
 
 
-<dt>prepend</dt>
+<dt id="arg-prepend">prepend<a class="anchor" aria-label="anchor" href="#arg-prepend"></a></dt>
 <dd><p>values to prepend to input along dim before computing the
 difference. Their dimensions must be equivalent to that of input, and their
 shapes must match input’s shape except on dim.</p></dd>
 
 
-<dt>append</dt>
+<dt id="arg-append">append<a class="anchor" aria-label="anchor" href="#arg-append"></a></dt>
 <dd><p>values to append to input along dim before computing the
 difference. Their dimensions must be equivalent to that of input, and their
 shapes must match input’s shape except on dim.</p></dd>
@@ -147,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -156,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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+
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diff --git a/reference/torch_digamma.html b/reference/torch_digamma.html
index 49c4670440..4d0b855c4e 100644
--- a/reference/torch_digamma.html
+++ b/reference/torch_digamma.html
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-      <img src="" class="logo" alt=""><h1>Digamma</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Digamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_digamma.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compute the digamma function on</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="digamma-input-out-null-gt-tensor-">digamma(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#digamma-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the logarithmic derivative of the gamma function on <code>input</code>.</p>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Dist</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Dist</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_dist.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the Right-hand-side input tensor</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(float, optional) the norm to be computed</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="dist-input-other-p-gt-tensor-">dist(input, other, p=2) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dist-input-other-p-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the p-norm of (<code>input</code> - <code>other</code>)</p>
@@ -131,11 +115,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_dist</span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3.93348</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.93941</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -144,14 +128,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 20896d2287..7e118da09f 100644
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Div</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Div</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_div.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Number) the number to be divided to each element of <code>input</code></p></dd>
 
 
-<dt>rounding_mode</dt>
+<dt id="arg-rounding-mode">rounding_mode<a class="anchor" aria-label="anchor" href="#arg-rounding-mode"></a></dt>
 <dd><p>(str, optional) – Type of rounding applied to the result:</p><ul><li><p><code>NULL</code> - default behavior. Performs no rounding and, if both input and
 other are integer types, promotes the inputs to the default scalar type.
 Equivalent to true division in Python (the / operator) and NumPy’s
@@ -117,7 +101,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="div-input-other-out-null-gt-tensor-">div(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#div-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Divides each element of the input <code>input</code> with the scalar <code>other</code> and
@@ -139,7 +123,7 @@ <h2 id="div-input-other-out-null-gt-tensor-">div(input, other, out=NULL) -&gt; T
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>Integer division using div is deprecated, and in a future release div will
 perform true division like <code><a href="torch_true_divide.html">torch_true_divide()</a></code>.
@@ -172,14 +156,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_div</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1205  0.0517  0.4914 -1.1369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4111  0.4443  0.3243 -3.1527</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1085 -1.0652  1.2698 -0.9235</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.1766 -0.8320  0.8040 -4.1717</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8633  0.0851 -3.1823 -0.1029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4324 -0.7425  4.7351 -0.7904</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7002  0.9732  2.1361  1.3438</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2862 -2.4670  4.6140 -0.1961</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -188,14 +172,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_divide.html b/reference/torch_divide.html
index b691f64f04..b142192315 100644
--- a/reference/torch_divide.html
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Divide"><title>Divide — torch_divide • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Divide — torch_divide"><meta property="og:description" content="Divide"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Divide — torch_divide • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Divide — torch_divide"><meta name="description" content="Divide"><meta property="og:description" content="Divide"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Divide</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Divide</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_divide.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Number) the number to be divided to each element of <code>input</code></p></dd>
 
 
-<dt>rounding_mode</dt>
+<dt id="arg-rounding-mode">rounding_mode<a class="anchor" aria-label="anchor" href="#arg-rounding-mode"></a></dt>
 <dd><p>(str, optional) – Type of rounding applied to the result:</p><ul><li><p><code>NULL</code> - default behavior. Performs no rounding and, if both input and
 other are integer types, promotes the inputs to the default scalar type.
 Equivalent to true division in Python (the / operator) and NumPy’s
@@ -117,13 +101,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="divide-input-other-out-none-gt-tensor-">divide(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#divide-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_div.html">torch_div()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="divide-input-other-out-none-gt-tensor-">divide(input, other, *, out=None
 </div>
 
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index 7d02c1843c..e22718a0a8 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Dot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Dot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_dot.Rd</code></div>
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@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>tensor</dt>
+<dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>the other input tensor</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="dot-input-tensor-gt-tensor-">dot(input, tensor) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dot-input-tensor-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the dot product (inner product) of two tensors.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_double.html b/reference/torch_double.html
new file mode 100644
index 0000000000..b522acdaf8
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@@ -0,0 +1,8 @@
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index 37c646a698..fee440bbe9 100644
--- a/reference/torch_dstack.html
+++ b/reference/torch_dstack.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Dstack</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Dstack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_dstack.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="dstack-tensors-out-none-gt-tensor-">dstack(tensors, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#dstack-tensors-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Stack tensors in sequence depthwise (along third axis).</p>
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,1,2} ]</span>
 </code></pre></div>
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index 28319e30d0..9a6a55e75c 100644
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-      <img src="" class="logo" alt=""><h1>Eig</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_eig.Rd</code></div>
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@@ -91,23 +73,25 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></dd>
 
 
-<dt>eigenvectors</dt>
+<dt id="arg-eigenvectors">eigenvectors<a class="anchor" aria-label="anchor" href="#arg-eigenvectors"></a></dt>
 <dd><p>(bool) <code>TRUE</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-">eig(input, eigenvectors=False, out=NULL) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the eigenvalues and eigenvectors of a real square matrix.</p>
     </div>
 
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@@ -116,14 +100,14 @@ <h2 id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-">eig(input, eige
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diff --git a/reference/torch_einsum.html b/reference/torch_einsum.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Einsum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Einsum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_einsum.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>equation</dt>
+
+
+<dl><dt id="arg-equation">equation<a class="anchor" aria-label="anchor" href="#arg-equation"></a></dt>
 <dd><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></dd>
 
 
-<dt>tensors</dt>
+<dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(Tensor) The operands to compute the Einstein sum of.</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>(int) This function uses <a href="https://optimized-einsum.readthedocs.io/en/stable/" class="external-link">opt_einsum</a> to
 speed up computation or to consume less memory by optimizing contraction order. This optimization
 occurs when there are at least three inputs, since the order does not matter otherwise.
@@ -115,7 +99,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="einsum-equation-operands-gt-tensor-">einsum(equation, *operands) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#einsum-equation-operands-gt-tensor-"></a></h2>
-    
+
 
 
 <p>This function provides a way of computing multilinear expressions (i.e. sums of products) using the
@@ -147,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2 3 5 4</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -156,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_empty.html b/reference/torch_empty.html
index 3c1fd6be83..c3ec29f344 100644
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@@ -1,8 +1,5 @@
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Empty</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Empty</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_empty.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>a sequence of integers defining the shape of the output tensor.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>optional character vector naming each dimension.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-">empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with uninitialized data. The shape of the tensor is
@@ -147,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -156,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_empty_like.html b/reference/torch_empty_like.html
index a25ba6b8fc..3586a9ab19 100644
--- a/reference/torch_empty_like.html
+++ b/reference/torch_empty_like.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Empty_like"><title>Empty_like — torch_empty_like • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Empty_like — torch_empty_like"><meta property="og:description" content="Empty_like"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Empty_like — torch_empty_like • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Empty_like — torch_empty_like"><meta name="description" content="Empty_like"><meta property="og:description" content="Empty_like"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Empty_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Empty_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_empty_like.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns an uninitialized tensor with the same size as <code>input</code>.
@@ -148,7 +132,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -157,14 +141,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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diff --git a/reference/torch_empty_strided.html b/reference/torch_empty_strided.html
index f23330d9ff..d8ccc07bb1 100644
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+++ b/reference/torch_empty_strided.html
@@ -1,8 +1,5 @@
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Empty_strided</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_empty_strided.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>size</dt>
+
+
+<dl><dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(tuple of ints) the shape of the output tensor</p></dd>
 
 
-<dt>stride</dt>
+<dt id="arg-stride">stride<a class="anchor" aria-label="anchor" href="#arg-stride"></a></dt>
 <dd><p>(tuple of ints) the strides of the output tensor</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>pin_memory</dt>
+<dt id="arg-pin-memory">pin_memory<a class="anchor" aria-label="anchor" href="#arg-pin-memory"></a></dt>
 <dd><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-">empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
@@ -143,7 +127,7 @@ <h2 id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-gr
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>More than one element of the created tensor may refer to a single memory
 location. As a result, in-place operations (especially ones that are
@@ -163,7 +147,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -172,14 +156,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
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index 42e07900fa..371c2586af 100644
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@@ -1,8 +1,5 @@
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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-      <img src="" class="logo" alt=""><h1>Eq</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Eq</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_eq.Rd</code></div>
     </div>
 
@@ -95,18 +77,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare
 Must be a <code>ByteTensor</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="eq-input-other-out-null-gt-tensor-">eq(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#eq-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes element-wise equality</p>
@@ -128,7 +112,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_equal.html b/reference/torch_equal.html
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-      <img src="" class="logo" alt=""><h1>Equal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_equal.Rd</code></div>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>the other input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="equal-input-other-gt-bool-">equal(input, other) -&gt; bool <a class="anchor" aria-label="anchor" href="#equal-input-other-gt-bool-"></a></h2>
-    
+
 
 
 <p><code>TRUE</code> if two tensors have the same size and elements, <code>FALSE</code> otherwise.</p>
@@ -120,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] TRUE</span>
 </code></pre></div>
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 </div>
 
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diff --git a/reference/torch_erf.html b/reference/torch_erf.html
index 3f194902bc..0b264cf490 100644
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+
+      <h1>Erf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_erf.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="erf-input-out-null-gt-tensor-">erf(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#erf-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the error function of each element. The error function is defined as follows:</p>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
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diff --git a/reference/torch_erfc.html b/reference/torch_erfc.html
index 929df1a501..770602c627 100644
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-      <img src="" class="logo" alt=""><h1>Erfc</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Erfc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_erfc.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="erfc-input-out-null-gt-tensor-">erfc(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#erfc-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the complementary error function of each element of <code>input</code>.
@@ -124,7 +108,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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index 521d8019d5..427cc01b92 100644
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-      <img src="" class="logo" alt=""><h1>Erfinv</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Erfinv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_erfinv.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="erfinv-input-out-null-gt-tensor-">erfinv(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#erfinv-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the inverse error function of each element of <code>input</code>.
@@ -124,7 +108,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_exp.html b/reference/torch_exp.html
index 18d5f59e96..61a76f8aed 100644
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-      <img src="" class="logo" alt=""><h1>Exp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Exp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_exp.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="exp-input-out-null-gt-tensor-">exp(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#exp-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the exponential of the elements
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
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@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_exp2.html b/reference/torch_exp2.html
index 647d80fb9f..e4f2340ba0 100644
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-      <img src="" class="logo" alt=""><h1>Exp2</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Exp2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_exp2.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="exp-input-out-none-gt-tensor-">exp2(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#exp-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the base two exponential function of <code>input</code>.</p>
@@ -124,7 +108,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
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diff --git a/reference/torch_expm1.html b/reference/torch_expm1.html
index 13ebb0ebec..2b3cdd47b0 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Expm1</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Expm1</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_expm1.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="expm-input-out-null-gt-tensor-">expm1(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#expm-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the exponential of the elements minus 1
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_eye.html b/reference/torch_eye.html
index e5a3745e80..690a524285 100644
--- a/reference/torch_eye.html
+++ b/reference/torch_eye.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Eye"><title>Eye — torch_eye • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Eye — torch_eye"><meta property="og:description" content="Eye"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Eye — torch_eye • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Eye — torch_eye"><meta name="description" content="Eye"><meta property="og:description" content="Eye"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Eye</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Eye</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_eye.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>n</dt>
+
+
+<dl><dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) the number of rows</p></dd>
 
 
-<dt>m</dt>
+<dt id="arg-m">m<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>(int, optional) the number of columns with default being <code>n</code></p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p>
@@ -147,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -156,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_fft_fft.html b/reference/torch_fft_fft.html
index 95c5197a32..c5faa82b30 100644
--- a/reference/torch_fft_fft.html
+++ b/reference/torch_fft_fft.html
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-      <img src="" class="logo" alt=""><h1>Fft</h1>
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@@ -95,20 +77,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) Signal length. If given, the input will either be zero-padded
 or trimmed to this length before computing the FFT.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) The dimension along which to take the one dimensional FFT.</p></dd>
 
 
-<dt>norm</dt>
+<dt id="arg-norm">norm<a class="anchor" aria-label="anchor" href="#arg-norm"></a></dt>
 <dd><p>(str, optional) Normalization mode. For the forward transform, these
 correspond to:</p><ul><li><p>"forward" - normalize by 1/n</p></li>
 <li><p>"backward" - no normalization</p></li>
@@ -142,7 +126,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexFloatType{4} ]</span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>fftfreq</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>fftfreq</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_fft_fftfreq.Rd</code></div>
     </div>
 
@@ -102,30 +84,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>n</dt>
+
+
+<dl><dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(integer) – the FFT length</p></dd>
 
 
-<dt>d</dt>
+<dt id="arg-d">d<a class="anchor" aria-label="anchor" href="#arg-d"></a></dt>
 <dd><p>(float, optional) – the sampling length scale. The spacing between individual
 samples of the FFT input. The default assumes unit spacing, dividing that result by the
 actual spacing gives the result in physical frequency units.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(default: <code><a href="default_dtype.html">torch_get_default_dtype()</a></code>) the desired data type of returned tensor.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(default: <code><a href="torch_layout.html">torch_strided()</a></code>) the desired layout of returned tensor.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(default: <code>NULL</code>) the desired device of returned tensor.  Default:
 If <code>NULL</code>, uses the current device for the default tensor type.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(default: <code>FALSE</code>)  If autograd should record operations on the returned tensor.</p></dd>
 
 </dl></div>
@@ -155,7 +139,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -164,14 +148,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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index 1b2bc30fae..c764026daf 100644
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Ifft</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Ifft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_fft_ifft.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int, optional) – Signal length. If given, the input will either be
 zero-padded or trimmed to this length before computing the IFFT.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) – The dimension along which to take the one
 dimensional IFFT.</p></dd>
 
 
-<dt>norm</dt>
+<dt id="arg-norm">norm<a class="anchor" aria-label="anchor" href="#arg-norm"></a></dt>
 <dd><p>(str, optional) – Normalization mode. For the backward transform,
 these correspond to:</p><ul><li><p>"forward" - no normalization</p></li>
 <li><p>"backward" - normalize by 1/n</p></li>
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_fft_irfft.html b/reference/torch_fft_irfft.html
index b48fb382c8..cb467bd199 100644
--- a/reference/torch_fft_irfft.html
+++ b/reference/torch_fft_irfft.html
@@ -1,14 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Computes the inverse of torch_fft_rfft().
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Irfft — torch_fft_irfft • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Irfft — torch_fft_irfft"><meta name="description" content="Computes the inverse of torch_fft_rfft().
 Input is interpreted as a one-sided Hermitian signal in the Fourier domain,
 as produced by torch_fft_rfft(). By the Hermitian property, the output will
-be real-valued."><title>Irfft — torch_fft_irfft • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Irfft — torch_fft_irfft"><meta property="og:description" content="Computes the inverse of torch_fft_rfft().
+be real-valued."><meta property="og:description" content="Computes the inverse of torch_fft_rfft().
 Input is interpreted as a one-sided Hermitian signal in the Fourier domain,
 as produced by torch_fft_rfft(). By the Hermitian property, the output will
-be real-valued."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Irfft</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Irfft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_fft_irfft.Rd</code></div>
     </div>
 
@@ -104,22 +86,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor representing a half-Hermitian signal</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) Output signal length. This determines the length of the output
 signal. If given, the input will either be zero-padded or trimmed to this
 length before computing the real IFFT. Defaults to even output: <code>n=2*(input.size(dim) - 1)</code>.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) – The dimension along which to take the one
 dimensional real IFFT.</p></dd>
 
 
-<dt>norm</dt>
+<dt id="arg-norm">norm<a class="anchor" aria-label="anchor" href="#arg-norm"></a></dt>
 <dd><p>(str, optional) – Normalization mode. For the backward transform,
 these correspond to:</p><ul><li><p>"forward" - no normalization</p></li>
 <li><p>"backward" - normalize by 1/n</p></li>
@@ -162,7 +146,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -171,14 +155,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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+
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diff --git a/reference/torch_fft_rfft.html b/reference/torch_fft_rfft.html
index 632181566a..f707f0f5fc 100644
--- a/reference/torch_fft_rfft.html
+++ b/reference/torch_fft_rfft.html
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Rfft</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Rfft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_fft_rfft.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor)  the real input tensor</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) Signal length. If given, the input will either be zero-padded
 or trimmed to this length before computing the real FFT.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) – The dimension along which to take the one
 dimensional real FFT.</p></dd>
 
 
-<dt>norm</dt>
+<dt id="arg-norm">norm<a class="anchor" aria-label="anchor" href="#arg-norm"></a></dt>
 <dd><p>norm (str, optional) – Normalization mode. For the forward
 transform, these correspond to:</p><ul><li><p>"forward" - normalize by 1/n</p></li>
 <li><p>"backward" - no normalization</p></li>
@@ -140,7 +124,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexFloatType{3} ]</span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_finfo.html b/reference/torch_finfo.html
index ee49ff3d25..4850454611 100644
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@@ -1,10 +1,7 @@
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/type-info.R" class="external-link"><code>R/type-info.R</code></a></small>
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+
+      <h1>Fix</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_fix.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="fix-input-out-none-gt-tensor-">fix(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#fix-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_trunc.html">torch_trunc()</a></code></p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -116,14 +100,14 @@ <h2 id="fix-input-out-none-gt-tensor-">fix(input, *, out=None) -&gt; Tensor <a c
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_flatten.html b/reference/torch_flatten.html
index d05e978059..8ff7418a28 100644
--- a/reference/torch_flatten.html
+++ b/reference/torch_flatten.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Flatten — torch_flatten • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Flatten — torch_flatten"><meta name="description" content="Flatten"><meta property="og:description" content="Flatten"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Flatten</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Flatten</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_flatten.Rd</code></div>
     </div>
 
@@ -95,30 +77,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dims</dt>
+<dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
 <dd><p>if tensor is named you can pass the name of the dimensions to
 flatten</p></dd>
 
 
-<dt>start_dim</dt>
+<dt id="arg-start-dim">start_dim<a class="anchor" aria-label="anchor" href="#arg-start-dim"></a></dt>
 <dd><p>(int) the first dim to flatten</p></dd>
 
 
-<dt>end_dim</dt>
+<dt id="arg-end-dim">end_dim<a class="anchor" aria-label="anchor" href="#arg-end-dim"></a></dt>
 <dd><p>(int) the last dim to flatten</p></dd>
 
 
-<dt>out_dim</dt>
+<dt id="arg-out-dim">out_dim<a class="anchor" aria-label="anchor" href="#arg-out-dim"></a></dt>
 <dd><p>the name of the resulting dimension if a named tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="flatten-input-start-dim-end-dim-gt-tensor-">flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Flattens a contiguous range of dims in a tensor.</p>
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_flip.html b/reference/torch_flip.html
index c8f4d51443..64eb2f01de 100644
--- a/reference/torch_flip.html
+++ b/reference/torch_flip.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Flip</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Flip</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_flip.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dims</dt>
+<dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
 <dd><p>(a list or tuple) axis to flip on</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="flip-input-dims-gt-tensor-">flip(input, dims) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#flip-input-dims-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Reverse the order of a n-D tensor along given axis in dims.</p>
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index adf12be7cf..c254ce9d53 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Fliplr</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Fliplr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_fliplr.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) Must be at least 2-dimensional.</p></dd>
 
 </dl></div>
@@ -105,7 +89,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="fliplr-input-gt-tensor-">fliplr(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#fliplr-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Flip array in the left/right direction, returning a new tensor.</p>
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
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diff --git a/reference/torch_flipud.html b/reference/torch_flipud.html
index 78233cf8c5..c5bee8b875 100644
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-      <img src="" class="logo" alt=""><h1>Flipud</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Flipud</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_flipud.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) Must be at least 1-dimensional.</p></dd>
 
 </dl></div>
@@ -105,7 +89,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="flipud-input-gt-tensor-">flipud(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#flipud-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Flip array in the up/down direction, returning a new tensor.</p>
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_float.html b/reference/torch_float.html
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new file mode 100644
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diff --git a/reference/torch_float32.html b/reference/torch_float32.html
new file mode 100644
index 0000000000..b522acdaf8
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diff --git a/reference/torch_float64.html b/reference/torch_float64.html
new file mode 100644
index 0000000000..b522acdaf8
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+++ b/reference/torch_float64.html
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diff --git a/reference/torch_floor.html b/reference/torch_floor.html
index e9cda108aa..943e853113 100644
--- a/reference/torch_floor.html
+++ b/reference/torch_floor.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Floor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Floor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_floor.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="floor-input-out-null-gt-tensor-">floor(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#floor-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the floor of the elements of <code>input</code>,
@@ -120,14 +104,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_floor</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_floor_divide.html b/reference/torch_floor_divide.html
index 8ebdffeab9..0b22e6b848 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Floor_divide</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Floor_divide</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_floor_divide.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the numerator tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or Scalar) the denominator</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="floor-divide-input-other-out-null-gt-tensor-">floor_divide(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#floor-divide-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Return the division of the inputs rounded down to the nearest integer. See <code><a href="torch_div.html">torch_div</a></code>
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_fmod.html b/reference/torch_fmod.html
index 7195bb28a2..f3f5c85271 100644
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@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fmod</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Fmod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_fmod.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the dividend</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="fmod-input-other-out-null-gt-tensor-">fmod(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#fmod-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise remainder of division.</p>
@@ -131,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index dfd847e484..80239c42c2 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Frac</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Frac</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_frac.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="frac-input-out-null-gt-tensor-">frac(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#frac-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the fractional portion of each element in <code>input</code>.</p>
@@ -123,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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 <div class="row">
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-      <img src="" class="logo" alt=""><h1>Full</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Full</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_full.Rd</code></div>
     </div>
 
@@ -103,44 +85,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>size</dt>
+
+
+<dl><dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></dd>
 
 
-<dt>fill_value</dt>
+<dt id="arg-fill-value">fill_value<a class="anchor" aria-label="anchor" href="#arg-fill-value"></a></dt>
 <dd><p>NA the number to fill the output tensor with.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>optional names of the dimensions</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor of size <code>size</code> filled with <code>fill_value</code>.</p>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>In PyTorch 1.5 a bool or integral <code>fill_value</code> will produce a warning if
 <code>dtype</code> or <code>out</code> are not set.
@@ -161,7 +145,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -170,14 +154,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_full_like.html b/reference/torch_full_like.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Full_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Full_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_full_like.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>fill_value</dt>
+<dt id="arg-fill-value">fill_value<a class="anchor" aria-label="anchor" href="#arg-fill-value"></a></dt>
 <dd><p>the number to fill the output tensor with.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-">full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, <a class="anchor" aria-label="anchor" href="#full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a></h2>
-    
+
 
 
 <p>memory_format=torch.preserve_format) -&gt; Tensor</p>
@@ -142,7 +126,7 @@ <h2 id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-devi
 <code>torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -151,14 +135,14 @@ <h2 id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-devi
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_gather.html b/reference/torch_gather.html
index 2843f64c4b..3231dfcaa3 100644
--- a/reference/torch_gather.html
+++ b/reference/torch_gather.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Gather</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Gather</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_gather.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the source tensor</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the axis along which to index</p></dd>
 
 
-<dt>index</dt>
+<dt id="arg-index">index<a class="anchor" aria-label="anchor" href="#arg-index"></a></dt>
 <dd><p>(LongTensor) the indices of elements to gather</p></dd>
 
 
-<dt>sparse_grad</dt>
+<dt id="arg-sparse-grad">sparse_grad<a class="anchor" aria-label="anchor" href="#arg-sparse-grad"></a></dt>
 <dd><p>(bool,optional) If <code>TRUE</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="gather-input-dim-index-sparse-grad-false-gt-tensor-">gather(input, dim, index, sparse_grad=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gather-input-dim-index-sparse-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Gathers values along an axis specified by <code>dim</code>.</p>
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_gcd.html b/reference/torch_gcd.html
index 5849ed9103..5fed5aacc8 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Gcd</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Gcd</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_gcd.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="gcd-input-other-out-none-gt-tensor-">gcd(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gcd-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise greatest common divisor (GCD) of <code>input</code> and <code>other</code>.</p>
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-      <img src="" class="logo" alt=""><h1>Ge</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Ge</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_ge.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="ge-input-other-out-null-gt-tensor-">ge(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ge-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes \(\mbox{input} \geq \mbox{other}\) element-wise.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 995941b0a4..21c613607e 100644
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@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A torch_generator  is an object which manages the state of the algorithm
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-      <img src="" class="logo" alt=""><h1>Create a Generator object</h1>
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+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/generator.R" class="external-link"><code>R/generator.R</code></a></small>
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@@ -115,7 +97,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 1234567</span>
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diff --git a/reference/torch_geqrf.html b/reference/torch_geqrf.html
index f99f04ec25..890fa6b0a7 100644
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+++ b/reference/torch_geqrf.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Geqrf</h1>
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+
+      <h1>Geqrf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_geqrf.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input matrix</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="geqrf-input-out-null-gt-tensor-tensor-">geqrf(input, out=NULL) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#geqrf-input-out-null-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>This is a low-level function for calling LAPACK directly. This function
@@ -114,7 +98,7 @@ <h2 id="geqrf-input-out-null-gt-tensor-tensor-">geqrf(input, out=NULL) -&gt; (Te
 <p>See <code>LAPACK documentation for geqrf</code>_ for further details.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -123,14 +107,14 @@ <h2 id="geqrf-input-out-null-gt-tensor-tensor-">geqrf(input, out=NULL) -&gt; (Te
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+
+      <h1>Ger</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_ger.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) 1-D input vector</p></dd>
 
 
-<dt>vec2</dt>
+<dt id="arg-vec-">vec2<a class="anchor" aria-label="anchor" href="#arg-vec-"></a></dt>
 <dd><p>(Tensor) 1-D input vector</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="ger-input-vec-out-null-gt-tensor-">ger(input, vec2, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ger-input-vec-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Outer product of <code>input</code> and <code>vec2</code>.
@@ -134,7 +118,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
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diff --git a/reference/torch_get_default_dtype.html b/reference/torch_get_default_dtype.html
new file mode 100644
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new file mode 100644
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diff --git a/reference/torch_get_num_threads.html b/reference/torch_get_num_threads.html
new file mode 100644
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index 0fc37e15da..591e2bc6a1 100644
--- a/reference/torch_get_rng_state.html
+++ b/reference/torch_get_rng_state.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Low level functionality to set and change the RNG state.
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+It's recommended to use torch_manual_seed() for most cases."><meta property="og:description" content="Low level functionality to set and change the RNG state.
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>RNG state management</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/generator.R" class="external-link"><code>R/generator.R</code></a></small>
+
+      <h1>RNG state management</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/generator.R" class="external-link"><code>R/generator.R</code></a></small>
       <div class="d-none name"><code>torch_get_rng_state.Rd</code></div>
     </div>
 
@@ -104,25 +86,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>state</dt>
+
+
+<dl><dt id="arg-state">state<a class="anchor" aria-label="anchor" href="#arg-state"></a></dt>
 <dd><p>A tensor with the current state or a list containing the state
 for each device - (for CUDA).</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>The cuda device index to get or set the state. If <code>NULL</code> gets the state
 for all available devices.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>torch_set_rng_state()</code>: Sets the RNG state for the CPU</p></li>
 <li><p><code>cuda_get_rng_state()</code>: Gets the RNG state for CUDA.</p></li>
 <li><p><code>cuda_set_rng_state()</code>: Sets the RNG state for CUDA.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -131,14 +115,14 @@ <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functio
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index e4da1ac72a..171b0b7213 100644
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-      <img src="" class="logo" alt=""><h1>Greater</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Greater</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_greater.Rd</code></div>
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@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="greater-input-other-out-none-gt-tensor-">greater(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#greater-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_gt.html">torch_gt()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -120,14 +104,14 @@ <h2 id="greater-input-other-out-none-gt-tensor-">greater(input, other, *, out=No
 </div>
 
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-  
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diff --git a/reference/torch_greater_equal.html b/reference/torch_greater_equal.html
index fdd30f12bf..d74188bc25 100644
--- a/reference/torch_greater_equal.html
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Greater_equal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Greater_equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_greater_equal.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="greater-equal-input-other-out-none-gt-tensor-">greater_equal(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#greater-equal-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_ge.html">torch_ge()</a></code>.</p>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -120,14 +104,14 @@ <h2 id="greater-equal-input-other-out-none-gt-tensor-">greater_equal(input, othe
 </div>
 
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diff --git a/reference/torch_gt.html b/reference/torch_gt.html
index 424ef6a590..147f2f568a 100644
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-      <img src="" class="logo" alt=""><h1>Gt</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Gt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_gt.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="gt-input-other-out-null-gt-tensor-">gt(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#gt-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes \(\mbox{input} &gt; \mbox{other}\) element-wise.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_half.html b/reference/torch_half.html
new file mode 100644
index 0000000000..b522acdaf8
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@@ -0,0 +1,8 @@
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diff --git a/reference/torch_hamming_window.html b/reference/torch_hamming_window.html
index 82de3e7903..415d4b7d67 100644
--- a/reference/torch_hamming_window.html
+++ b/reference/torch_hamming_window.html
@@ -1,8 +1,5 @@
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Hamming_window</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Hamming_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_hamming_window.Rd</code></div>
     </div>
 
@@ -104,35 +86,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>window_length</dt>
+
+
+<dl><dt id="arg-window-length">window_length<a class="anchor" aria-label="anchor" href="#arg-window-length"></a></dt>
 <dd><p>(int) the size of returned window</p></dd>
 
 
-<dt>periodic</dt>
+<dt id="arg-periodic">periodic<a class="anchor" aria-label="anchor" href="#arg-periodic"></a></dt>
 <dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(float, optional) The coefficient \(\beta\) in the equation above</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -143,7 +127,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Hamming window function.</p>
@@ -161,7 +145,7 @@ <h2 id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-
 <code>torch_hamming_window(L + 1, periodic=False)[:-1])</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -170,14 +154,14 @@ <h2 id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_hann_window.html b/reference/torch_hann_window.html
index 02dd963099..944527ca59 100644
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+++ b/reference/torch_hann_window.html
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Hann_window</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Hann_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_hann_window.Rd</code></div>
     </div>
 
@@ -102,27 +84,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>window_length</dt>
+
+
+<dl><dt id="arg-window-length">window_length<a class="anchor" aria-label="anchor" href="#arg-window-length"></a></dt>
 <dd><p>(int) the size of returned window</p></dd>
 
 
-<dt>periodic</dt>
+<dt id="arg-periodic">periodic<a class="anchor" aria-label="anchor" href="#arg-periodic"></a></dt>
 <dd><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -132,7 +116,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Hann window function.</p>
@@ -151,7 +135,7 @@ <h2 id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-
 <code>torch_hann_window(L + 1, periodic=False)[:-1])</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_heaviside.html b/reference/torch_heaviside.html
index 0899c68b3c..e026b16c5c 100644
--- a/reference/torch_heaviside.html
+++ b/reference/torch_heaviside.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Heaviside</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Heaviside</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_heaviside.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>values</dt>
+<dt id="arg-values">values<a class="anchor" aria-label="anchor" href="#arg-values"></a></dt>
 <dd><p>(Tensor) The values to use where <code>input</code> is zero.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="heaviside-input-values-out-none-gt-tensor-">heaviside(input, values, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#heaviside-input-values-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the Heaviside step function for each element in <code>input</code>.
@@ -136,7 +120,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_histc.html b/reference/torch_histc.html
index cb85270492..b3e7de3691 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Histc</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Histc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_histc.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>bins</dt>
+<dt id="arg-bins">bins<a class="anchor" aria-label="anchor" href="#arg-bins"></a></dt>
 <dd><p>(int) number of histogram bins</p></dd>
 
 
-<dt>min</dt>
+<dt id="arg-min">min<a class="anchor" aria-label="anchor" href="#arg-min"></a></dt>
 <dd><p>(int) lower end of the range (inclusive)</p></dd>
 
 
-<dt>max</dt>
+<dt id="arg-max">max<a class="anchor" aria-label="anchor" href="#arg-max"></a></dt>
 <dd><p>(int) upper end of the range (inclusive)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="histc-input-bins-min-max-out-null-gt-tensor-">histc(input, bins=100, min=0, max=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#histc-input-bins-min-max-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the histogram of a tensor.</p>
@@ -136,7 +120,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_hstack.html b/reference/torch_hstack.html
index 0c7804be0f..28e9f19e4c 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Hstack</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Hstack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_hstack.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="hstack-tensors-out-none-gt-tensor-">hstack(tensors, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hstack-tensors-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Stack tensors in sequence horizontally (column wise).</p>
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_hypot.html b/reference/torch_hypot.html
index 3703886557..47ef9c4f0c 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Hypot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Hypot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_hypot.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="hypot-input-other-out-none-gt-tensor-">hypot(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#hypot-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Given the legs of a right triangle, return its hypotenuse.</p>
@@ -129,7 +113,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -138,14 +122,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 5833dc3db0..334de7da7a 100644
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-      <img src="" class="logo" alt=""><h1>I0</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>I0</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="i-input-out-none-gt-tensor-">i0(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#i-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the zeroth order modified Bessel function of the first kind for each element of <code>input</code>.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6} ]</span>
 </code></pre></div>
     </div>
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index 928259821e..a3d8b9ee42 100644
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Integer type info</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/type-info.R" class="external-link"><code>R/type-info.R</code></a></small>
+
+      <h1>Integer type info</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/type-info.R" class="external-link"><code>R/type-info.R</code></a></small>
       <div class="d-none name"><code>torch_iinfo.Rd</code></div>
     </div>
 
@@ -98,12 +80,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dtype</dt>
+
+
+<dl><dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>dtype to get information from.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -112,14 +96,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_imag.html b/reference/torch_imag.html
index 68ddfea600..cbdbd14c80 100644
--- a/reference/torch_imag.html
+++ b/reference/torch_imag.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Imag"><title>Imag — torch_imag • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Imag — torch_imag"><meta property="og:description" content="Imag"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Imag — torch_imag • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Imag — torch_imag"><meta name="description" content="Imag"><meta property="og:description" content="Imag"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+    <li><h6 class="dropdown-header" data-toc-skip>Tensors</h6></li>
+    <li><a class="dropdown-item" href="../articles/tensor-creation.html">Creating tensors</a></li>
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+    <li><h6 class="dropdown-header" data-toc-skip>Datasets</h6></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Imag</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Imag</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_imag.Rd</code></div>
     </div>
 
@@ -95,20 +77,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="imag-input-gt-tensor-">imag(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#imag-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the imaginary part of the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>Not yet implemented.</p>
 <p>$$
@@ -119,13 +103,13 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu">torch_imag</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -134,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_index.html b/reference/torch_index.html
index 73c6b1e044..99b5f387a1 100644
--- a/reference/torch_index.html
+++ b/reference/torch_index.html
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-      <img src="" class="logo" alt=""><h1>Index torch tensors</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Index torch tensors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_index.Rd</code></div>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) Tensor that will be indexed.</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
 <code><a href="torch_dtype.html">torch_long()</a></code> dtype.</p></dd>
 
 </dl></div>
 
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diff --git a/reference/torch_index_put.html b/reference/torch_index_put.html
index 60287dda7d..5f35cb2273 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Modify values selected by <code>indices</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Modify values selected by <code>indices</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_index_put.Rd</code></div>
     </div>
 
@@ -95,27 +77,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) Tensor that will be indexed.</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
 <code><a href="torch_dtype.html">torch_long()</a></code> dtype.</p></dd>
 
 
-<dt>values</dt>
+<dt id="arg-values">values<a class="anchor" aria-label="anchor" href="#arg-values"></a></dt>
 <dd><p>(Tensor) values that will be replaced the indexed location. Used
 for <code>torch_index_put</code> and <code>torch_index_put_</code>.</p></dd>
 
 
-<dt>accumulate</dt>
+<dt id="arg-accumulate">accumulate<a class="anchor" aria-label="anchor" href="#arg-accumulate"></a></dt>
 <dd><p>(bool) Wether instead of replacing the current values with <code>values</code>,
 you want to add them.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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 </div>
 
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diff --git a/reference/torch_index_put_.html b/reference/torch_index_put_.html
index 4f294c750a..12959f040a 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>In-place version of <code>torch_index_put</code>.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>In-place version of <code>torch_index_put</code>.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_index_put_.Rd</code></div>
     </div>
 
@@ -95,27 +77,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) Tensor that will be indexed.</p></dd>
 
 
-<dt>indices</dt>
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>(<code>List[Tensor]</code>) List of indices. Indices are torch tensors with
 <code><a href="torch_dtype.html">torch_long()</a></code> dtype.</p></dd>
 
 
-<dt>values</dt>
+<dt id="arg-values">values<a class="anchor" aria-label="anchor" href="#arg-values"></a></dt>
 <dd><p>(Tensor) values that will be replaced the indexed location. Used
 for <code>torch_index_put</code> and <code>torch_index_put_</code>.</p></dd>
 
 
-<dt>accumulate</dt>
+<dt id="arg-accumulate">accumulate<a class="anchor" aria-label="anchor" href="#arg-accumulate"></a></dt>
 <dd><p>(bool) Wether instead of replacing the current values with <code>values</code>,
 you want to add them.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -124,14 +108,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/torch_index_select.html b/reference/torch_index_select.html
index c593378dd5..bd5c433854 100644
--- a/reference/torch_index_select.html
+++ b/reference/torch_index_select.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Index_select</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Index_select</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_index_select.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension in which we index</p></dd>
 
 
-<dt>index</dt>
+<dt id="arg-index">index<a class="anchor" aria-label="anchor" href="#arg-index"></a></dt>
 <dd><p>(LongTensor) the 1-D tensor containing the indices to index</p></dd>
 
 </dl></div>
@@ -116,7 +100,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="index-select-input-dim-index-out-null-gt-tensor-">index_select(input, dim, index, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#index-select-input-dim-index-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor which indexes the <code>input</code> tensor along dimension
@@ -137,13 +121,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_index_select</span><span class="op">(</span><span class="va">x</span>, <span class="fl">2</span>, <span class="va">indices</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2459  2.5702</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0427  0.2300</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0728  0.7199</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2243  0.0370</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5962  2.5766</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5611 -0.4842</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +136,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_install_path.html b/reference/torch_install_path.html
index d7c90592fb..e02b031ac3 100644
--- a/reference/torch_install_path.html
+++ b/reference/torch_install_path.html
@@ -1,10 +1,7 @@
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-      <img src="" class="logo" alt=""><h1>A simple exported version of install_path Returns the torch installation path.</h1>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Inverse</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Inverse</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_inverse.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of size \((*, n, n)\) where <code>*</code> is zero or more                    batch dimensions</p></dd>
 
 </dl></div>
@@ -106,7 +90,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="inverse-input-out-null-gt-tensor-">inverse(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#inverse-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Takes the inverse of the square matrix <code>input</code>. <code>input</code> can be batches
@@ -117,7 +101,7 @@ <h2 id="inverse-input-out-null-gt-tensor-">inverse(input, out=NULL) -&gt; Tensor
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_rand.html">torch_rand</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_inverse</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">z</span> <span class="op">=</span> <span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span></span>
@@ -128,11 +112,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">y</span> <span class="op">=</span> <span class="fu">torch_inverse</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">z</span> <span class="op">=</span> <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="torch_max.html">torch_max</a></span><span class="op">(</span><span class="fu"><a href="torch_abs.html">torch_abs</a></span><span class="op">(</span><span class="va">z</span> <span class="op">-</span> <span class="fu"><a href="torch_eye.html">torch_eye</a></span><span class="op">(</span><span class="fl">4</span><span class="op">)</span><span class="op">$</span><span class="fu">expand_as</span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Max non-zero</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
+
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diff --git a/reference/torch_is_complex.html b/reference/torch_is_complex.html
index 0e1989339b..b918816461 100644
--- a/reference/torch_is_complex.html
+++ b/reference/torch_is_complex.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Is_complex"><title>Is_complex — torch_is_complex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Is_complex — torch_is_complex"><meta property="og:description" content="Is_complex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Is_complex — torch_is_complex • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Is_complex — torch_is_complex"><meta name="description" content="Is_complex"><meta property="og:description" content="Is_complex"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Is_complex</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Is_complex</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_is_complex.Rd</code></div>
     </div>
 
@@ -95,20 +77,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the PyTorch tensor to test</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="is-complex-input-gt-bool-">is_complex(input) -&gt; (bool) <a class="anchor" aria-label="anchor" href="#is-complex-input-gt-bool-"></a></h2>
-    
+
 
 
 <p>Returns TRUE if the data type of <code>input</code> is a complex data type i.e.,
 one of <code>torch_complex64</code>, and <code>torch.complex128</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -117,14 +101,14 @@ <h2 id="is-complex-input-gt-bool-">is_complex(input) -&gt; (bool) <a class="anch
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diff --git a/reference/torch_is_floating_point.html b/reference/torch_is_floating_point.html
index 5536d4fc32..4fbe083542 100644
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-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Is_floating_point</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the PyTorch tensor to test</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="is-floating-point-input-gt-bool-">is_floating_point(input) -&gt; (bool) <a class="anchor" aria-label="anchor" href="#is-floating-point-input-gt-bool-"></a></h2>
-    
+
 
 
 <p>Returns TRUE if the data type of <code>input</code> is a floating point data type i.e.,
 one of <code>torch_float64</code>, <code>torch.float32</code> and <code>torch.float16</code>.</p>
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@@ -117,14 +101,14 @@ <h2 id="is-floating-point-input-gt-bool-">is_floating_point(input) -&gt; (bool)
 </div>
 
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diff --git a/reference/torch_is_installed.html b/reference/torch_is_installed.html
index 81ccccac09..5ed2284dda 100644
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-      <img src="" class="logo" alt=""><h1>Verifies if torch is installed</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/install.R" class="external-link"><code>R/install.R</code></a></small>
+
+      <h1>Verifies if torch is installed</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/install.R" class="external-link"><code>R/install.R</code></a></small>
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diff --git a/reference/torch_is_nonzero.html b/reference/torch_is_nonzero.html
index 4e1211952c..efd12bc191 100644
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+++ b/reference/torch_is_nonzero.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Is_nonzero</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Is_nonzero</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_is_nonzero.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="is-nonzero-input-gt-bool-">is_nonzero(input) -&gt; (bool) <a class="anchor" aria-label="anchor" href="#is-nonzero-input-gt-bool-"></a></h2>
-    
+
 
 
 <p>Returns TRUE if the <code>input</code> is a single element tensor which is not equal to zero
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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index 237bd6b04d..45695baa4d 100644
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-      <img src="" class="logo" alt=""><h1>Isclose</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Isclose</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isclose.Rd</code></div>
     </div>
 
@@ -95,30 +77,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) first tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) second tensor to compare</p></dd>
 
 
-<dt>rtol</dt>
+<dt id="arg-rtol">rtol<a class="anchor" aria-label="anchor" href="#arg-rtol"></a></dt>
 <dd><p>(float, optional) relative tolerance. Default: 1e-05</p></dd>
 
 
-<dt>atol</dt>
+<dt id="arg-atol">atol<a class="anchor" aria-label="anchor" href="#arg-atol"></a></dt>
 <dd><p>(float, optional) absolute tolerance. Default: 1e-08</p></dd>
 
 
-<dt>equal_nan</dt>
+<dt id="arg-equal-nan">equal_nan<a class="anchor" aria-label="anchor" href="#arg-equal-nan"></a></dt>
 <dd><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be
 considered equal. Default: <code>FALSE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-">isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with boolean elements representing if each element of
@@ -146,7 +130,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -155,14 +139,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_isfinite.html b/reference/torch_isfinite.html
index 7034461133..08a5a78484 100644
--- a/reference/torch_isfinite.html
+++ b/reference/torch_isfinite.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Isfinite — torch_isfinite • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Isfinite — torch_isfinite"><meta name="description" content="Isfinite"><meta property="og:description" content="Isfinite"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-      <img src="" class="logo" alt=""><h1>Isfinite</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Isfinite</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isfinite.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) A tensor to check</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with boolean elements representing if each element is <code>Finite</code> or not.</p>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{5} ]</span>
 </code></pre></div>
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diff --git a/reference/torch_isinf.html b/reference/torch_isinf.html
index 7e939d0af6..5c89723fa0 100644
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-      <img src="" class="logo" alt=""><h1>Isinf</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Isinf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isinf.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) A tensor to check</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with boolean elements representing if each element is <code>+/-INF</code> or not.</p>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{5} ]</span>
 </code></pre></div>
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diff --git a/reference/torch_isnan.html b/reference/torch_isnan.html
index 6223f28bf3..b19d4f3ba5 100644
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@@ -1,8 +1,5 @@
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+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isnan.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
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+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) A tensor to check</p></dd>
 
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-    
+
 
 
 <p>Returns a new tensor with boolean elements representing if each element is <code>NaN</code> or not.</p>
@@ -120,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
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index 29f13093ec..c0eb1ea62e 100644
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-      <img src="" class="logo" alt=""><h1>Isneginf</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Isneginf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isneginf.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="isneginf-input-out-none-gt-tensor-">isneginf(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isneginf-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Tests if each element of <code>input</code> is negative infinity or not.</p>
@@ -121,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 </div>
 
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diff --git a/reference/torch_isposinf.html b/reference/torch_isposinf.html
index db3b2f06bf..1b70f830d5 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Isposinf</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Isposinf</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isposinf.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="isposinf-input-out-none-gt-tensor-">isposinf(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isposinf-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Tests if each element of <code>input</code> is positive infinity or not.</p>
@@ -121,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -130,14 +114,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_isreal.html b/reference/torch_isreal.html
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-      <img src="" class="logo" alt=""><h1>Isreal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Isreal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_isreal.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="isreal-input-gt-tensor-">isreal(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#isreal-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with boolean elements representing if each element of <code>input</code> is real-valued or not.
@@ -117,7 +101,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -126,14 +110,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_istft.html b/reference/torch_istft.html
index 9f132b1811..3829ae9c08 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Istft</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Istft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_istft.Rd</code></div>
     </div>
 
@@ -106,53 +88,55 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) The input tensor. Expected to be output of <code><a href="torch_stft.html">torch_stft()</a></code>,
 can either be complex (<code>channel</code>, <code>fft_size</code>, <code>n_frame</code>), or real
 (<code>channel</code>, <code>fft_size</code>, <code>n_frame</code>, 2) where the <code>channel</code> dimension is
 optional.</p></dd>
 
 
-<dt>n_fft</dt>
+<dt id="arg-n-fft">n_fft<a class="anchor" aria-label="anchor" href="#arg-n-fft"></a></dt>
 <dd><p>(int) Size of Fourier transform</p></dd>
 
 
-<dt>hop_length</dt>
+<dt id="arg-hop-length">hop_length<a class="anchor" aria-label="anchor" href="#arg-hop-length"></a></dt>
 <dd><p>(Optional<code>[int]</code>) The distance between neighboring sliding window frames.
 (Default: <code>n_fft %% 4</code>)</p></dd>
 
 
-<dt>win_length</dt>
+<dt id="arg-win-length">win_length<a class="anchor" aria-label="anchor" href="#arg-win-length"></a></dt>
 <dd><p>(Optional<code>[int]</code>) The size of window frame and STFT filter.
 (Default: <code>n_fft</code>)</p></dd>
 
 
-<dt>window</dt>
+<dt id="arg-window">window<a class="anchor" aria-label="anchor" href="#arg-window"></a></dt>
 <dd><p>(Optional(torch.Tensor)) The optional window function.
 (Default: <code>torch_ones(win_length)</code>)</p></dd>
 
 
-<dt>center</dt>
+<dt id="arg-center">center<a class="anchor" aria-label="anchor" href="#arg-center"></a></dt>
 <dd><p>(bool) Whether <code>input</code> was padded on both sides so that the
 \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).
 (Default: <code>TRUE</code>)</p></dd>
 
 
-<dt>normalized</dt>
+<dt id="arg-normalized">normalized<a class="anchor" aria-label="anchor" href="#arg-normalized"></a></dt>
 <dd><p>(bool) Whether the STFT was normalized. (Default: <code>FALSE</code>)</p></dd>
 
 
-<dt>onesided</dt>
+<dt id="arg-onesided">onesided<a class="anchor" aria-label="anchor" href="#arg-onesided"></a></dt>
 <dd><p>(Optional(bool)) Whether the STFT was onesided.
 (Default: <code>TRUE</code> if <code>n_fft != fft_size</code> in the input size)</p></dd>
 
 
-<dt>length</dt>
+<dt id="arg-length">length<a class="anchor" aria-label="anchor" href="#arg-length"></a></dt>
 <dd><p>(Optional(int)]) The amount to trim the signal by (i.e. the
 original signal length). (Default: whole signal)</p></dd>
 
 
-<dt>return_complex</dt>
+<dt id="arg-return-complex">return_complex<a class="anchor" aria-label="anchor" href="#arg-return-complex"></a></dt>
 <dd><p>(Optional(bool)) Whether the output should be complex,
 or if the input should be assumed to derive from a real signal and window.
 Note that this is incompatible with <code>onesided=TRUE</code>. (Default: <code>FALSE</code>)</p></dd>
@@ -182,7 +166,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 IEEE Trans. ASSP, vol.32, no.2, pp.236-243, Apr. 1984.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -191,14 +175,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/torch_kaiser_window.html b/reference/torch_kaiser_window.html
index b1610b50be..2eb8246681 100644
--- a/reference/torch_kaiser_window.html
+++ b/reference/torch_kaiser_window.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Kaiser_window</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Kaiser_window</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_kaiser_window.Rd</code></div>
     </div>
 
@@ -103,31 +85,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>window_length</dt>
+
+
+<dl><dt id="arg-window-length">window_length<a class="anchor" aria-label="anchor" href="#arg-window-length"></a></dt>
 <dd><p>(int) length of the window.</p></dd>
 
 
-<dt>periodic</dt>
+<dt id="arg-periodic">periodic<a class="anchor" aria-label="anchor" href="#arg-periodic"></a></dt>
 <dd><p>(bool, optional) If TRUE, returns a periodic window suitable for use in spectral analysis.        If FALSE, returns a symmetric window suitable for use in filter design.</p></dd>
 
 
-<dt>beta</dt>
+<dt id="arg-beta">beta<a class="anchor" aria-label="anchor" href="#arg-beta"></a></dt>
 <dd><p>(float, optional) shape parameter for the window.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
@@ -138,7 +122,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-">kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the Kaiser window with window length <code>window_length</code> and shape parameter <code>beta</code>.</p>
@@ -154,7 +138,7 @@ <h2 id="kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-s
 to produce a periodic window as input to functions like <code><a href="torch_stft.html">torch_stft()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-s
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/torch_kron.html b/reference/torch_kron.html
index b8ae33c8cb..d6145dd3be 100644
--- a/reference/torch_kron.html
+++ b/reference/torch_kron.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Kronecker product</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Kronecker product</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_kron.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(<code>Tensor</code>) input Tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(<code>Tensor</code>) other tensor.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/torch_kthvalue.html b/reference/torch_kthvalue.html
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Kthvalue</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Kthvalue</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_kthvalue.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>(int) k for the k-th smallest element</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) the dimension to find the kth value along</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-">kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the <code>k</code> th
@@ -150,7 +134,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index c6230381a8..8ccdd04427 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Lcm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Lcm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_lcm.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="lcm-input-other-out-none-gt-tensor-">lcm(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lcm-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise least common multiple (LCM) of <code>input</code> and <code>other</code>.</p>
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-      <img src="" class="logo" alt=""><h1>Le</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Le</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_le.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="le-input-other-out-null-gt-tensor-">le(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#le-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes \(\mbox{input} \leq \mbox{other}\) element-wise.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_lerp.html b/reference/torch_lerp.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Lerp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Lerp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_lerp.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor with the starting points</p></dd>
 
 
-<dt>end</dt>
+<dt id="arg-end">end<a class="anchor" aria-label="anchor" href="#arg-end"></a></dt>
 <dd><p>(Tensor) the tensor with the ending points</p></dd>
 
 
-<dt>weight</dt>
+<dt id="arg-weight">weight<a class="anchor" aria-label="anchor" href="#arg-weight"></a></dt>
 <dd><p>(float or tensor) the weight for the interpolation formula</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="lerp-input-end-weight-out-null-">lerp(input, end, weight, out=NULL) <a class="anchor" aria-label="anchor" href="#lerp-input-end-weight-out-null-"></a></h2>
-    
+
 
 
 <p>Does a linear interpolation of two tensors <code>start</code> (given by <code>input</code>) and <code>end</code> based
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index e79a1e70d8..12eb09260e 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Less</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Less</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_less.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="less-input-other-out-none-gt-tensor-">less(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#less-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_lt.html">torch_lt()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -120,14 +104,14 @@ <h2 id="less-input-other-out-none-gt-tensor-">less(input, other, *, out=None) -&
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
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diff --git a/reference/torch_less_equal.html b/reference/torch_less_equal.html
index 6e24d4c606..d920002ffc 100644
--- a/reference/torch_less_equal.html
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Less_equal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Less_equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_less_equal.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
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+
 
 
 <p>Alias for <code><a href="torch_le.html">torch_le()</a></code>.</p>
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@@ -120,14 +104,14 @@ <h2 id="less-equal-input-other-out-none-gt-tensor-">less_equal(input, other, *,
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/torch_lgamma.html b/reference/torch_lgamma.html
index 7658c3c943..a69143c369 100644
--- a/reference/torch_lgamma.html
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Lgamma</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Lgamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_lgamma.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="lgamma-input-out-null-gt-tensor-">lgamma(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lgamma-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the logarithm of the gamma function on <code>input</code>.</p>
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -134,14 +118,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_linspace.html b/reference/torch_linspace.html
index 39cf0484b4..007c68f9bb 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Linspace</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Linspace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_linspace.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>start</dt>
+
+
+<dl><dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>(float) the starting value for the set of points</p></dd>
 
 
-<dt>end</dt>
+<dt id="arg-end">end<a class="anchor" aria-label="anchor" href="#arg-end"></a></dt>
 <dd><p>(float) the ending value for the set of points</p></dd>
 
 
-<dt>steps</dt>
+<dt id="arg-steps">steps<a class="anchor" aria-label="anchor" href="#arg-steps"></a></dt>
 <dd><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a one-dimensional tensor of <code>steps</code>
@@ -155,7 +139,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -164,14 +148,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_load.html b/reference/torch_load.html
index 10f0b8a39a..d887f2afb1 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Loads a saved object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+
+      <h1>Loads a saved object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/save.R" class="external-link"><code>R/save.R</code></a></small>
       <div class="d-none name"><code>torch_load.Rd</code></div>
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@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>path</dt>
+
+
+<dl><dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>a path to the saved object</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>a device to load tensors to. By default we load to the <code>cpu</code> but you can also
 load them to any <code>cuda</code> device. If <code>NULL</code> then the device where the tensor has been saved will
 be reused.</p></dd>
@@ -107,12 +91,12 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other torch_save: 
+    <div class="dont-index"><p>Other torch_save:
 <code><a href="torch_save.html">torch_save</a>()</code>,
 <code><a href="torch_serialize.html">torch_serialize</a>()</code></p></div>
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@@ -121,14 +105,14 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 </div>
 
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index 614a0e6f74..4024cdac45 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Log</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Log</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_log.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="log-input-out-null-gt-tensor-">log(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the natural logarithm of the elements
@@ -120,15 +104,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_log</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1847</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> nan</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_log10.html b/reference/torch_log10.html
index ba13fc09b7..98eb5c9c0f 100644
--- a/reference/torch_log10.html
+++ b/reference/torch_log10.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Log10"><title>Log10 — torch_log10 • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Log10 — torch_log10"><meta property="og:description" content="Log10"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Log10 — torch_log10 • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Log10 — torch_log10"><meta name="description" content="Log10"><meta property="og:description" content="Log10"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Log10</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Log10</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_log10.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="log-input-out-null-gt-tensor-">log10(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the logarithm to the base 10 of the elements
@@ -120,16 +104,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_log10</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.01 *</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -8.2896</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -100.2900</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.4480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -46.5474</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -13.5409</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1356</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4927</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0069</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8804</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8490</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -138,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index bd78f4fdb2..571d715b5d 100644
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@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Log1p</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Log1p</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_log1p.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
@@ -106,7 +90,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="log-p-input-out-null-gt-tensor-">log1p(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-p-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the natural logarithm of (1 + <code>input</code>).</p>
@@ -124,15 +108,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_log1p</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4804</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2434</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0869</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0442</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.4409</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0291</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7767</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1844</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_log2.html b/reference/torch_log2.html
index 377dc1a893..8460b714e7 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Log2</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Log2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_log2.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="log-input-out-null-gt-tensor-">log2(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#log-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the logarithm to the base 2 of the elements
@@ -120,15 +104,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_log2</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7491</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.9394</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.5573</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6504</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4162</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1048</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.1116</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3869</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6945</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.8945</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_logaddexp.html b/reference/torch_logaddexp.html
index 8cdf79a34b..a0bb1c1a15 100644
--- a/reference/torch_logaddexp.html
+++ b/reference/torch_logaddexp.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Logaddexp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logaddexp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logaddexp.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logaddexp-input-other-out-none-gt-tensor-">logaddexp(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logaddexp-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Logarithm of the sum of exponentiations of the inputs.</p>
@@ -133,7 +117,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -142,14 +126,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_logaddexp2.html b/reference/torch_logaddexp2.html
index 7dca916cae..8723c7ebcd 100644
--- a/reference/torch_logaddexp2.html
+++ b/reference/torch_logaddexp2.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Logaddexp2</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logaddexp2</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logaddexp2.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logaddexp-input-other-out-none-gt-tensor-">logaddexp2(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logaddexp-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Logarithm of the sum of exponentiations of the inputs in base-2.</p>
@@ -113,7 +97,7 @@ <h2 id="logaddexp-input-other-out-none-gt-tensor-">logaddexp2(input, other, *, o
 <code><a href="torch_logaddexp.html">torch_logaddexp()</a></code> for more details.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -122,14 +106,14 @@ <h2 id="logaddexp-input-other-out-none-gt-tensor-">logaddexp2(input, other, *, o
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_logcumsumexp.html b/reference/torch_logcumsumexp.html
index 16e519d52d..27ac9cad26 100644
--- a/reference/torch_logcumsumexp.html
+++ b/reference/torch_logcumsumexp.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Logcumsumexp"><title>Logcumsumexp — torch_logcumsumexp • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Logcumsumexp — torch_logcumsumexp"><meta property="og:description" content="Logcumsumexp"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Logcumsumexp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logcumsumexp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logcumsumexp.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to do the operation over</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logcumsumexp-input-dim-out-none-gt-tensor-">logcumsumexp(input, dim, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logcumsumexp-input-dim-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the logarithm of the cumulative summation of the exponentiation of
@@ -124,20 +108,20 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_logcumsumexp</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.8849</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8080</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6575</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0437</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1585</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.3805</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5151</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5274</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7236</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9456</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1725</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1297</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2222</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2610</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.4665</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.5697</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.6306</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.6780</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-      <img src="" class="logo" alt=""><h1>Logdet</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logdet</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logdet.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="logdet-input-gt-tensor-">logdet(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logdet-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Calculates log determinant of a square matrix or batches of square matrices.</p>
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_logical_and.html b/reference/torch_logical_and.html
index d71ed21994..83b2b9ace2 100644
--- a/reference/torch_logical_and.html
+++ b/reference/torch_logical_and.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Logical_and</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logical_and</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logical_and.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the tensor to compute AND with</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logical-and-input-other-out-null-gt-tensor-">logical_and(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-and-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise logical AND of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
@@ -120,13 +104,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_logical_and</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu">torch_logical_and</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, out<span class="op">=</span><span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_logical_not.html b/reference/torch_logical_not.html
index e5b332d5b8..06250179d6 100644
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+++ b/reference/torch_logical_not.html
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-      <img src="" class="logo" alt=""><h1>Logical_not</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Logical_not</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_logical_not.Rd</code></div>
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@@ -91,13 +73,15 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
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-    <dl><dt>self</dt>
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+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logical-not-input-out-null-gt-tensor-">logical_not(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-not-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
@@ -119,7 +103,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{3} ]</span>
 </code></pre></div>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Logical_or</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logical_or</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logical_or.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the tensor to compute OR with</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logical-or-input-other-out-null-gt-tensor-">logical_or(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-or-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise logical OR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
@@ -120,15 +104,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_int8</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">double</span><span class="op">(</span><span class="op">)</span>, <span class="va">b</span><span class="op">$</span><span class="fu">double</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span><span class="op">$</span><span class="fu">double</span><span class="op">(</span><span class="op">)</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_logical_or</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, out<span class="op">=</span><span class="fu"><a href="torch_empty.html">torch_empty</a></span><span class="op">(</span><span class="fl">4</span>, dtype<span class="op">=</span><span class="fu"><a href="torch_dtype.html">torch_bool</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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diff --git a/reference/torch_logical_xor.html b/reference/torch_logical_xor.html
index fb0e63c4ff..3de167af39 100644
--- a/reference/torch_logical_xor.html
+++ b/reference/torch_logical_xor.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Logical_xor"><title>Logical_xor — torch_logical_xor • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Logical_xor — torch_logical_xor"><meta property="og:description" content="Logical_xor"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Logical_xor — torch_logical_xor • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Logical_xor — torch_logical_xor"><meta name="description" content="Logical_xor"><meta property="og:description" content="Logical_xor"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Logical_xor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logical_xor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logical_xor.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the tensor to compute XOR with</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logical-xor-input-other-out-null-gt-tensor-">logical_xor(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logical-xor-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise logical XOR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
@@ -131,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_logit.html b/reference/torch_logit.html
index 959c2c5bce..16d7160cff 100644
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+++ b/reference/torch_logit.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Logit</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logit.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>eps</dt>
+<dt id="arg-eps">eps<a class="anchor" aria-label="anchor" href="#arg-eps"></a></dt>
 <dd><p>(float, optional) the epsilon for input clamp bound. Default: <code>None</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logit-input-eps-none-out-none-gt-tensor-">logit(input, eps=None, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logit-input-eps-none-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the logit of the elements of <code>input</code>.
@@ -131,15 +115,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_logit</span><span class="op">(</span><span class="va">a</span>, eps<span class="op">=</span><span class="fl">1e-6</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9320</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3958</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3.1804</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0449</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4256</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5341</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -3.9903</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4445</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.6060</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2971</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Logspace</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Logspace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_logspace.Rd</code></div>
     </div>
 
@@ -104,41 +86,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>start</dt>
+
+
+<dl><dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>(float) the starting value for the set of points</p></dd>
 
 
-<dt>end</dt>
+<dt id="arg-end">end<a class="anchor" aria-label="anchor" href="#arg-end"></a></dt>
 <dd><p>(float) the ending value for the set of points</p></dd>
 
 
-<dt>steps</dt>
+<dt id="arg-steps">steps<a class="anchor" aria-label="anchor" href="#arg-steps"></a></dt>
 <dd><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></dd>
 
 
-<dt>base</dt>
+<dt id="arg-base">base<a class="anchor" aria-label="anchor" href="#arg-base"></a></dt>
 <dd><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a one-dimensional tensor of <code>steps</code> points
@@ -161,7 +145,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -170,14 +154,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 0fdc6b2d69..5c8805a1d7 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Logsumexp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Logsumexp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_logsumexp.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="logsumexp-input-dim-keepdim-false-out-null-">logsumexp(input, dim, keepdim=False, out=NULL) <a class="anchor" aria-label="anchor" href="#logsumexp-input-dim-keepdim-false-out-null-"></a></h2>
-    
+
 
 
 <p>Returns the log of summed exponentials of each row of the <code>input</code>
@@ -133,13 +117,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_logsumexp</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8542</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3111</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.1658</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3163</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4516</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3805</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -148,14 +132,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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new file mode 100644
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diff --git a/reference/torch_lstsq.html b/reference/torch_lstsq.html
index 021d649466..be2fe48b5f 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Lstsq</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a></small>
+
+      <h1>Lstsq</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a></small>
       <div class="d-none name"><code>torch_lstsq.Rd</code></div>
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@@ -91,11 +73,13 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the matrix \(B\)</p></dd>
 
 
-<dt>A</dt>
+<dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></dd>
 
 </dl></div>
@@ -105,7 +89,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="lstsq-input-a-out-null-gt-tensor-">lstsq(input, A, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lstsq-input-a-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the solution to the least squares and least norm problems for a full
@@ -129,7 +113,7 @@ <h2 id="lstsq-input-a-out-null-gt-tensor-">lstsq(input, A, out=NULL) -&gt; Tenso
 remaining \(m - n\) rows of that column.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +122,14 @@ <h2 id="lstsq-input-a-out-null-gt-tensor-">lstsq(input, A, out=NULL) -&gt; Tenso
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Lt</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Lt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_lt.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="lt-input-other-out-null-gt-tensor-">lt(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lt-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes \(\mbox{input} &lt; \mbox{other}\) element-wise.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_lu.html b/reference/torch_lu.html
index 2f8b89eee4..fb0b58a02c 100644
--- a/reference/torch_lu.html
+++ b/reference/torch_lu.html
@@ -1,12 +1,9 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Computes the LU factorization of a matrix or batches of matrices A. Returns a
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>LU — torch_lu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LU — torch_lu"><meta name="description" content="Computes the LU factorization of a matrix or batches of matrices A. Returns a
 tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True."><title>LU — torch_lu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="LU — torch_lu"><meta property="og:description" content="Computes the LU factorization of a matrix or batches of matrices A. Returns a
+is set to True."><meta property="og:description" content="Computes the LU factorization of a matrix or batches of matrices A. Returns a
 tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -14,77 +11,62 @@
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>LU</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>LU</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_lu.Rd</code></div>
     </div>
 
@@ -101,19 +83,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>A</dt>
+
+
+<dl><dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></dd>
 
 
-<dt>pivot</dt>
+<dt id="arg-pivot">pivot<a class="anchor" aria-label="anchor" href="#arg-pivot"></a></dt>
 <dd><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></dd>
 
 
-<dt>get_infos</dt>
+<dt id="arg-get-infos">get_infos<a class="anchor" aria-label="anchor" href="#arg-get-infos"></a></dt>
 <dd><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></dd>
 
 
-<dt>out</dt>
+<dt id="arg-out">out<a class="anchor" aria-label="anchor" href="#arg-out"></a></dt>
 <dd><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
 in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
 elements in the tuple are Tensor, IntTensor. Default: NULL</p></dd>
@@ -130,25 +114,25 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3834  0.6801 -1.2962</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1962 -0.7595  1.2475</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8721 -0.6387  0.5538</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9564 -0.4735  0.4072</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8957  0.8704 -0.5667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9415  0.9478  0.6570</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -2.8596 -0.3844  1.0102</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7906 -0.3574 -1.2487</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4587  0.5223  1.4304</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1940 -0.6063 -0.7687</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.0325  1.0327 -0.4151</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2992  0.2452 -0.1442</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3,3} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  3  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUIntType{2,3} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -157,14 +141,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_lu_solve.html b/reference/torch_lu_solve.html
index d94d39aa1b..82980c388a 100644
--- a/reference/torch_lu_solve.html
+++ b/reference/torch_lu_solve.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Lu_solve</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Lu_solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_lu_solve.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></dd>
 
 
-<dt>LU_data</dt>
+<dt id="arg-lu-data">LU_data<a class="anchor" aria-label="anchor" href="#arg-lu-data"></a></dt>
 <dd><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></dd>
 
 
-<dt>LU_pivots</dt>
+<dt id="arg-lu-pivots">LU_pivots<a class="anchor" aria-label="anchor" href="#arg-lu-pivots"></a></dt>
 <dd><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-">lu_solve(input, LU_data, LU_pivots, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the LU solve of the linear system \(Ax = b\) using the partially pivoted
@@ -126,11 +110,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="torch_norm.html">torch_norm</a></span><span class="op">(</span><span class="fu"><a href="torch_bmm.html">torch_bmm</a></span><span class="op">(</span><span class="va">A</span>, <span class="va">x</span><span class="op">)</span> <span class="op">-</span> <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2.14908e-07</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4.74035e-07</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_lu_unpack.html b/reference/torch_lu_unpack.html
index affe4e5b83..1173a94691 100644
--- a/reference/torch_lu_unpack.html
+++ b/reference/torch_lu_unpack.html
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-      <img src="" class="logo" alt=""><h1>Lu_unpack</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Lu_unpack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_lu_unpack.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>LU_data</dt>
+
+
+<dl><dt id="arg-lu-data">LU_data<a class="anchor" aria-label="anchor" href="#arg-lu-data"></a></dt>
 <dd><p>(Tensor) – the packed LU factorization data</p></dd>
 
 
-<dt>LU_pivots</dt>
+<dt id="arg-lu-pivots">LU_pivots<a class="anchor" aria-label="anchor" href="#arg-lu-pivots"></a></dt>
 <dd><p>(Tensor) – the packed LU factorization pivots</p></dd>
 
 
-<dt>unpack_data</dt>
+<dt id="arg-unpack-data">unpack_data<a class="anchor" aria-label="anchor" href="#arg-unpack-data"></a></dt>
 <dd><p>(logical) – flag indicating if the data should be unpacked. If FALSE, then the returned L and U are NULL Default: TRUE</p></dd>
 
 
-<dt>unpack_pivots</dt>
+<dt id="arg-unpack-pivots">unpack_pivots<a class="anchor" aria-label="anchor" href="#arg-unpack-pivots"></a></dt>
 <dd><p>(logical) – flag indicating if the pivots should be unpacked into a permutation matrix P. If FALSE, then the returned P is None. Default: TRUE</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="lu-unpack-lu-data-lu-pivots-unpack-data-true-unpack-pivots-true-gt-tensor-">lu_unpack(LU_data, LU_pivots, unpack_data = TRUE, unpack_pivots=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#lu-unpack-lu-data-lu-pivots-unpack-data-true-unpack-pivots-true-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Unpacks the data and pivots from a LU factorization of a tensor into tensors <code>L</code> and <code>U</code> and
@@ -121,7 +105,7 @@ <h2 id="lu-unpack-lu-data-lu-pivots-unpack-data-true-unpack-pivots-true-gt-tenso
 Returns a list of tensors as <code>list(the P tensor (permutation matrix), the L tensor, the U tensor)</code></p>
     </div>
 
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -130,14 +114,14 @@ <h2 id="lu-unpack-lu-data-lu-pivots-unpack-data-true-unpack-pivots-true-gt-tenso
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index 0cc01f4a7b..6ba394a8c8 100644
--- a/reference/torch_manual_seed.html
+++ b/reference/torch_manual_seed.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Sets the seed for generating random numbers.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/generator.R" class="external-link"><code>R/generator.R</code></a></small>
+
+      <h1>Sets the seed for generating random numbers.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/generator.R" class="external-link"><code>R/generator.R</code></a></small>
       <div class="d-none name"><code>torch_manual_seed.Rd</code></div>
     </div>
 
@@ -99,25 +81,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>seed</dt>
+
+
+<dl><dt id="arg-seed">seed<a class="anchor" aria-label="anchor" href="#arg-seed"></a></dt>
 <dd><p>integer seed.</p></dd>
 
 
-<dt>.env</dt>
+<dt id="arg--env">.env<a class="anchor" aria-label="anchor" href="#arg--env"></a></dt>
 <dd><p>environment that will take the modifications from manual_seed.</p></dd>
 
 
-<dt>code</dt>
+<dt id="arg-code">code<a class="anchor" aria-label="anchor" href="#arg-code"></a></dt>
 <dd><p>expression to run in the context of the seed</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>unused currently.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>local_torch_manual_seed()</code>: Modifies the torch seed in the environment scope.</p></li>
 <li><p><code>with_torch_manual_seed()</code>: A with context to change the seed during the function execution.</p></li>
 </ul></div>
@@ -128,7 +112,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
 move them to MPS if reproducibility is required.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_masked_select.html b/reference/torch_masked_select.html
index dd73c60f16..08e0b881cd 100644
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+++ b/reference/torch_masked_select.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Masked_select</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Masked_select</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_masked_select.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>mask</dt>
+<dt id="arg-mask">mask<a class="anchor" aria-label="anchor" href="#arg-mask"></a></dt>
 <dd><p>(BoolTensor) the tensor containing the binary mask to index with</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="masked-select-input-mask-out-null-gt-tensor-">masked_select(input, mask, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#masked-select-input-mask-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new 1-D tensor which indexes the <code>input</code> tensor according to
@@ -130,14 +114,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_masked_select</span><span class="op">(</span><span class="va">x</span>, <span class="va">mask</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6461</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4241</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1868</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5365</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3810</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7781</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2837</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 2f89af1cae..659eec4151 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Matmul</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Matmul</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_matmul.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first tensor to be multiplied</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second tensor to be multiplied</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="matmul-input-other-out-null-gt-tensor-">matmul(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matmul-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Matrix product of two tensors.</p>
@@ -158,40 +142,40 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.6703  0.1748  0.1019  2.1782 -1.9164</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   3.4863  0.7423 -0.8082  1.5387 -0.2886</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.2476  0.9486  1.8962 -1.2999  1.4790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3851  0.7968  0.2611  0.2167 -0.2853</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8506 -1.5824  1.5723  0.1699  0.1608</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8180 -0.1436 -3.5947  4.8995  1.5083</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.9446  1.0571  1.4544 -0.2813  0.9670</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.4007 -0.4533  0.5070 -0.2281 -0.8250</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.2017  0.3509  0.6494  0.0332  0.1461</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.8802 -1.0578 -0.0391 -1.0979 -0.2634</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.3431 -1.8385  4.6939 -4.6969 -0.5755</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2714 -2.5916  1.3334 -2.2095  0.4801</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (3,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -3.4653 -1.6867 -0.8829 -0.4846 -1.2331</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.6890  0.2529  1.7228 -0.7841  0.2751</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.6621  0.6449  0.9571  2.8237 -2.0121</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9593  5.0288 -3.3491  0.1169 -2.4787</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4831 -0.2680 -0.7904 -0.1181 -0.0924</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.4521 -0.2914 -1.1571  2.7915  0.9958</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (4,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3645 -0.8249  0.9828 -2.5264  0.8061</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.7000 -0.6100 -0.2068  0.9414 -1.2617</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.9504  1.7264  2.3723 -1.4703  2.5558</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.7540 -2.6506  3.9934 -1.1908  1.1497</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0666  0.2036 -2.0001  2.8705  1.1918</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.9074 -1.2377 -0.1101 -0.8432  0.7071</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (5,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3427  0.8398  1.4759 -0.6493  1.1072</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.5539 -1.2103 -1.4570 -0.5723 -0.6212</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.2649 -0.9920 -2.4079  0.2779 -0.6104</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.1063  0.2889  2.1356 -0.1968 -0.9114</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.5778  2.4138 -3.6613  1.2819 -0.8573</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.4689 -1.2846 -0.6056  1.1021  0.3662</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (6,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.8022  0.1544 -0.5616  0.9548 -0.2760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.6957 -2.7108 -2.1812 -1.5212 -1.2216</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   2.8840 -0.3510 -1.7105 -1.5380  1.7126</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.8615  0.0674  1.1361  0.2918 -0.2679</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.4541 -0.7477 -2.1147  1.1032  1.2781</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.2123  3.6691  2.0633 -3.5226 -2.3677</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> ... [the output was truncated (use n=-1 to disable)]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{10,3,5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -200,14 +184,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 11614361c3..5b197111a1 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Matrix_exp</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Matrix_exp</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_matrix_exp.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="matrix-power-input-gt-tensor-">matrix_power(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matrix-power-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the matrix exponential. Supports batched input.
@@ -134,7 +118,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
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@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-      <img src="" class="logo" alt=""><h1>Matrix_power</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Matrix_power</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_matrix_power.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>n</dt>
+<dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) the power to raise the matrix to</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="matrix-power-input-n-gt-tensor-">matrix_power(input, n) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matrix-power-input-n-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the matrix raised to the power <code>n</code> for square matrices.
@@ -126,16 +110,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.8053 -0.0262</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.1418 -1.8350</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    0.2047   0.6763</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    4.3261  17.6104</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   0.3966 -0.3756</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3245  0.3026</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.8462 -1.1701</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.4960  2.3349</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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 </div>
 
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diff --git a/reference/torch_matrix_rank.html b/reference/torch_matrix_rank.html
index cf2bf350e3..50cb68fd79 100644
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-      <img src="" class="logo" alt=""><h1>Matrix_rank</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a></small>
+
+      <h1>Matrix_rank</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a></small>
       <div class="d-none name"><code>torch_matrix_rank.Rd</code></div>
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@@ -91,21 +73,23 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input 2-D tensor</p></dd>
 
 
-<dt>tol</dt>
+<dt id="arg-tol">tol<a class="anchor" aria-label="anchor" href="#arg-tol"></a></dt>
 <dd><p>(float, optional) the tolerance value. Default: <code>NULL</code></p></dd>
 
 
-<dt>symmetric</dt>
+<dt id="arg-symmetric">symmetric<a class="anchor" aria-label="anchor" href="#arg-symmetric"></a></dt>
 <dd><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>FALSE</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-">matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#matrix-rank-input-tol-null-symmetric-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the numerical rank of a 2-D tensor. The method to compute the
@@ -119,7 +103,7 @@ <h2 id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-">matrix_rank(input
 is the epsilon value for the datatype of <code>input</code>.</p>
     </div>
 
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@@ -128,14 +112,14 @@ <h2 id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-">matrix_rank(input
 </div>
 
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diff --git a/reference/torch_max.html b/reference/torch_max.html
index 8ac04c16a3..f0373826d1 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Max</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Max</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_max.Rd</code></div>
     </div>
 
@@ -91,23 +73,25 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>out</dt>
+<dt id="arg-out">out<a class="anchor" aria-label="anchor" href="#arg-out"></a></dt>
 <dd><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
@@ -118,14 +102,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="max-input-gt-tensor-">max(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#max-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the maximum value of all elements in the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">max(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the maximum
@@ -135,7 +119,7 @@ <h2 id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">max(input, d
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p><code>indices</code> does not necessarily contain the first occurrence of each
 maximal value found, unless it is unique.
@@ -148,7 +132,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     </div>
     <div class="section level2">
     <h2 id="max-input-other-out-null-gt-tensor-">max(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#max-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Each element of the tensor <code>input</code> is compared with the corresponding
@@ -181,14 +165,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_max</span><span class="op">(</span><span class="va">a</span>, other <span class="op">=</span> <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1791</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9259</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0413</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1109</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3014</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4046</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2867</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0281</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -197,14 +181,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_maximum.html b/reference/torch_maximum.html
index fdc46434c6..c9c8740e4d 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Maximum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Maximum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_maximum.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="maximum-input-other-out-none-gt-tensor-">maximum(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#maximum-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise maximum of <code>input</code> and <code>other</code>.</p>
@@ -131,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_mean.html b/reference/torch_mean.html
index f9b2720579..622f27a721 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Mean — torch_mean • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Mean — torch_mean"><meta name="description" content="Mean"><meta property="og:description" content="Mean"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mean</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Mean</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_mean.Rd</code></div>
     </div>
 
@@ -95,32 +77,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>the resulting data type.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="mean-input-gt-tensor-">mean(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mean-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the mean value of all elements in the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="mean-input-dim-keepdim-false-out-null-gt-tensor-">mean(input, dim, keepdim=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mean-input-dim-keepdim-false-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the mean value of each row of the <code>input</code> tensor in the given
@@ -147,11 +131,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_mean</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span>, <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0464  0.1152  0.6736 -0.3284</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4723 -0.2494 -0.0274  0.3934</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_median.html b/reference/torch_median.html
index e3192713a8..d146efa3df 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Median"><title>Median — torch_median • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Median — torch_median"><meta property="og:description" content="Median"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Median</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Median</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_median.Rd</code></div>
     </div>
 
@@ -95,28 +77,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="median-input-gt-tensor-">median(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#median-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the median value of all elements in the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">median(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the median
@@ -144,25 +128,25 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0628</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7662</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1937</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2784</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7597</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5959</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2752</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2743</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4593</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1138</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{5} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -171,14 +155,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_memory_format.html b/reference/torch_memory_format.html
index c577b345a1..860cf2d5b9 100644
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+++ b/reference/torch_memory_format.html
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diff --git a/reference/torch_meshgrid.html b/reference/torch_meshgrid.html
index 72553ce53e..0f5a2ee44c 100644
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Take \(N\) tensors, each of which can be either scalar or 1-dimensional
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+
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_meshgrid.Rd</code></div>
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@@ -101,12 +83,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
 treated (1,).</p></dd>
 
 
-<dt>indexing</dt>
+<dt id="arg-indexing">indexing<a class="anchor" aria-label="anchor" href="#arg-indexing"></a></dt>
 <dd><p>(str, optional): the indexing mode, either “xy” or “ij”, defaults to “ij”.
 See warning for future changes.
 If “xy” is selected, the first dimension corresponds to the cardinality of
@@ -118,7 +102,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>In the future <code>torch_meshgrid</code> will transition to indexing=’xy’ as the default.
 This <a href="https://github.com/pytorch/pytorch/issues/50276" class="external-link">issue</a> tracks this issue
@@ -150,7 +134,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -159,14 +143,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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       <div class="d-none name"><code>torch_min.Rd</code></div>
     </div>
 
@@ -91,23 +73,25 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>out</dt>
+<dt id="arg-out">out<a class="anchor" aria-label="anchor" href="#arg-out"></a></dt>
 <dd><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
@@ -118,14 +102,14 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="min-input-gt-tensor-">min(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#min-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the minimum value of all elements in the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">min(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the minimum
@@ -135,7 +119,7 @@ <h2 id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">min(input, d
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p><code>indices</code> does not necessarily contain the first occurrence of each
 minimal value found, unless it is unique.
@@ -148,7 +132,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     </div>
     <div class="section level2">
     <h2 id="min-input-other-out-null-gt-tensor-">min(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#min-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Each element of the tensor <code>input</code> is compared with the corresponding
@@ -182,14 +166,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_min</span><span class="op">(</span><span class="va">a</span>, other <span class="op">=</span> <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3243</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2041</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7173</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4610</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5141</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.3883</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.1665</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3134</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -198,14 +182,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_minimum.html b/reference/torch_minimum.html
index 800b2f6310..ade0041c0b 100644
--- a/reference/torch_minimum.html
+++ b/reference/torch_minimum.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Minimum"><title>Minimum — torch_minimum • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Minimum — torch_minimum"><meta property="og:description" content="Minimum"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Minimum — torch_minimum • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Minimum — torch_minimum"><meta name="description" content="Minimum"><meta property="og:description" content="Minimum"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Minimum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Minimum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_minimum.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="minimum-input-other-out-none-gt-tensor-">minimum(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#minimum-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise minimum of <code>input</code> and <code>other</code>.</p>
@@ -131,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_mm.html b/reference/torch_mm.html
index c3624eb912..8ab0c07927 100644
--- a/reference/torch_mm.html
+++ b/reference/torch_mm.html
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-      <img src="" class="logo" alt=""><h1>Mm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Mm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_mm.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first matrix to be multiplied</p></dd>
 
 
-<dt>mat2</dt>
+<dt id="arg-mat-">mat2<a class="anchor" aria-label="anchor" href="#arg-mat-"></a></dt>
 <dd><p>(Tensor) the second matrix to be multiplied</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="mm-input-mat-out-null-gt-tensor-">mm(input, mat2, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mm-input-mat-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a matrix multiplication of the matrices <code>input</code> and <code>mat2</code>.</p>
@@ -127,12 +111,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_mm</span><span class="op">(</span><span class="va">mat1</span>, <span class="va">mat2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5435 -0.2961  1.1167</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8410 -0.8920  0.1800</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2545 -0.2052 -1.7691</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3064  1.2126 -2.8038</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index e162bc665a..86db6b3c18 100644
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mode</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Mode</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_mode.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
@@ -113,7 +97,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-">mode(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the mode
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
@@ -147,7 +131,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -156,14 +140,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 9db6a635ef..83c9390f94 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Movedim</h1>
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+
+      <h1>Movedim</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_movedim.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>source</dt>
+<dt id="arg-source">source<a class="anchor" aria-label="anchor" href="#arg-source"></a></dt>
 <dd><p>(int or tuple of ints) Original positions of the dims to move. These must be unique.</p></dd>
 
 
-<dt>destination</dt>
+<dt id="arg-destination">destination<a class="anchor" aria-label="anchor" href="#arg-destination"></a></dt>
 <dd><p>(int or tuple of ints) Destination positions for each of the original dims. These must also be unique.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="movedim-input-source-destination-gt-tensor-">movedim(input, source, destination) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#movedim-input-source-destination-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Moves the dimension(s) of <code>input</code> at the position(s) in <code>source</code>
@@ -131,14 +115,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.4197 -0.3672 -1.5816</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.4617  0.8193  0.2749</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -0.3044 -0.3522  1.1081</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   0.2758  1.4123  0.2071</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,1,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -147,14 +131,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index c157c0e5a2..cf677841fb 100644
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       <div class="d-none name"><code>torch_mul.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first multiplicand tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second multiplicand tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="mul-input-other-out-null-">mul(input, other, out=NULL) <a class="anchor" aria-label="anchor" href="#mul-input-other-out-null-"></a></h2>
-    
+
 
 
 <p>Multiplies each element of the input <code>input</code> with the scalar
@@ -142,14 +126,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_mul</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1375 -1.3661  2.4762 -0.3392</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1289 -0.1549  0.2807 -0.0385</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1674 -0.2011  0.3645 -0.0499</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0937 -0.1126  0.2040 -0.0280</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8229e+00  6.1573e-01 -3.7671e-01  3.9122e-03</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8.1764e-01  2.7618e-01 -1.6897e-01  1.7548e-03</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9.2321e+00  3.1184e+00 -1.9079e+00  1.9814e-02</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.1543e-02 -3.8989e-03  2.3854e-03 -2.4772e-05</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +142,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 4a29252fdd..33ad1258e4 100644
--- a/reference/torch_multinomial.html
+++ b/reference/torch_multinomial.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Multinomial</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Multinomial</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_multinomial.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor containing probabilities</p></dd>
 
 
-<dt>num_samples</dt>
+<dt id="arg-num-samples">num_samples<a class="anchor" aria-label="anchor" href="#arg-num-samples"></a></dt>
 <dd><p>(int) number of samples to draw</p></dd>
 
 
-<dt>replacement</dt>
+<dt id="arg-replacement">replacement<a class="anchor" aria-label="anchor" href="#arg-replacement"></a></dt>
 <dd><p>(bool, optional) whether to draw with replacement or not</p></dd>
 
 
-<dt>generator</dt>
+<dt id="arg-generator">generator<a class="anchor" aria-label="anchor" href="#arg-generator"></a></dt>
 <dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
 
 </dl></div>
@@ -130,7 +114,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-">multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor where each row contains <code>num_samples</code> indices sampled
@@ -154,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_multiply.html b/reference/torch_multiply.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Multiply</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Multiply</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_multiply.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first multiplicand tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second multiplicand tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="multiply-input-other-out-none-">multiply(input, other, *, out=None) <a class="anchor" aria-label="anchor" href="#multiply-input-other-out-none-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_mul.html">torch_mul()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -120,14 +104,14 @@ <h2 id="multiply-input-other-out-none-">multiply(input, other, *, out=None) <a c
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_mv.html b/reference/torch_mv.html
index e60df9c6a9..aa6cbf99b4 100644
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+++ b/reference/torch_mv.html
@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mv</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Mv</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_mv.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) matrix to be multiplied</p></dd>
 
 
-<dt>vec</dt>
+<dt id="arg-vec">vec<a class="anchor" aria-label="anchor" href="#arg-vec"></a></dt>
 <dd><p>(Tensor) vector to be multiplied</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="mv-input-vec-out-null-gt-tensor-">mv(input, vec, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mv-input-vec-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs a matrix-vector product of the matrix <code>input</code> and the vector
@@ -127,12 +111,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_mv</span><span class="op">(</span><span class="va">mat</span>, <span class="va">vec</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5089</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2.4542</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1862</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2170</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_mvlgamma.html b/reference/torch_mvlgamma.html
index b04b8145aa..542f1f13ae 100644
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@@ -1,8 +1,5 @@
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mvlgamma</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Mvlgamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_mvlgamma.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(int) the number of dimensions</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="mvlgamma-input-p-gt-tensor-">mvlgamma(input, p) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#mvlgamma-input-p-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the <code>multivariate log-gamma function &lt;https://en.wikipedia.org/wiki/Multivariate_gamma_function&gt;</code>_) with dimension
@@ -126,12 +110,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_mvlgamma</span><span class="op">(</span><span class="va">a</span>, <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3956  0.4255  0.3985</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4307  0.3940  0.4207</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7204  0.4106  0.3954</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4121  0.6110  0.3928</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_nanquantile.html b/reference/torch_nanquantile.html
index b8ec993117..d84b4acda5 100644
--- a/reference/torch_nanquantile.html
+++ b/reference/torch_nanquantile.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Nanquantile</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Nanquantile</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_nanquantile.Rd</code></div>
     </div>
 
@@ -101,29 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>q</dt>
+<dt id="arg-q">q<a class="anchor" aria-label="anchor" href="#arg-q"></a></dt>
 <dd><p>(float or Tensor) a scalar or 1D tensor of quantile values in the range <code>[0, 1]</code></p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>interpolation</dt>
+<dt id="arg-interpolation">interpolation<a class="anchor" aria-label="anchor" href="#arg-interpolation"></a></dt>
 <dd><p>The interpolation method.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-">nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>This is a variant of <code><a href="torch_quantile.html">torch_quantile()</a></code> that "ignores" <code>NaN</code> values,
@@ -151,7 +135,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_nansum.html b/reference/torch_nansum.html
index 9296e2b241..8eb51760ac 100644
--- a/reference/torch_nansum.html
+++ b/reference/torch_nansum.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Nansum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Nansum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_nansum.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>the desired data type of returned tensor. If specified, the
 input tensor is casted to dtype before the operation is performed. This is
 useful for preventing data type overflows. Default: <code>NULL</code>.</p></dd>
@@ -115,14 +99,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="nansum-input-dtype-none-gt-tensor-">nansum(input, *, dtype=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nansum-input-dtype-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the sum of all elements, treating Not a Numbers (NaNs) as zero.</p>
     </div>
     <div class="section level2">
     <h2 id="nansum-input-dim-keepdim-false-dtype-none-gt-tensor-">nansum(input, dim, keepdim=FALSE, *, dtype=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nansum-input-dim-keepdim-false-dtype-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the sum of each row of the <code>input</code> tensor in the given
@@ -154,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_narrow.html b/reference/torch_narrow.html
index 81ceb7ad68..d7bdc0366c 100644
--- a/reference/torch_narrow.html
+++ b/reference/torch_narrow.html
@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Narrow</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Narrow</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_narrow.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to narrow</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension along which to narrow</p></dd>
 
 
-<dt>start</dt>
+<dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>(int) the starting dimension</p></dd>
 
 
-<dt>length</dt>
+<dt id="arg-length">length<a class="anchor" aria-label="anchor" href="#arg-length"></a></dt>
 <dd><p>(int) the distance to the ending dimension</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="narrow-input-dim-start-length-gt-tensor-">narrow(input, dim, start, length) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor that is a narrowed version of <code>input</code> tensor. The
@@ -136,7 +120,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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index aaaf1efe4b..5e7abe4256 100644
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Ne</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Ne</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_ne.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="ne-input-other-out-null-gt-tensor-">ne(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ne-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes \(input \neq other\) element-wise.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_neg.html b/reference/torch_neg.html
index 83ce9e8653..03f6a22398 100644
--- a/reference/torch_neg.html
+++ b/reference/torch_neg.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Neg"><title>Neg — torch_neg • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Neg — torch_neg"><meta property="og:description" content="Neg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Neg — torch_neg • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Neg — torch_neg"><meta name="description" content="Neg"><meta property="og:description" content="Neg"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Neg</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Neg</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_neg.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="neg-input-out-null-gt-tensor-">neg(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#neg-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the negative of the elements of <code>input</code>.</p>
@@ -119,15 +103,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_neg</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3203</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3018</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2356</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0700</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9773</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1485</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3309</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7101</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6614</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
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@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_negative.html b/reference/torch_negative.html
index 1447f8bc61..1dbee2f4de 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Negative</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Negative</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_negative.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="negative-input-out-none-gt-tensor-">negative(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#negative-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_neg.html">torch_neg()</a></code></p>
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@@ -116,14 +100,14 @@ <h2 id="negative-input-out-none-gt-tensor-">negative(input, *, out=None) -&gt; T
 </div>
 
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diff --git a/reference/torch_nextafter.html b/reference/torch_nextafter.html
index ecdc6f64a8..40f4218bbf 100644
--- a/reference/torch_nextafter.html
+++ b/reference/torch_nextafter.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Nextafter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Nextafter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_nextafter.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the first input tensor</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) the second input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="nextafter-input-other-out-none-gt-tensor-">nextafter(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#nextafter-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Return the next floating-point value after <code>input</code> towards <code>other</code>, elementwise.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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 </div>
 
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index 363a24ebb2..e0305ff69e 100644
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-      <img src="" class="logo" alt=""><h1>Nonzero</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Nonzero</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_nonzero.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>as_list</dt>
+<dt id="arg-as-list">as_list<a class="anchor" aria-label="anchor" href="#arg-as-list"></a></dt>
 <dd><p>If <code>FALSE</code>, the output tensor containing indices. If <code>TRUE</code>, one
 1-D tensor for each dimension, containing the indices of each nonzero element
 along that dimension.</p>
@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4,1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_norm.html b/reference/torch_norm.html
index f2d88f5f8a..f57b78ea64 100644
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Norm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Norm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_norm.Rd</code></div>
     </div>
 
@@ -95,30 +77,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>p</dt>
-<dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></dd>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
+<dd><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                –        'nuc'  nuclear norm                  –        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>NULL</code> and        <code>out</code> = <code>NULL</code>. Default: <code>FALSE</code>
 Ignored if        <code>dim</code> = <code>NULL</code> and <code>out</code> = <code>NULL</code>.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Returns the matrix norm or vector norm of a given tensor.</p>
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
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diff --git a/reference/torch_normal.html b/reference/torch_normal.html
index 143d2af1fe..871a4bb930 100644
--- a/reference/torch_normal.html
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@@ -1,10 +1,7 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Normal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Normal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_normal.Rd</code></div>
     </div>
 
@@ -98,30 +80,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mean</dt>
+
+
+<dl><dt id="arg-mean">mean<a class="anchor" aria-label="anchor" href="#arg-mean"></a></dt>
 <dd><p>(tensor or scalar double) Mean of the normal distribution.
 If this is a <code><a href="torch_tensor.html">torch_tensor()</a></code> then the output has the same dim as <code>mean</code>
 and it represents the per-element mean. If it's a scalar value, it's reused
 for all elements.</p></dd>
 
 
-<dt>std</dt>
+<dt id="arg-std">std<a class="anchor" aria-label="anchor" href="#arg-std"></a></dt>
 <dd><p>(tensor or scalar double) The standard deviation of the normal
 distribution. If this is a <code><a href="torch_tensor.html">torch_tensor()</a></code> then the output has the same size as <code>std</code>
 and it represents the per-element standard deviation. If it's a scalar value,
 it's reused for all elements.</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(integers, optional) only used if both <code>mean</code> and <code>std</code> are scalars.</p></dd>
 
 
-<dt>generator</dt>
+<dt id="arg-generator">generator<a class="anchor" aria-label="anchor" href="#arg-generator"></a></dt>
 <dd><p>a random number generator created with <code><a href="torch_generator.html">torch_generator()</a></code>. If <code>NULL</code>
 a default generator is used.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Tensor option parameters like <code>dtype</code>, <code>layout</code>, and <code>device</code>.
 Can only be used when <code>mean</code> and <code>std</code> are both scalar numerics.</p></dd>
 
@@ -133,7 +117,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="normal-mean-std-gt-tensor-">normal(mean, std, *) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor of random numbers drawn from separate normal distributions
@@ -147,7 +131,7 @@ <h2 id="normal-mean-std-gt-tensor-">normal(mean, std, *) -&gt; Tensor <a class="
     </div>
     <div class="section level2">
     <h2 id="normal-mean-std-gt-tensor--1">normal(mean=0.0, std) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-gt-tensor--1"></a></h2>
-    
+
 
 
 <p>Similar to the function above, but the means are shared among all drawn
@@ -155,7 +139,7 @@ <h2 id="normal-mean-std-gt-tensor--1">normal(mean=0.0, std) -&gt; Tensor <a clas
     </div>
     <div class="section level2">
     <h2 id="normal-mean-std-gt-tensor--2">normal(mean, std=1.0) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-gt-tensor--2"></a></h2>
-    
+
 
 
 <p>Similar to the function above, but the standard-deviations are shared among
@@ -163,7 +147,7 @@ <h2 id="normal-mean-std-gt-tensor--2">normal(mean, std=1.0) -&gt; Tensor <a clas
     </div>
     <div class="section level2">
     <h2 id="normal-mean-std-size-gt-tensor-">normal(mean, std, size, *) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#normal-mean-std-size-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Similar to the function above, but the means and standard deviations are shared
@@ -181,11 +165,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0455  2.4500  3.9270  5.6783</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2658 -0.3548  1.2976  1.0859</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -194,14 +178,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_not_equal.html b/reference/torch_not_equal.html
index a7d42a920c..d3745ce4a9 100644
--- a/reference/torch_not_equal.html
+++ b/reference/torch_not_equal.html
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-      <img src="" class="logo" alt=""><h1>Not_equal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Not_equal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_not_equal.Rd</code></div>
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@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to compare</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the tensor or value to compare</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="not-equal-input-other-out-none-gt-tensor-">not_equal(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#not-equal-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_ne.html">torch_ne()</a></code>.</p>
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@@ -120,14 +104,14 @@ <h2 id="not-equal-input-other-out-none-gt-tensor-">not_equal(input, other, *, ou
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diff --git a/reference/torch_ones.html b/reference/torch_ones.html
index 5582b2999b..f84f7a398b 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Ones</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Ones</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_ones.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>optional names for the dimensions</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with the scalar value <code>1</code>, with the shape defined
@@ -151,7 +135,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/torch_ones_like.html b/reference/torch_ones_like.html
index b8118c1aec..77cf51c6d9 100644
--- a/reference/torch_ones_like.html
+++ b/reference/torch_ones_like.html
@@ -1,8 +1,5 @@
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-<li class="nav-item">
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-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Ones_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Ones_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_ones_like.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with the scalar value <code>1</code>, with the same size as
@@ -137,7 +121,7 @@ <h2 id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-m
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
 the old <code>torch_ones_like(input, out=output)</code> is equivalent to
@@ -157,7 +141,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -166,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_orgqr.html b/reference/torch_orgqr.html
index 298c117934..46b24e5ddc 100644
--- a/reference/torch_orgqr.html
+++ b/reference/torch_orgqr.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Orgqr"><title>Orgqr — torch_orgqr • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Orgqr — torch_orgqr"><meta property="og:description" content="Orgqr"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Orgqr — torch_orgqr • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Orgqr — torch_orgqr"><meta name="description" content="Orgqr"><meta property="og:description" content="Orgqr"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
   window.dataLayer = window.dataLayer || [];
   function gtag(){dataLayer.push(arguments);}
   gtag('js', new Date());
@@ -10,77 +7,62 @@
   gtag('config', 'G-9ZJSKW3L0N');
 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
-    
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-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
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+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Orgqr</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Orgqr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_orgqr.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the <code>a</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
 
 
-<dt>input2</dt>
+<dt id="arg-input-">input2<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>(Tensor) the <code>tau</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="orgqr-input-input-gt-tensor-">orgqr(input, input2) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#orgqr-input-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the orthogonal matrix <code>Q</code> of a QR factorization, from the <code>(input, input2)</code>
@@ -114,7 +98,7 @@ <h2 id="orgqr-input-input-gt-tensor-">orgqr(input, input2) -&gt; Tensor <a class
 See <code>LAPACK documentation for orgqr</code>_ for further details.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -123,14 +107,14 @@ <h2 id="orgqr-input-input-gt-tensor-">orgqr(input, input2) -&gt; Tensor <a class
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_ormqr.html b/reference/torch_ormqr.html
index 6816acc15f..7a5ee5516e 100644
--- a/reference/torch_ormqr.html
+++ b/reference/torch_ormqr.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Ormqr"><title>Ormqr — torch_ormqr • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Ormqr — torch_ormqr"><meta property="og:description" content="Ormqr"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Ormqr</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Ormqr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_ormqr.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the <code>a</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
 
 
-<dt>input2</dt>
+<dt id="arg-input-">input2<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>(Tensor) the <code>tau</code> from <code><a href="torch_geqrf.html">torch_geqrf</a></code>.</p></dd>
 
 
-<dt>input3</dt>
+<dt id="arg-input-">input3<a class="anchor" aria-label="anchor" href="#arg-input-"></a></dt>
 <dd><p>(Tensor) the matrix to be multiplied.</p></dd>
 
 
-<dt>left</dt>
+<dt id="arg-left">left<a class="anchor" aria-label="anchor" href="#arg-left"></a></dt>
 <dd><p>see LAPACK documentation</p></dd>
 
 
-<dt>transpose</dt>
+<dt id="arg-transpose">transpose<a class="anchor" aria-label="anchor" href="#arg-transpose"></a></dt>
 <dd><p>see LAPACK documentation</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-">ormqr(input, input2, input3, left=TRUE, transpose=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Multiplies <code>mat</code> (given by <code>input3</code>) by the orthogonal <code>Q</code> matrix of the QR factorization
@@ -125,7 +109,7 @@ <h2 id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-">ormqr(inpu
 <p>This directly calls the underlying LAPACK function <code>?ormqr</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -134,14 +118,14 @@ <h2 id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-">ormqr(inpu
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_outer.html b/reference/torch_outer.html
index 353aa9b47a..6ac4c43cc3 100644
--- a/reference/torch_outer.html
+++ b/reference/torch_outer.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Outer"><title>Outer — torch_outer • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Outer — torch_outer"><meta property="og:description" content="Outer"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Outer</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Outer</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_outer.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) 1-D input vector</p></dd>
 
 
-<dt>vec2</dt>
+<dt id="arg-vec-">vec2<a class="anchor" aria-label="anchor" href="#arg-vec-"></a></dt>
 <dd><p>(Tensor) 1-D input vector</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="outer-input-vec-out-none-gt-tensor-">outer(input, vec2, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#outer-input-vec-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Outer product of <code>input</code> and <code>vec2</code>.
@@ -134,7 +118,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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index cd9b7a0b79..41d2902069 100644
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+
+      <h1>Pdist</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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     <div class="section level2">
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-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>NA input tensor of shape \(N \times M\).</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="pdist-input-p-gt-tensor-">pdist(input, p=2) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pdist-input-p-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the p-norm distance between every pair of row vectors in the input.
@@ -120,7 +104,7 @@ <h2 id="pdist-input-p-gt-tensor-">pdist(input, p=2) -&gt; Tensor <a class="ancho
 <code>scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -129,14 +113,14 @@ <h2 id="pdist-input-p-gt-tensor-">pdist(input, p=2) -&gt; Tensor <a class="ancho
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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+
+      <h1>Pinverse</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_pinverse.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></dd>
 
 
-<dt>rcond</dt>
+<dt id="arg-rcond">rcond<a class="anchor" aria-label="anchor" href="#arg-rcond"></a></dt>
 <dd><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></dd>
 
 </dl></div>
@@ -114,7 +98,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="pinverse-input-rcond-e-gt-tensor-">pinverse(input, rcond=1e-15) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
@@ -135,18 +119,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (1,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000e+00 -4.2841e-08 -8.9407e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -8.3819e-09  1.0000e+00 -1.3411e-07</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.0245e-07 -9.6858e-08  1.0000e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000e+00 -8.9407e-08 -2.9802e-08</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -4.4703e-08  1.0000e+00 -1.3039e-07</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -7.4506e-09 -2.2352e-08  1.0000e+00</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> (2,.,.) = </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000e+00 -3.4459e-07  9.6858e-08</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -1.7881e-07  1.0000e+00 -2.4587e-07</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   1.4901e-07  1.7043e-07  1.0000e+00</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   1.0000e+00  4.8429e-08 -5.9605e-08</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  -5.9605e-08  1.0000e+00  2.3097e-07</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   3.8743e-07  5.7742e-08  1.0000e+00</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -155,14 +139,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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index 7a41718d8a..91c578059d 100644
--- a/reference/torch_pixel_shuffle.html
+++ b/reference/torch_pixel_shuffle.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pixel_shuffle — torch_pixel_shuffle • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pixel_shuffle — torch_pixel_shuffle"><meta name="description" content="Pixel_shuffle"><meta property="og:description" content="Pixel_shuffle"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Pixel_shuffle</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Pixel_shuffle</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_pixel_shuffle.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>upscale_factor</dt>
+<dt id="arg-upscale-factor">upscale_factor<a class="anchor" aria-label="anchor" href="#arg-upscale-factor"></a></dt>
 <dd><p>(int) factor to increase spatial resolution by</p></dd>
 
 </dl></div>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1]  1  1 12 12</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -131,14 +115,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_poisson.html b/reference/torch_poisson.html
index 67e78bf7bb..5367ff7565 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Poisson</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Poisson</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_poisson.Rd</code></div>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></dd>
 
 
-<dt>generator</dt>
+<dt id="arg-generator">generator<a class="anchor" aria-label="anchor" href="#arg-generator"></a></dt>
 <dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="poisson-input-generator-null-gt-tensor-">poisson(input *, generator=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#poisson-input-generator-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor of the same size as <code>input</code> with each element
@@ -124,14 +108,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_poisson</span><span class="op">(</span><span class="va">rates</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3  0  2  2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  7  0  3  3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3  4  4  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2  5  2  4</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  0  3  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  5  6  0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1  6</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -140,14 +124,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_polar.html b/reference/torch_polar.html
index 0b6c9ae89d..6988c7b24e 100644
--- a/reference/torch_polar.html
+++ b/reference/torch_polar.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Polar</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Polar</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_polar.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>abs</dt>
+
+
+<dl><dt id="arg-abs">abs<a class="anchor" aria-label="anchor" href="#arg-abs"></a></dt>
 <dd><p>(Tensor) The absolute value the complex tensor. Must be float or
 double.</p></dd>
 
 
-<dt>angle</dt>
+<dt id="arg-angle">angle<a class="anchor" aria-label="anchor" href="#arg-angle"></a></dt>
 <dd><p>(Tensor) The angle of the complex tensor. Must be same dtype as
 <code>abs</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="polar-abs-angle-out-none-gt-tensor-">polar(abs, angle, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#polar-abs-angle-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Constructs a complex tensor whose elements are Cartesian coordinates
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUComplexDoubleType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_polygamma.html b/reference/torch_polygamma.html
index 44c4ab56a2..0580dfd57c 100644
--- a/reference/torch_polygamma.html
+++ b/reference/torch_polygamma.html
@@ -1,8 +1,5 @@
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+    <li><a class="dropdown-item" href="../articles/examples/basic-nn-module.html">basic-nn-module</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Polygamma</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Polygamma</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_polygamma.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>n</dt>
+
+
+<dl><dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) the order of the polygamma function</p></dd>
 
 
-<dt>input</dt>
+<dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="polygamma-n-input-out-null-gt-tensor-">polygamma(n, input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#polygamma-n-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the \(n^{th}\) derivative of the digamma function on <code>input</code>.
@@ -122,14 +106,14 @@ <h2 id="polygamma-n-input-out-null-gt-tensor-">polygamma(n, input, out=NULL) -&g
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_polygamma</span><span class="op">(</span><span class="fl">1</span>, <span class="va">a</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +122,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_pow.html b/reference/torch_pow.html
index 5529cf2312..a6fbeff03a 100644
--- a/reference/torch_pow.html
+++ b/reference/torch_pow.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Pow"><title>Pow — torch_pow • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pow — torch_pow"><meta property="og:description" content="Pow"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pow — torch_pow • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Pow — torch_pow"><meta name="description" content="Pow"><meta property="og:description" content="Pow"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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   gtag('js', new Date());
@@ -10,77 +7,62 @@
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+    <li><h6 class="dropdown-header" data-toc-skip>Tensors</h6></li>
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+    <li><a class="dropdown-item" href="../articles/examples/basic-nn-module.html">basic-nn-module</a></li>
+    <li><a class="dropdown-item" href="../articles/examples/dataset.html">dataset</a></li>
+  </ul></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
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+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Pow</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Pow</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_pow.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(float) the scalar base value for the power operation</p></dd>
 
 
-<dt>exponent</dt>
+<dt id="arg-exponent">exponent<a class="anchor" aria-label="anchor" href="#arg-exponent"></a></dt>
 <dd><p>(float or tensor) the exponent value</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="pow-input-exponent-out-null-gt-tensor-">pow(input, exponent, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pow-input-exponent-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Takes the power of each element in <code>input</code> with <code>exponent</code> and
@@ -125,7 +109,7 @@ <h2 id="pow-input-exponent-out-null-gt-tensor-">pow(input, exponent, out=NULL) -
     </div>
     <div class="section level2">
     <h2 id="pow-self-exponent-out-null-gt-tensor-">pow(self, exponent, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#pow-self-exponent-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p><code>self</code> is a scalar <code>float</code> value, and <code>exponent</code> is a tensor.
@@ -163,7 +147,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -172,14 +156,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_preserve_format.html b/reference/torch_preserve_format.html
new file mode 100644
index 0000000000..eb6204681e
--- /dev/null
+++ b/reference/torch_preserve_format.html
@@ -0,0 +1,8 @@
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diff --git a/reference/torch_prod.html b/reference/torch_prod.html
index 21d8fe1d6d..9f27b149e7 100644
--- a/reference/torch_prod.html
+++ b/reference/torch_prod.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Prod — torch_prod • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Prod — torch_prod"><meta name="description" content="Prod"><meta property="og:description" content="Prod"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Prod</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Prod</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_prod.Rd</code></div>
     </div>
 
@@ -95,32 +77,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="prod-input-dtype-null-gt-tensor-">prod(input, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#prod-input-dtype-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the product of all elements in the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="prod-input-dim-keepdim-false-dtype-null-gt-tensor-">prod(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#prod-input-dim-keepdim-false-dtype-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the product of each row of the <code>input</code> tensor in the given
@@ -145,12 +129,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_prod</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0789</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6899</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 0.001 *</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.7749</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -91.8498</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -159,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_promote_types.html b/reference/torch_promote_types.html
index fac197266a..af0f48c6f6 100644
--- a/reference/torch_promote_types.html
+++ b/reference/torch_promote_types.html
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-      <img src="" class="logo" alt=""><h1>Promote_types</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Promote_types</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>type1</dt>
+
+
+<dl><dt id="arg-type-">type1<a class="anchor" aria-label="anchor" href="#arg-type-"></a></dt>
 <dd><p>(<code>torch.dtype</code>)</p></dd>
 
 
-<dt>type2</dt>
+<dt id="arg-type-">type2<a class="anchor" aria-label="anchor" href="#arg-type-"></a></dt>
 <dd><p>(<code>torch.dtype</code>)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="promote-types-type-type-gt-dtype-">promote_types(type1, type2) -&gt; dtype <a class="anchor" aria-label="anchor" href="#promote-types-type-type-gt-dtype-"></a></h2>
-    
+
 
 
 <p>Returns the <code>torch_dtype</code> with the smallest size and scalar kind that is
@@ -124,7 +108,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_Long</span>
 </code></pre></div>
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diff --git a/reference/torch_qint32.html b/reference/torch_qint32.html
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index ea2c081c45..55101533e7 100644
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Qr</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Qr</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_qr.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></dd>
 
 
-<dt>some</dt>
+<dt id="arg-some">some<a class="anchor" aria-label="anchor" href="#arg-some"></a></dt>
 <dd><p>(bool, optional) Set to <code>TRUE</code> for reduced QR decomposition and <code>FALSE</code> for                complete QR decomposition.</p></dd>
 
 </dl></div>
@@ -113,7 +97,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="qr-input-some-true-out-null-gt-tensor-tensor-">qr(input, some=TRUE, out=NULL) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#qr-input-some-true-out-null-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the QR decomposition of a matrix or a batch of matrices <code>input</code>,
@@ -136,13 +120,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="torch_mm.html">torch_mm</a></span><span class="op">(</span><span class="va">q</span><span class="op">$</span><span class="fu">t</span><span class="op">(</span><span class="op">)</span>, <span class="va">q</span><span class="op">)</span><span class="op">$</span><span class="fu">round</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1  0  0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0  1  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1 -0  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0  1  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0  0  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -151,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_qscheme.html b/reference/torch_qscheme.html
index 574ba66357..a29e623041 100644
--- a/reference/torch_qscheme.html
+++ b/reference/torch_qscheme.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Creates the corresponding Scheme object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/qscheme.R" class="external-link"><code>R/qscheme.R</code></a></small>
+
+      <h1>Creates the corresponding Scheme object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/qscheme.R" class="external-link"><code>R/qscheme.R</code></a></small>
       <div class="d-none name"><code>torch_qscheme.Rd</code></div>
     </div>
 
@@ -108,14 +90,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 </div>
 
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index 540640e10c..c623ea9234 100644
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-      <img src="" class="logo" alt=""><h1>Quantile</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Quantile</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_quantile.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>q</dt>
+<dt id="arg-q">q<a class="anchor" aria-label="anchor" href="#arg-q"></a></dt>
 <dd><p>(float or Tensor) a scalar or 1D tensor of quantile values in the range <code>[0, 1]</code></p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>interpolation</dt>
+<dt id="arg-interpolation">interpolation<a class="anchor" aria-label="anchor" href="#arg-interpolation"></a></dt>
 <dd><p>The interpolation method.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="quantile-input-q-gt-tensor-">quantile(input, q) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantile-input-q-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the q-th quantiles of all elements in the <code>input</code> tensor, doing a linear
@@ -125,7 +109,7 @@ <h2 id="quantile-input-q-gt-tensor-">quantile(input, q) -&gt; Tensor <a class="a
     </div>
     <div class="section level2">
     <h2 id="quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-">quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the q-th quantiles of each row of the <code>input</code> tensor along the dimension
@@ -158,7 +142,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3 1 3</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -167,14 +151,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-      <img src="" class="logo" alt=""><h1>Quantize_per_channel</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Quantize_per_channel</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_quantize_per_channel.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) float tensor to quantize</p></dd>
 
 
-<dt>scales</dt>
+<dt id="arg-scales">scales<a class="anchor" aria-label="anchor" href="#arg-scales"></a></dt>
 <dd><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></dd>
 
 
-<dt>zero_points</dt>
+<dt id="arg-zero-points">zero_points<a class="anchor" aria-label="anchor" href="#arg-zero-points"></a></dt>
 <dd><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></dd>
 
 
-<dt>axis</dt>
+<dt id="arg-axis">axis<a class="anchor" aria-label="anchor" href="#arg-axis"></a></dt>
 <dd><p>(int) dimension on which apply per-channel quantization</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-">quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p>
@@ -138,7 +122,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUByteType{2,2} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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 </div>
 
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index 76a14b931d..db64bf8cba 100644
--- a/reference/torch_quantize_per_tensor.html
+++ b/reference/torch_quantize_per_tensor.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Quantize_per_tensor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Quantize_per_tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_quantize_per_tensor.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) float tensor to quantize</p></dd>
 
 
-<dt>scale</dt>
+<dt id="arg-scale">scale<a class="anchor" aria-label="anchor" href="#arg-scale"></a></dt>
 <dd><p>(float) scale to apply in quantization formula</p></dd>
 
 
-<dt>zero_point</dt>
+<dt id="arg-zero-point">zero_point<a class="anchor" aria-label="anchor" href="#arg-zero-point"></a></dt>
 <dd><p>(int) offset in integer value that maps to float zero</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-">quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Converts a float tensor to quantized tensor with given scale and zero point.</p>
@@ -133,7 +117,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUByteType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/torch_quint8.html b/reference/torch_quint8.html
new file mode 100644
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@@ -0,0 +1,8 @@
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diff --git a/reference/torch_rad2deg.html b/reference/torch_rad2deg.html
index c14c372c50..b714162445 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Rad2deg</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Rad2deg</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_rad2deg.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="rad-deg-input-out-none-gt-tensor-">rad2deg(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rad-deg-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with each of the elements of <code>input</code>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -131,14 +115,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_rand.html b/reference/torch_rand.html
index 28847e429b..64d6cd0ca8 100644
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-      <img src="" class="logo" alt=""><h1>Rand</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Rand</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_rand.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>optional dimension names</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with random numbers from a uniform distribution
@@ -144,12 +128,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_rand</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8802  0.0500  0.8957</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2329  0.6650  0.5580</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8258  0.3044  0.1683</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7110  0.5204  0.0257</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +142,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_rand_like.html b/reference/torch_rand_like.html
index 5881596cdf..a111bc20d6 100644
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@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Rand_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Rand_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_rand_like.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor with the same size as <code>input</code> that is filled with
@@ -137,7 +121,7 @@ <h2 id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-m
 <code>torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-m
 </div>
 
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diff --git a/reference/torch_randint.html b/reference/torch_randint.html
index 0dadc3be5e..32b7c29559 100644
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+++ b/reference/torch_randint.html
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randint</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Randint</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_randint.Rd</code></div>
     </div>
 
@@ -105,45 +87,47 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>low</dt>
+
+
+<dl><dt id="arg-low">low<a class="anchor" aria-label="anchor" href="#arg-low"></a></dt>
 <dd><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></dd>
 
 
-<dt>high</dt>
+<dt id="arg-high">high<a class="anchor" aria-label="anchor" href="#arg-high"></a></dt>
 <dd><p>(int) One above the highest integer to be drawn from the distribution.</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(tuple) a tuple defining the shape of the output tensor.</p></dd>
 
 
-<dt>generator</dt>
+<dt id="arg-generator">generator<a class="anchor" aria-label="anchor" href="#arg-generator"></a></dt>
 <dd><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>memory format for the resulting tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="randint-low-high-size-generator-null-out-null-">randint(low=0, high, size, *, generator=NULL, out=NULL, \ <a class="anchor" aria-label="anchor" href="#randint-low-high-size-generator-null-out-null-"></a></h2>
-    
+
 
 
 <p>dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor</p>
@@ -164,12 +148,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_randint</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  6  8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  5  7</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  6  9</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  9  9</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -178,14 +162,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_randint_like.html b/reference/torch_randint_like.html
index 4625f6f19a..82587ac084 100644
--- a/reference/torch_randint_like.html
+++ b/reference/torch_randint_like.html
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randint_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Randint_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_randint_like.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>low</dt>
+<dt id="arg-low">low<a class="anchor" aria-label="anchor" href="#arg-low"></a></dt>
 <dd><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></dd>
 
 
-<dt>high</dt>
+<dt id="arg-high">high<a class="anchor" aria-label="anchor" href="#arg-high"></a></dt>
 <dd><p>(int) One above the highest integer to be drawn from the distribution.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-">randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, <a class="anchor" aria-label="anchor" href="#randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a></h2>
-    
+
 
 
 <p>memory_format=torch.preserve_format) -&gt; Tensor</p>
@@ -145,7 +129,7 @@ <h2 id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-
 a tensor with dtype <code>torch_int64</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -154,14 +138,14 @@ <h2 id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_randn.html b/reference/torch_randn.html
index d1acea7759..8956bb8664 100644
--- a/reference/torch_randn.html
+++ b/reference/torch_randn.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randn</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Randn</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_randn.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>optional names for the dimensions</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with random numbers from a normal distribution
@@ -148,12 +132,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_randn</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8321  0.3774 -1.2515</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1037  2.6689  0.0240</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3322 -0.4046 -0.0668</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2351  1.0560  0.8915</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -162,14 +146,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_randn_like.html b/reference/torch_randn_like.html
index 274c194737..9fa899b500 100644
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@@ -1,8 +1,5 @@
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randn_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Randn_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_randn_like.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor with the same size as <code>input</code> that is filled with
@@ -137,7 +121,7 @@ <h2 id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-
 <code>torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -146,14 +130,14 @@ <h2 id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_randperm.html b/reference/torch_randperm.html
index 5ed9246972..aa74c83b3d 100644
--- a/reference/torch_randperm.html
+++ b/reference/torch_randperm.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Randperm"><title>Randperm — torch_randperm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Randperm — torch_randperm"><meta property="og:description" content="Randperm"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Randperm — torch_randperm • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Randperm — torch_randperm"><meta name="description" content="Randperm"><meta property="og:description" content="Randperm"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Randperm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Randperm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_randperm.Rd</code></div>
     </div>
 
@@ -101,29 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>n</dt>
+
+
+<dl><dt id="arg-n">n<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int) the upper bound (exclusive)</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-">randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -&gt; LongTensor <a class="anchor" aria-label="anchor" href="#randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns a random permutation of integers from <code>0</code> to <code>n - 1</code>.</p>
@@ -137,13 +121,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  3</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +136,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_range.html b/reference/torch_range.html
index 35f25432af..8f6aa6b0e5 100644
--- a/reference/torch_range.html
+++ b/reference/torch_range.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Range"><title>Range — torch_range • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Range — torch_range"><meta property="og:description" content="Range"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Range — torch_range • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Range — torch_range"><meta name="description" content="Range"><meta property="og:description" content="Range"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Range</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Range</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_range.Rd</code></div>
     </div>
 
@@ -103,37 +85,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>start</dt>
+
+
+<dl><dt id="arg-start">start<a class="anchor" aria-label="anchor" href="#arg-start"></a></dt>
 <dd><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></dd>
 
 
-<dt>end</dt>
+<dt id="arg-end">end<a class="anchor" aria-label="anchor" href="#arg-end"></a></dt>
 <dd><p>(float) the ending value for the set of points</p></dd>
 
 
-<dt>step</dt>
+<dt id="arg-step">step<a class="anchor" aria-label="anchor" href="#arg-step"></a></dt>
 <dd><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a 1-D tensor of size \(\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1\)
@@ -145,7 +129,7 @@ <h2 id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-nul
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>This function is deprecated in favor of <code><a href="torch_arange.html">torch_arange</a></code>.</p>
     </div>
@@ -170,7 +154,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{7} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -179,14 +163,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_real.html b/reference/torch_real.html
index 9b4037d3d4..9388caef9c 100644
--- a/reference/torch_real.html
+++ b/reference/torch_real.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Real"><title>Real — torch_real • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Real — torch_real"><meta property="og:description" content="Real"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Real — torch_real • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Real — torch_real"><meta name="description" content="Real"><meta property="og:description" content="Real"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Real</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Real</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_real.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="real-input-gt-tensor-">real(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#real-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the real part of the <code>input</code> tensor. If
@@ -110,7 +94,7 @@ <h2 id="real-input-gt-tensor-">real(input) -&gt; Tensor <a class="anchor" aria-l
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>Not yet implemented for complex tensors.</p>
 <p>$$
@@ -121,13 +105,13 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="fu">torch_real</span><span class="op">(</span><span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1i</span>, <span class="op">-</span><span class="fl">2</span> <span class="op">+</span> <span class="fl">2i</span>, <span class="fl">3</span> <span class="op">-</span> <span class="fl">3i</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_reciprocal.html b/reference/torch_reciprocal.html
index d33cb960d4..a1f0867df6 100644
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-      <img src="" class="logo" alt=""><h1>Reciprocal</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Reciprocal</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_reciprocal.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="reciprocal-input-out-null-gt-tensor-">reciprocal(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#reciprocal-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the reciprocal of the elements of <code>input</code></p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_reciprocal</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  -12.5890</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    1.9890</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  162.2891</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    1.2674</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.5504</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -2.8364</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -30.0044</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  8.3571</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_reduction.html b/reference/torch_reduction.html
index a828b5bdcc..9b068000a5 100644
--- a/reference/torch_reduction.html
+++ b/reference/torch_reduction.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Creates the reduction objet</h1>
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       <div class="d-none name"><code>torch_reduction.Rd</code></div>
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-      <img src="" class="logo" alt=""><h1>Relu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Relu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
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-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="relu-input-gt-tensor-">relu(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#relu-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the relu tranformation.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="relu-input-gt-tensor-">relu(input) -&gt; Tensor <a class="anchor" aria-l
 </div>
 
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diff --git a/reference/torch_relu_.html b/reference/torch_relu_.html
index 53d5ed21fc..496503fb67 100644
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-      <img src="" class="logo" alt=""><h1>Relu_</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Relu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_relu_.Rd</code></div>
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@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="relu-input-gt-tensor-">relu_(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#relu-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>In-place version of <code><a href="torch_relu.html">torch_relu()</a></code>.</p>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="relu-input-gt-tensor-">relu_(input) -&gt; Tensor <a class="anchor" aria-
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diff --git a/reference/torch_remainder.html b/reference/torch_remainder.html
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-      <img src="" class="logo" alt=""><h1>Remainder</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Remainder</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_remainder.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the dividend</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="remainder-input-other-out-null-gt-tensor-">remainder(input, other, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#remainder-input-other-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the element-wise remainder of division.</p>
@@ -131,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
     </div>
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diff --git a/reference/torch_renorm.html b/reference/torch_renorm.html
index 11e8d81f38..acadc5ca73 100644
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Renorm</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Renorm</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_renorm.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>p</dt>
+<dt id="arg-p">p<a class="anchor" aria-label="anchor" href="#arg-p"></a></dt>
 <dd><p>(float) the power for the norm computation</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to slice over to get the sub-tensors</p></dd>
 
 
-<dt>maxnorm</dt>
+<dt id="arg-maxnorm">maxnorm<a class="anchor" aria-label="anchor" href="#arg-maxnorm"></a></dt>
 <dd><p>(float) the maximum norm to keep each sub-tensor under</p></dd>
 
 </dl></div>
@@ -117,7 +101,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="renorm-input-p-dim-maxnorm-out-null-gt-tensor-">renorm(input, p, dim, maxnorm, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#renorm-input-p-dim-maxnorm-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor where each sub-tensor of <code>input</code> along dimension
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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index c234298157..99e621b89c 100644
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+++ b/reference/torch_repeat_interleave.html
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Repeat_interleave</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Repeat_interleave</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_repeat_interleave.Rd</code></div>
     </div>
 
@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>repeats</dt>
+<dt id="arg-repeats">repeats<a class="anchor" aria-label="anchor" href="#arg-repeats"></a></dt>
 <dd><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></dd>
 
 
-<dt>output_size</dt>
+<dt id="arg-output-size">output_size<a class="anchor" aria-label="anchor" href="#arg-output-size"></a></dt>
 <dd><p>(int, optional) – Total output size for the given axis
 ( e.g. sum of repeats). If given, it will avoid stream syncronization needed
 to calculate output shape of the tensor.</p></dd>
@@ -115,21 +99,21 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="repeat-interleave-input-repeats-dim-null-gt-tensor-">repeat_interleave(input, repeats, dim=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#repeat-interleave-input-repeats-dim-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Repeat elements of a tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 
 <p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a>This is different from <span class="st">`</span><span class="at">torch_Tensor.repeat</span><span class="st">`</span> but similar to <span class="st">`</span><span class="at">numpy.repeat</span><span class="st">`</span>.</span></code></pre><p></p></div>
     </div>
     <div class="section level2">
     <h2 id="repeat-interleave-repeats-gt-tensor-">repeat_interleave(repeats) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a></h2>
-    
+
 
 
 <p>If the <code>repeats</code> is <code>tensor([n1, n2, n3, ...])</code>, then the output will be
@@ -140,18 +124,18 @@ <h2 id="repeat-interleave-repeats-gt-tensor-">repeat_interleave(repeats) -&gt; T
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">x</span><span class="op">$</span><span class="fu">repeat_interleave</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span>, byrow<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">y</span>, <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">y</span>, <span class="fl">3</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_repeat_interleave</span><span class="op">(</span><span class="va">y</span>, <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_reshape.html b/reference/torch_reshape.html
index 360312e896..986be1c7a6 100644
--- a/reference/torch_reshape.html
+++ b/reference/torch_reshape.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Reshape"><title>Reshape — torch_reshape • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Reshape — torch_reshape"><meta property="og:description" content="Reshape"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Reshape — torch_reshape • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Reshape — torch_reshape"><meta name="description" content="Reshape"><meta property="og:description" content="Reshape"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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-      <img src="" class="logo" alt=""><h1>Reshape</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Reshape</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_reshape.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to be reshaped</p></dd>
 
 
-<dt>shape</dt>
+<dt id="arg-shape">shape<a class="anchor" aria-label="anchor" href="#arg-shape"></a></dt>
 <dd><p>(tuple of ints) the new shape</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="reshape-input-shape-gt-tensor-">reshape(input, shape) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#reshape-input-shape-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor with the same data and number of elements as <code>input</code>,
@@ -135,7 +119,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -144,14 +128,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_result_type.html b/reference/torch_result_type.html
index 6e7cdd4a8c..19b2f3e721 100644
--- a/reference/torch_result_type.html
+++ b/reference/torch_result_type.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Result_type</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Result_type</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_result_type.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensor1</dt>
+
+
+<dl><dt id="arg-tensor-">tensor1<a class="anchor" aria-label="anchor" href="#arg-tensor-"></a></dt>
 <dd><p>(Tensor or Number) an input tensor or number</p></dd>
 
 
-<dt>tensor2</dt>
+<dt id="arg-tensor-">tensor2<a class="anchor" aria-label="anchor" href="#arg-tensor-"></a></dt>
 <dd><p>(Tensor or Number) an input tensor or number</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="result-type-tensor-tensor-gt-dtype-">result_type(tensor1, tensor2) -&gt; dtype <a class="anchor" aria-label="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a></h2>
-    
+
 
 
 <p>Returns the <code>torch_dtype</code> that would result from performing an arithmetic
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_Float</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -131,14 +115,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_roll.html b/reference/torch_roll.html
index d1d550eca0..e84cf53ee1 100644
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Roll</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Roll</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_roll.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>shifts</dt>
+<dt id="arg-shifts">shifts<a class="anchor" aria-label="anchor" href="#arg-shifts"></a></dt>
 <dd><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></dd>
 
 
-<dt>dims</dt>
+<dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
 <dd><p>(int or tuple of ints) Axis along which to roll</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="roll-input-shifts-dims-null-gt-tensor-">roll(input, shifts, dims=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#roll-input-shifts-dims-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Roll the tensor along the given dimension(s). Elements that are shifted beyond the
@@ -136,7 +120,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_rot90.html b/reference/torch_rot90.html
index 45abd0e7b0..5140b55f31 100644
--- a/reference/torch_rot90.html
+++ b/reference/torch_rot90.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Rot90</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Rot90</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_rot90.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>(int) number of times to rotate</p></dd>
 
 
-<dt>dims</dt>
+<dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
 <dd><p>(a list or tuple) axis to rotate</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="rot-input-k-dims-gt-tensor-">rot90(input, k, dims) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rot-input-k-dims-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
@@ -138,7 +122,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -147,14 +131,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_round.html b/reference/torch_round.html
index f1b451d6f4..d21f49abcd 100644
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Round</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Round</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_round.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>decimals</dt>
+<dt id="arg-decimals">decimals<a class="anchor" aria-label="anchor" href="#arg-decimals"></a></dt>
 <dd><p>Number of decimal places to round to (default: 0).
 If decimals is negative, it specifies the number of positions to
 the left of the decimal point.</p></dd>
@@ -107,7 +91,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="round-input-out-null-gt-tensor-">round(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#round-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with each of the elements of <code>input</code> rounded
@@ -123,14 +107,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_round</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
-  
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diff --git a/reference/torch_rrelu_.html b/reference/torch_rrelu_.html
index 5f5d394576..6054cdaa3f 100644
--- a/reference/torch_rrelu_.html
+++ b/reference/torch_rrelu_.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Rrelu_</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Rrelu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_rrelu_.Rd</code></div>
     </div>
 
@@ -101,35 +83,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 
-<dt>lower</dt>
+<dt id="arg-lower">lower<a class="anchor" aria-label="anchor" href="#arg-lower"></a></dt>
 <dd><p>lower bound of the uniform distribution. Default: 1/8</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>upper bound of the uniform distribution. Default: 1/3</p></dd>
 
 
-<dt>training</dt>
+<dt id="arg-training">training<a class="anchor" aria-label="anchor" href="#arg-training"></a></dt>
 <dd><p>bool wether it's a training pass. DEfault: FALSE</p></dd>
 
 
-<dt>generator</dt>
+<dt id="arg-generator">generator<a class="anchor" aria-label="anchor" href="#arg-generator"></a></dt>
 <dd><p>random number generator</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="rrelu-input-lower-upper-training-false-gt-tensor-">rrelu_(input, lower=1./8, upper=1./3, training=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rrelu-input-lower-upper-training-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>In-place version of <code>torch_rrelu</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -138,14 +122,14 @@ <h2 id="rrelu-input-lower-upper-training-false-gt-tensor-">rrelu_(input, lower=1
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_rsqrt.html b/reference/torch_rsqrt.html
index 37227feba4..cea23bcc00 100644
--- a/reference/torch_rsqrt.html
+++ b/reference/torch_rsqrt.html
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-      <img src="" class="logo" alt=""><h1>Rsqrt</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Rsqrt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_rsqrt.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="rsqrt-input-out-null-gt-tensor-">rsqrt(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#rsqrt-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the reciprocal of the square-root of each of
@@ -120,14 +104,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_rsqrt</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0125</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3.2777</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3045</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9953</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_save.html b/reference/torch_save.html
index 5603419918..cf576eb879 100644
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@@ -1,10 +1,7 @@
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Saves an object to a disk file.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+
+      <h1>Saves an object to a disk file.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/save.R" class="external-link"><code>R/save.R</code></a></small>
       <div class="d-none name"><code>torch_save.Rd</code></div>
     </div>
 
@@ -98,19 +80,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>obj</dt>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
 <dd><p>the saved object</p></dd>
 
 
-<dt>path</dt>
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
 <dd><p>a connection or the name of the file to save.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>not currently used.</p></dd>
 
 
-<dt>compress</dt>
+<dt id="arg-compress">compress<a class="anchor" aria-label="anchor" href="#arg-compress"></a></dt>
 <dd><p>a logical specifying whether saving to a named file is to use
 "gzip" compression, or one of "gzip", "bzip2" or "xz" to indicate the type of
 compression to be used. Ignored if file is a connection.</p></dd>
@@ -118,12 +102,12 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other torch_save: 
+    <div class="dont-index"><p>Other torch_save:
 <code><a href="torch_load.html">torch_load</a>()</code>,
 <code><a href="torch_serialize.html">torch_serialize</a>()</code></p></div>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -132,14 +116,14 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
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diff --git a/reference/torch_scalar_tensor.html b/reference/torch_scalar_tensor.html
index 13454d325a..16064b731e 100644
--- a/reference/torch_scalar_tensor.html
+++ b/reference/torch_scalar_tensor.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Scalar tensor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a></small>
+
+      <h1>Scalar tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a></small>
       <div class="d-none name"><code>torch_scalar_tensor.Rd</code></div>
     </div>
 
@@ -95,24 +77,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>value</dt>
+
+
+<dl><dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>the value you want to use</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -121,14 +105,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_searchsorted.html b/reference/torch_searchsorted.html
index 486118fe09..c92d87d29c 100644
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@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Searchsorted</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Searchsorted</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_searchsorted.Rd</code></div>
     </div>
 
@@ -102,22 +84,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>sorted_sequence</dt>
+
+
+<dl><dt id="arg-sorted-sequence">sorted_sequence<a class="anchor" aria-label="anchor" href="#arg-sorted-sequence"></a></dt>
 <dd><p>(Tensor) N-D or 1-D tensor, containing monotonically increasing
 sequence on the <em>innermost</em> dimension.</p></dd>
 
 
-<dt>self</dt>
+<dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).</p></dd>
 
 
-<dt>out_int32</dt>
+<dt id="arg-out-int-">out_int32<a class="anchor" aria-label="anchor" href="#arg-out-int-"></a></dt>
 <dd><p>(bool, optional) – indicate the output data type. <code><a href="torch_dtype.html">torch_int32()</a></code>
 if True, <code><a href="torch_dtype.html">torch_int64()</a></code> otherwise. Default value is FALSE, i.e. default output
 data type is <code><a href="torch_dtype.html">torch_int64()</a></code>.</p></dd>
 
 
-<dt>right</dt>
+<dt id="arg-right">right<a class="anchor" aria-label="anchor" href="#arg-right"></a></dt>
 <dd><p>(bool, optional) – if False, return the first suitable location
 that is found. If True, return the last such index. If no suitable index found,
 return 0 for non-numerical value (eg. nan, inf) or the size of boundaries
@@ -126,13 +110,13 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 instead. Default value is False.</p></dd>
 
 
-<dt>side</dt>
+<dt id="arg-side">side<a class="anchor" aria-label="anchor" href="#arg-side"></a></dt>
 <dd><p>the same as right but preferred. “left” corresponds to <code>FALSE</code> for right
 and “right” corresponds to <code>TRUE</code> for right. It will error if this is set to
 “left” while right is <code>TRUE</code>.</p></dd>
 
 
-<dt>sorter</dt>
+<dt id="arg-sorter">sorter<a class="anchor" aria-label="anchor" href="#arg-sorter"></a></dt>
 <dd><p>if provided, a tensor matching the shape of the unsorted <code>sorted_sequence</code>
 containing a sequence of indices that sort it in the ascending order on the
 innermost dimension.</p></dd>
@@ -140,7 +124,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-">searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Find the indices from the <em>innermost</em> dimension of <code>sorted_sequence</code> such that, if the
@@ -170,7 +154,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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 </div>
 
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diff --git a/reference/torch_selu.html b/reference/torch_selu.html
index e7f404e06a..b005c2fbc9 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Selu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Selu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_selu.Rd</code></div>
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@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="selu-input-gt-tensor-">selu(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#selu-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the selu transformation.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="selu-input-gt-tensor-">selu(input) -&gt; Tensor <a class="anchor" aria-l
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diff --git a/reference/torch_selu_.html b/reference/torch_selu_.html
index 8f6807df2b..eaaac186e5 100644
--- a/reference/torch_selu_.html
+++ b/reference/torch_selu_.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Selu_</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Selu_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_selu_.Rd</code></div>
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@@ -95,19 +77,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="selu-input-gt-tensor-">selu_(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#selu-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>In-place version of <code><a href="torch_selu.html">torch_selu()</a></code>.</p>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -116,14 +100,14 @@ <h2 id="selu-input-gt-tensor-">selu_(input) -&gt; Tensor <a class="anchor" aria-
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diff --git a/reference/torch_serialize.html b/reference/torch_serialize.html
index 9233b9adb5..4a4c7edd14 100644
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-      <img src="" class="logo" alt=""><h1>Serialize a torch object returning a raw object</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/save.R" class="external-link"><code>R/save.R</code></a></small>
+
+      <h1>Serialize a torch object returning a raw object</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/save.R" class="external-link"><code>R/save.R</code></a></small>
       <div class="d-none name"><code>torch_serialize.Rd</code></div>
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@@ -95,30 +77,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>obj</dt>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
 <dd><p>the saved object</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional arguments passed to <code><a href="torch_save.html">torch_save()</a></code>. <code>obj</code> and <code>path</code> are
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 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A raw vector containing the serialized object. Can be reloaded using
+    <p>A raw vector containing the serialized object. Can be reloaded using
 <code><a href="torch_load.html">torch_load()</a></code>.</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
-    <div class="dont-index"><p>Other torch_save: 
+    <div class="dont-index"><p>Other torch_save:
 <code><a href="torch_load.html">torch_load</a>()</code>,
 <code><a href="torch_save.html">torch_save</a>()</code></p></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -127,14 +109,14 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 </div>
 
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diff --git a/reference/torch_set_num_interop_threads.html b/reference/torch_set_num_interop_threads.html
new file mode 100644
index 0000000000..c9f18bb9b3
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diff --git a/reference/torch_set_num_threads.html b/reference/torch_set_num_threads.html
new file mode 100644
index 0000000000..c9f18bb9b3
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+++ b/reference/torch_set_num_threads.html
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diff --git a/reference/torch_set_rng_state.html b/reference/torch_set_rng_state.html
new file mode 100644
index 0000000000..2637dbbdf2
--- /dev/null
+++ b/reference/torch_set_rng_state.html
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diff --git a/reference/torch_sgn.html b/reference/torch_sgn.html
index 0fcf2c6144..790838d68d 100644
--- a/reference/torch_sgn.html
+++ b/reference/torch_sgn.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Sgn"><title>Sgn — torch_sgn • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Sgn — torch_sgn"><meta property="og:description" content="Sgn"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sgn</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sgn</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sgn.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sgn-input-out-none-gt-tensor-">sgn(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sgn-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>For complex tensors, this function returns a new tensor whose elemants have the same angle as that of the
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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index 9c5bbcdaab..013b97ff99 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sigmoid</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sigmoid</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sigmoid.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sigmoid-input-out-null-gt-tensor-">sigmoid(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sigmoid-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the sigmoid of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_sigmoid</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3561</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6325</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5406</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2522</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2432</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6924</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5103</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5446</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 8797ae2b3a..f13bb58fc1 100644
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-      <img src="" class="logo" alt=""><h1>Sign</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sign</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sign.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sign-input-out-null-gt-tensor-">sign(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sign-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the signs of the elements of <code>input</code>.</p>
@@ -126,7 +110,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_signbit.html b/reference/torch_signbit.html
index a0e4b57866..76b8ebc4b6 100644
--- a/reference/torch_signbit.html
+++ b/reference/torch_signbit.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Signbit</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Signbit</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_signbit.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="signbit-input-out-none-gt-tensor-">signbit(input, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#signbit-input-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Tests if each element of <code>input</code> has its sign bit set (is less than zero) or not.</p>
@@ -122,7 +106,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUBoolType{4} ]</span>
 </code></pre></div>
     </div>
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diff --git a/reference/torch_sin.html b/reference/torch_sin.html
index 3bfd29dd72..013697528f 100644
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-      <img src="" class="logo" alt=""><h1>Sin</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sin</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sin.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sin-input-out-null-gt-tensor-">sin(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sin-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the sine of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_sin</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6204</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8166</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9796</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9412</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3945</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7831</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1282</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9433</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_sinh.html b/reference/torch_sinh.html
index ce7d7cf80d..47d5f7c240 100644
--- a/reference/torch_sinh.html
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sinh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sinh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sinh.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sinh-input-out-null-gt-tensor-">sinh(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sinh-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the hyperbolic sine of the elements of
@@ -120,14 +104,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_sinh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.4683</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7364</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.0255</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5774</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3335</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.5504</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1.9916</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.3577</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Slogdet</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Slogdet</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_slogdet.Rd</code></div>
     </div>
 
@@ -95,7 +77,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code>*</code> is zero or more                batch dimensions.</p></dd>
 
 </dl></div>
@@ -109,7 +93,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="slogdet-input-gt-tensor-tensor-">slogdet(input) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#slogdet-input-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.</p>
@@ -132,12 +116,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 0.240458</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1.12348</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_sort.html b/reference/torch_sort.html
index c151dd7092..a0b8018eeb 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Sort</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Sort</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_sort.Rd</code></div>
     </div>
 
@@ -91,26 +73,28 @@ <h6 class="dropdown-header" data-toc-skip>Autograd</h6>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) the dimension to sort along</p></dd>
 
 
-<dt>descending</dt>
+<dt id="arg-descending">descending<a class="anchor" aria-label="anchor" href="#arg-descending"></a></dt>
 <dd><p>(bool, optional) controls the sorting order (ascending or descending)</p></dd>
 
 
-<dt>stable</dt>
+<dt id="arg-stable">stable<a class="anchor" aria-label="anchor" href="#arg-stable"></a></dt>
 <dd><p>(bool, optional) – makes the sorting routine stable, which guarantees
 that the order of equivalent elements is preserved.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sort-input-dim-descending-false-gt-tensor-longtensor-">sort(input, dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#sort-input-dim-descending-false-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Sorts the elements of the <code>input</code> tensor along a given dimension
@@ -135,21 +119,21 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7761 -2.0850 -0.6274 -0.7934</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5297 -1.2828 -0.2581  0.2248</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0347 -0.2183  0.0451  0.8375</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9502  0.2731 -2.9674 -1.5218</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7463  0.4347 -0.7894 -0.6639</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4535  0.7934  1.3197  1.2911</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,4} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  3  2  2  3</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1  1  1  1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  2  3  3  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1  3  3  2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2  1  2  3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  3  2  1  1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPULongType{3,4} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -158,14 +142,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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diff --git a/reference/torch_sparse_coo.html b/reference/torch_sparse_coo.html
new file mode 100644
index 0000000000..63c09d8468
--- /dev/null
+++ b/reference/torch_sparse_coo.html
@@ -0,0 +1,8 @@
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diff --git a/reference/torch_sparse_coo_tensor.html b/reference/torch_sparse_coo_tensor.html
index 9bcb726006..f22d712833 100644
--- a/reference/torch_sparse_coo_tensor.html
+++ b/reference/torch_sparse_coo_tensor.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Sparse_coo_tensor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Sparse_coo_tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_sparse_coo_tensor.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>indices</dt>
+
+
+<dl><dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
 <dd><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></dd>
 
 
-<dt>values</dt>
+<dt id="arg-values">values<a class="anchor" aria-label="anchor" href="#arg-values"></a></dt>
 <dd><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></dd>
 
 
-<dt>size</dt>
+<dt id="arg-size">size<a class="anchor" aria-label="anchor" href="#arg-size"></a></dt>
 <dd><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from <code>values</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-">sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code>indices</code>
@@ -160,7 +144,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -169,14 +153,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_split.html b/reference/torch_split.html
index e31decc761..869e1ccd9c 100644
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-      <img src="" class="logo" alt=""><h1>Split</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Split</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_split.Rd</code></div>
     </div>
 
@@ -95,16 +77,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) tensor to split.</p></dd>
 
 
-<dt>split_size</dt>
+<dt id="arg-split-size">split_size<a class="anchor" aria-label="anchor" href="#arg-split-size"></a></dt>
 <dd><p>(int) size of a single chunk or
 list of sizes for each chunk</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension along which to split the tensor.</p></dd>
 
 </dl></div>
@@ -119,7 +103,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 to <code>split_size_or_sections</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -128,14 +112,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_sqrt.html b/reference/torch_sqrt.html
index aa23d551ec..2336149d32 100644
--- a/reference/torch_sqrt.html
+++ b/reference/torch_sqrt.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Sqrt"><title>Sqrt — torch_sqrt • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Sqrt — torch_sqrt"><meta property="og:description" content="Sqrt"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Sqrt — torch_sqrt • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Sqrt — torch_sqrt"><meta name="description" content="Sqrt"><meta property="og:description" content="Sqrt"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sqrt</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sqrt</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sqrt.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sqrt-input-out-null-gt-tensor-">sqrt(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sqrt-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the square-root of the elements of <code>input</code>.</p>
@@ -120,13 +104,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1726</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     nan</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5573</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5131</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_square.html b/reference/torch_square.html
index f5968b090d..5efff869b0 100644
--- a/reference/torch_square.html
+++ b/reference/torch_square.html
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-      <img src="" class="logo" alt=""><h1>Square</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Square</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_square.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="square-input-out-null-gt-tensor-">square(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#square-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the square of the elements of <code>input</code>.</p>
@@ -116,14 +100,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_square</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1155</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0852</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  6.8488</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0022</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0045</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3852</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3863</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.2824</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -132,14 +116,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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diff --git a/reference/torch_squeeze.html b/reference/torch_squeeze.html
index 82d21b4781..8dbfd438a1 100644
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+++ b/reference/torch_squeeze.html
@@ -1,8 +1,5 @@
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Squeeze</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Squeeze</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_squeeze.Rd</code></div>
     </div>
 
@@ -95,11 +77,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="squeeze-input-dim-null-out-null-gt-tensor-">squeeze(input, dim=NULL, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#squeeze-input-dim-null-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor with all the dimensions of <code>input</code> of size <code>1</code> removed.</p>
@@ -151,7 +135,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2,1,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index bc97955b4b..7646b51935 100644
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@@ -1,8 +1,5 @@
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Stack</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Stack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_stack.Rd</code></div>
     </div>
 
@@ -95,24 +77,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="stack-tensors-dim-out-null-gt-tensor-">stack(tensors, dim=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#stack-tensors-dim-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Concatenates sequence of tensors along a new dimension.</p>
 <p>All tensors need to be of the same size.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -121,14 +105,14 @@ <h2 id="stack-tensors-dim-out-null-gt-tensor-">stack(tensors, dim=0, out=NULL) -
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_std.html b/reference/torch_std.html
index 783acdc50b..7e1fad2bef 100644
--- a/reference/torch_std.html
+++ b/reference/torch_std.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Std"><title>Std — torch_std • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Std — torch_std"><meta property="og:description" content="Std"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Std — torch_std • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Std — torch_std"><meta name="description" content="Std"><meta property="og:description" content="Std"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Std</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Std</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_std.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>unbiased</dt>
+<dt id="arg-unbiased">unbiased<a class="anchor" aria-label="anchor" href="#arg-unbiased"></a></dt>
 <dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="std-input-unbiased-true-gt-tensor-">std(input, unbiased=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#std-input-unbiased-true-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the standard-deviation of all elements in the <code>input</code> tensor.</p>
@@ -122,7 +106,7 @@ <h2 id="std-input-unbiased-true-gt-tensor-">std(input, unbiased=TRUE) -&gt; Tens
     </div>
     <div class="section level2">
     <h2 id="std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-">std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the standard-deviation of each row of the <code>input</code> tensor in the
@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_std</span><span class="op">(</span><span class="va">a</span>, dim<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1563</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3994</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5251</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2504</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  2.0246</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5911</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1543</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5795</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -166,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 3615049595..7bf5e7b6ae 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Std_mean</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Std_mean</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_std_mean.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>unbiased</dt>
+<dt id="arg-unbiased">unbiased<a class="anchor" aria-label="anchor" href="#arg-unbiased"></a></dt>
 <dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="std-mean-input-unbiased-true-gt-tensor-tensor-">std_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the standard-deviation and mean of all elements in the <code>input</code> tensor.</p>
@@ -122,7 +106,7 @@ <h2 id="std-mean-input-unbiased-true-gt-tensor-tensor-">std_mean(input, unbiased
     </div>
     <div class="section level2">
     <h2 id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-">std_mean(input, dim, unbiased=TRUE, keepdim=False) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the standard-deviation and mean of each row of the <code>input</code> tensor in the
@@ -151,23 +135,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3698</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6728</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1034</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8341</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6614</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7547</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7540</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0862</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1781</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1767</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4981</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7320</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5279</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5551</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -176,14 +160,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_stft.html b/reference/torch_stft.html
index b6528853b9..e54420b8a1 100644
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+++ b/reference/torch_stft.html
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Stft</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Stft</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_stft.Rd</code></div>
     </div>
 
@@ -106,58 +88,60 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>n_fft</dt>
+<dt id="arg-n-fft">n_fft<a class="anchor" aria-label="anchor" href="#arg-n-fft"></a></dt>
 <dd><p>(int) size of Fourier transform</p></dd>
 
 
-<dt>hop_length</dt>
+<dt id="arg-hop-length">hop_length<a class="anchor" aria-label="anchor" href="#arg-hop-length"></a></dt>
 <dd><p>(int, optional) the distance between neighboring sliding window
 frames. Default: <code>NULL</code> (treated as equal to <code>floor(n_fft / 4)</code>)</p></dd>
 
 
-<dt>win_length</dt>
+<dt id="arg-win-length">win_length<a class="anchor" aria-label="anchor" href="#arg-win-length"></a></dt>
 <dd><p>(int, optional) the size of window frame and STFT filter.
 Default: <code>NULL</code>  (treated as equal to <code>n_fft</code>)</p></dd>
 
 
-<dt>window</dt>
+<dt id="arg-window">window<a class="anchor" aria-label="anchor" href="#arg-window"></a></dt>
 <dd><p>(Tensor, optional) the optional window function.
 Default: <code>NULL</code> (treated as window of all \(1\) s)</p></dd>
 
 
-<dt>center</dt>
+<dt id="arg-center">center<a class="anchor" aria-label="anchor" href="#arg-center"></a></dt>
 <dd><p>(bool, optional) whether to pad <code>input</code> on both sides so
 that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).
 Default: <code>TRUE</code></p></dd>
 
 
-<dt>pad_mode</dt>
+<dt id="arg-pad-mode">pad_mode<a class="anchor" aria-label="anchor" href="#arg-pad-mode"></a></dt>
 <dd><p>(string, optional) controls the padding method used when
 <code>center</code> is <code>TRUE</code>. Default: <code>"reflect"</code></p></dd>
 
 
-<dt>normalized</dt>
+<dt id="arg-normalized">normalized<a class="anchor" aria-label="anchor" href="#arg-normalized"></a></dt>
 <dd><p>(bool, optional) controls whether to return the normalized
 STFT results Default: <code>FALSE</code></p></dd>
 
 
-<dt>onesided</dt>
+<dt id="arg-onesided">onesided<a class="anchor" aria-label="anchor" href="#arg-onesided"></a></dt>
 <dd><p>(bool, optional) controls whether to return half of results to
 avoid redundancy Default: <code>TRUE</code></p></dd>
 
 
-<dt>return_complex</dt>
+<dt id="arg-return-complex">return_complex<a class="anchor" aria-label="anchor" href="#arg-return-complex"></a></dt>
 <dd><p>(bool, optional) controls whether to return complex tensors
 or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="short-time-fourier-transform-stft-">Short-time Fourier transform (STFT). <a class="anchor" aria-label="anchor" href="#short-time-fourier-transform-stft-"></a></h2>
-    
+
 
 
 <p>Short-time Fourier transform (STFT).</p>
@@ -212,13 +196,13 @@ <h2 id="short-time-fourier-transform-stft-">Short-time Fourier transform (STFT).
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>This function changed signature at version 0.4.1. Calling with the
 previous signature may cause error or return incorrect result.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -227,14 +211,14 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_strided.html b/reference/torch_strided.html
new file mode 100644
index 0000000000..63c09d8468
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+++ b/reference/torch_strided.html
@@ -0,0 +1,8 @@
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diff --git a/reference/torch_sub.html b/reference/torch_sub.html
index 23297711ac..cef1a1c70f 100644
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sub</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sub</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sub.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or Scalar) the tensor or scalar to subtract from <code>input</code></p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the scalar multiplier for other</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sub-input-other-alpha-out-none-gt-tensor-">sub(input, other, *, alpha=1, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sub-input-other-alpha-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Subtracts <code>other</code>, scaled by <code>alpha</code>, from <code>input</code>.</p>
@@ -134,7 +118,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_subtract.html b/reference/torch_subtract.html
index 4a2ae3820c..8b0a5f757f 100644
--- a/reference/torch_subtract.html
+++ b/reference/torch_subtract.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Subtract</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Subtract</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_subtract.Rd</code></div>
     </div>
 
@@ -95,27 +77,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or Scalar) the tensor or scalar to subtract from <code>input</code></p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>the scalar multiplier for other</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="subtract-input-other-alpha-out-none-gt-tensor-">subtract(input, other, *, alpha=1, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#subtract-input-other-alpha-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Alias for <code><a href="torch_sub.html">torch_sub()</a></code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -124,14 +108,14 @@ <h2 id="subtract-input-other-alpha-out-none-gt-tensor-">subtract(input, other, *
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_sum.html b/reference/torch_sum.html
index 68ab7f3edd..cf8b842d3f 100644
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@@ -1,8 +1,5 @@
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Sum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Sum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_sum.Rd</code></div>
     </div>
 
@@ -95,32 +77,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="sum-input-dtype-null-gt-tensor-">sum(input, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sum-input-dtype-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the sum of all elements in the <code>input</code> tensor.</p>
     </div>
     <div class="section level2">
     <h2 id="sum-input-dim-keepdim-false-dtype-null-gt-tensor-">sum(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#sum-input-dim-keepdim-false-dtype-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the sum of each row of the <code>input</code> tensor in the given
@@ -157,7 +141,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -166,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_svd.html b/reference/torch_svd.html
index c256551d75..551027b91a 100644
--- a/reference/torch_svd.html
+++ b/reference/torch_svd.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Svd"><title>Svd — torch_svd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Svd — torch_svd"><meta property="og:description" content="Svd"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Svd — torch_svd • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Svd — torch_svd"><meta name="description" content="Svd"><meta property="og:description" content="Svd"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Svd</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Svd</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_svd.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor of size \((*, m, n)\) where <code>*</code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></dd>
 
 
-<dt>some</dt>
+<dt id="arg-some">some<a class="anchor" aria-label="anchor" href="#arg-some"></a></dt>
 <dd><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></dd>
 
 
-<dt>compute_uv</dt>
+<dt id="arg-compute-uv">compute_uv<a class="anchor" aria-label="anchor" href="#arg-compute-uv"></a></dt>
 <dd><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></dd>
 
 </dl></div>
@@ -130,7 +114,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-">svd(input, some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>This function returns a namedtuple <code>(U, S, V)</code> which is the singular value
@@ -162,11 +146,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="torch_dist.html">torch_dist</a></span><span class="op">(</span><span class="va">a_big</span>, <span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="fu"><a href="torch_matmul.html">torch_matmul</a></span><span class="op">(</span><span class="va">u</span>, <span class="fu"><a href="torch_diag_embed.html">torch_diag_embed</a></span><span class="op">(</span><span class="va">s</span><span class="op">)</span><span class="op">)</span>, <span class="va">v</span><span class="op">$</span><span class="fu">transpose</span><span class="op">(</span><span class="op">-</span><span class="fl">2</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2.91169e-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2.56462e-06</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -175,14 +159,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-      <img src="" class="logo" alt=""><h1>T</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>T</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_t.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="t-input-gt-tensor-">t(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#t-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Expects <code>input</code> to be &lt;= 2-D tensor and transposes dimensions 0
@@ -125,13 +109,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_t</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8600  0.5891</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.0482 -1.9269</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6084  0.3332</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2638 -0.3001</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.4795 -0.0578</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.2955  0.0028</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_take.html b/reference/torch_take.html
index d553d2c10a..c84625f7e8 100644
--- a/reference/torch_take.html
+++ b/reference/torch_take.html
@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Take</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Take</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_take.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>index</dt>
+<dt id="arg-index">index<a class="anchor" aria-label="anchor" href="#arg-index"></a></dt>
 <dd><p>(LongTensor) the indices into tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="take-input-index-gt-tensor-">take(input, index) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#take-input-index-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the elements of <code>input</code> at the given indices.
@@ -127,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_take_along_dim.html b/reference/torch_take_along_dim.html
new file mode 100644
index 0000000000..d73459b766
--- /dev/null
+++ b/reference/torch_take_along_dim.html
@@ -0,0 +1,137 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim"><meta name="description" content="Selects values from input at the 1-dimensional indices from indices along the given dim."><meta property="og:description" content="Selects values from input at the 1-dimensional indices from indices along the given dim."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Selects values from input at the 1-dimensional indices from indices along the given dim.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+      <div class="d-none name"><code>torch_take_along_dim.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Selects values from input at the 1-dimensional indices from indices along the given dim.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">torch_take_along_dim</span><span class="op">(</span><span class="va">self</span>, <span class="va">indices</span>, dim <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
+<dd><p>the input tensor.</p></dd>
+
+
+<dt id="arg-indices">indices<a class="anchor" aria-label="anchor" href="#arg-indices"></a></dt>
+<dd><p>the indices into input. Must have long dtype.</p></dd>
+
+
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
+<dd><p>the dimension to select along. Default is <code>NULL</code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
+    <p>If dim is <code>NULL</code>, the input array is treated as if it has been flattened to 1d.</p>
+<p>Functions that return indices along a dimension, like <code><a href="torch_argmax.html">torch_argmax()</a></code> and <code><a href="torch_argsort.html">torch_argsort()</a></code>,
+are designed to work with this function. See the examples below.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="va">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_tensor.html">torch_tensor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">30</span>, <span class="fl">20</span>, <span class="fl">60</span>, <span class="fl">40</span>, <span class="fl">50</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">max_idx</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_argmax.html">torch_argmax</a></span><span class="op">(</span><span class="va">t</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">torch_take_along_dim</span><span class="op">(</span><span class="va">t</span>, <span class="va">max_idx</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">sorted_idx</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_argsort.html">torch_argsort</a></span><span class="op">(</span><span class="va">t</span>, dim<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu">torch_take_along_dim</span><span class="op">(</span><span class="va">t</span>, <span class="va">sorted_idx</span>, dim<span class="op">=</span><span class="fl">2</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  10  20  40</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  30  50  60</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_tan.html b/reference/torch_tan.html
index f6b09e0513..3da8df9c94 100644
--- a/reference/torch_tan.html
+++ b/reference/torch_tan.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Tan"><title>Tan — torch_tan • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Tan — torch_tan"><meta property="og:description" content="Tan"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Tan — torch_tan • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Tan — torch_tan"><meta name="description" content="Tan"><meta property="og:description" content="Tan"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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 </script></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
+
 
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Modifying source code</a></li>
+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tan</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Tan</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_tan.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="tan-input-out-null-gt-tensor-">tan(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tan-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the tangent of the elements of <code>input</code>.</p>
@@ -119,14 +103,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_tan</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6133</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6518</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1024</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6123</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0479</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6779</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.9440</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2676</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_tanh.html b/reference/torch_tanh.html
index a44884de7e..ae493ca474 100644
--- a/reference/torch_tanh.html
+++ b/reference/torch_tanh.html
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-      <img src="" class="logo" alt=""><h1>Tanh</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Tanh</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_tanh.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="tanh-input-out-null-gt-tensor-">tanh(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tanh-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the hyperbolic tangent of the elements
@@ -120,14 +104,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_tanh</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2279</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1029</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8538</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8189</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.9192</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1055</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.6897</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index a0d43ec4c2..6574099d31 100644
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-      <img src="" class="logo" alt=""><h1>Converts R objects to a torch tensor</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
+
+      <h1>Converts R objects to a torch tensor</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
       <div class="d-none name"><code>torch_tensor.Rd</code></div>
     </div>
 
@@ -101,23 +83,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>data</dt>
+
+
+<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
 <dd><p>an R atomic vector, matrix or array</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>a <a href="torch_dtype.html">torch_dtype</a> instance</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>a device creted with <code><a href="torch_device.html">torch_device()</a></code></p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>if autograd should record operations on the returned tensor.</p></dd>
 
 
-<dt>pin_memory</dt>
+<dt id="arg-pin-memory">pin_memory<a class="anchor" aria-label="anchor" href="#arg-pin-memory"></a></dt>
 <dd><p>If set, returned tensor would be allocated in the pinned memory.</p></dd>
 
 </dl></div>
@@ -136,7 +120,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUIntType{4} ]</span>
 </code></pre></div>
     </div>
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -145,14 +129,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
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index ad5b53a30e..3ac016b632 100644
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Creates a tensor from a buffer of memory</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
+
+      <h1>Creates a tensor from a buffer of memory</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/tensor.R" class="external-link"><code>R/tensor.R</code></a></small>
       <div class="d-none name"><code>torch_tensor_from_buffer.Rd</code></div>
     </div>
 
@@ -100,29 +82,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>buffer</dt>
+
+
+<dl><dt id="arg-buffer">buffer<a class="anchor" aria-label="anchor" href="#arg-buffer"></a></dt>
 <dd><p>An R atomic object containing the data in a contiguous array.</p></dd>
 
 
-<dt>shape</dt>
+<dt id="arg-shape">shape<a class="anchor" aria-label="anchor" href="#arg-shape"></a></dt>
 <dd><p>The shape of the resulting tensor.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>A torch data type for the tresulting tensor.</p></dd>
 
 
-<dt>tensor</dt>
+<dt id="arg-tensor">tensor<a class="anchor" aria-label="anchor" href="#arg-tensor"></a></dt>
 <dd><p>Tensor object that will be converted into a buffer.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>buffer_from_torch_tensor()</code>: Creates a raw vector containing the tensor data. Causes a data copy.</p></li>
 </ul></div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -131,14 +115,14 @@ <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functio
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_tensordot.html b/reference/torch_tensordot.html
index 658da61844..19ea18e3fa 100644
--- a/reference/torch_tensordot.html
+++ b/reference/torch_tensordot.html
@@ -1,10 +1,7 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Returns a contraction of a and b over multiple dimensions.
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tensordot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Tensordot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_tensordot.Rd</code></div>
     </div>
 
@@ -98,15 +80,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>a</dt>
+
+
+<dl><dt id="arg-a">a<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor) Left tensor to contract</p></dd>
 
 
-<dt>b</dt>
+<dt id="arg-b">b<a class="anchor" aria-label="anchor" href="#arg-b"></a></dt>
 <dd><p>(Tensor) Right tensor to contract</p></dd>
 
 
-<dt>dims</dt>
+<dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
 <dd><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></dd>
 
 </dl></div>
@@ -118,15 +102,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">60</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="torch_arange.html">torch_arange</a></span><span class="op">(</span>start <span class="op">=</span> <span class="fl">1</span>, end <span class="op">=</span> <span class="fl">24</span><span class="op">)</span><span class="op">$</span><span class="fu">reshape</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">torch_tensordot</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, dims <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, device<span class="op">=</span><span class="st">'cuda'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">b</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">5</span>, <span class="fl">6</span>, device<span class="op">=</span><span class="st">'cuda'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">c</span> <span class="op">=</span> <span class="fu">torch_tensordot</span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span>, dims<span class="op">=</span><span class="fl">2</span><span class="op">)</span><span class="op">$</span><span class="fu">cpu</span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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diff --git a/reference/torch_threshold_.html b/reference/torch_threshold_.html
index bad70270c6..b13849a665 100644
--- a/reference/torch_threshold_.html
+++ b/reference/torch_threshold_.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Threshold_"><title>Threshold_ — torch_threshold_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Threshold_ — torch_threshold_"><meta property="og:description" content="Threshold_"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Threshold_ — torch_threshold_ • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Threshold_ — torch_threshold_"><meta name="description" content="Threshold_"><meta property="og:description" content="Threshold_"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
   gtag('config', 'G-9ZJSKW3L0N');
 </script></head><body>
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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+    <li><a class="dropdown-item" href="../articles/amp.html">Automatic Mixed Precision</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Threshold_</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Threshold_</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_threshold_.Rd</code></div>
     </div>
 
@@ -95,27 +77,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>input tensor</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>The value to threshold at</p></dd>
 
 
-<dt>value</dt>
+<dt id="arg-value">value<a class="anchor" aria-label="anchor" href="#arg-value"></a></dt>
 <dd><p>The value to replace with</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="threshold-input-threshold-value-gt-tensor-">threshold_(input, threshold, value) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#threshold-input-threshold-value-gt-tensor-"></a></h2>
-    
+
 
 
 <p>In-place version of <code>torch_threshold</code>.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -124,14 +108,14 @@ <h2 id="threshold-input-threshold-value-gt-tensor-">threshold_(input, threshold,
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_topk.html b/reference/torch_topk.html
index db4b3d4f39..3e364d4b56 100644
--- a/reference/torch_topk.html
+++ b/reference/torch_topk.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Topk</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Topk</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_topk.Rd</code></div>
     </div>
 
@@ -95,29 +77,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>k</dt>
+<dt id="arg-k">k<a class="anchor" aria-label="anchor" href="#arg-k"></a></dt>
 <dd><p>(int) the k in "top-k"</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int, optional) the dimension to sort along</p></dd>
 
 
-<dt>largest</dt>
+<dt id="arg-largest">largest<a class="anchor" aria-label="anchor" href="#arg-largest"></a></dt>
 <dd><p>(bool, optional) controls whether to return largest or           smallest elements</p></dd>
 
 
-<dt>sorted</dt>
+<dt id="arg-sorted">sorted<a class="anchor" aria-label="anchor" href="#arg-sorted"></a></dt>
 <dd><p>(bool, optional) controls whether to return the elements           in sorted order</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-">topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor) <a class="anchor" aria-label="anchor" href="#topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"></a></h2>
-    
+
 
 
 <p>Returns the <code>k</code> largest elements of the given <code>input</code> tensor along
@@ -154,7 +138,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -163,14 +147,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_trace.html b/reference/torch_trace.html
index 878fce9ca4..43e3a125c4 100644
--- a/reference/torch_trace.html
+++ b/reference/torch_trace.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Trace</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Trace</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_trace.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>the input tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="trace-input-gt-tensor-">trace(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trace-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the sum of the elements of the diagonal of the input 2-D matrix.</p>
@@ -120,7 +104,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/torch_transpose.html b/reference/torch_transpose.html
index e9d9103e9d..68bfe31f5d 100644
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Transpose</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Transpose</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_transpose.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim0</dt>
+<dt id="arg-dim-">dim0<a class="anchor" aria-label="anchor" href="#arg-dim-"></a></dt>
 <dd><p>(int) the first dimension to be transposed</p></dd>
 
 
-<dt>dim1</dt>
+<dt id="arg-dim-">dim1<a class="anchor" aria-label="anchor" href="#arg-dim-"></a></dt>
 <dd><p>(int) the second dimension to be transposed</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="transpose-input-dim-dim-gt-tensor-">transpose(input, dim0, dim1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor that is a transposed version of <code>input</code>.
@@ -128,13 +112,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_transpose</span><span class="op">(</span><span class="va">x</span>, <span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -1.2440 -0.3130</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0556  0.1887</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2138 -1.4054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1662 -0.6476</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.5733  1.8846</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3872  0.7389</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{3,2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -143,14 +127,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_trapz.html b/reference/torch_trapz.html
index d06479abe7..6aad3db025 100644
--- a/reference/torch_trapz.html
+++ b/reference/torch_trapz.html
@@ -1,8 +1,5 @@
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Trapz — torch_trapz • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Trapz — torch_trapz"><meta name="description" content="Trapz"><meta property="og:description" content="Trapz"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Trapz</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Trapz</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_trapz.Rd</code></div>
     </div>
 
@@ -95,32 +77,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>y</dt>
+
+
+<dl><dt id="arg-y">y<a class="anchor" aria-label="anchor" href="#arg-y"></a></dt>
 <dd><p>(Tensor) The values of the function to integrate</p></dd>
 
 
-<dt>dx</dt>
+<dt id="arg-dx">dx<a class="anchor" aria-label="anchor" href="#arg-dx"></a></dt>
 <dd><p>(float) The distance between points at which <code>y</code> is sampled.</p></dd>
 
 
-<dt>x</dt>
+<dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="trapz-y-x-dim-gt-tensor-">trapz(y, x, *, dim=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trapz-y-x-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Estimate \(\int y\,dx\) along <code>dim</code>, using the trapezoid rule.</p>
     </div>
     <div class="section level2">
     <h2 id="trapz-y-dx-dim-gt-tensor-">trapz(y, *, dx=1, dim=-1) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>As above, but the sample points are spaced uniformly at a distance of <code>dx</code>.</p>
@@ -137,12 +121,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -3.0178</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8236</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3347</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7395</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -151,14 +135,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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index 9a924e3e78..94928c944b 100644
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@@ -1,8 +1,5 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Triangular_solve</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Triangular_solve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_triangular_solve.Rd</code></div>
     </div>
 
@@ -101,29 +83,31 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></dd>
 
 
-<dt>A</dt>
+<dt id="arg-a">A<a class="anchor" aria-label="anchor" href="#arg-a"></a></dt>
 <dd><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></dd>
 
 
-<dt>upper</dt>
+<dt id="arg-upper">upper<a class="anchor" aria-label="anchor" href="#arg-upper"></a></dt>
 <dd><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>TRUE</code>.</p></dd>
 
 
-<dt>transpose</dt>
+<dt id="arg-transpose">transpose<a class="anchor" aria-label="anchor" href="#arg-transpose"></a></dt>
 <dd><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>unitriangular</dt>
+<dt id="arg-unitriangular">unitriangular<a class="anchor" aria-label="anchor" href="#arg-unitriangular"></a></dt>
 <dd><p>(bool, optional) whether \(A\) is unit triangular.        If TRUE, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-">triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Solves a system of equations with a triangular coefficient matrix \(A\)
@@ -147,19 +131,19 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -2.0220 -2.1697  0.2369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5612 -0.8687  0.3310</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4159 -19.8541 -28.8686</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -9.9554 -26.7462 -40.0722</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5262  0.6967</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.7233</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2344 -0.1714</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000 -0.0268</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,2} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -168,14 +152,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index e75ba0a7a6..b67a95a9d8 100644
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tril</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Tril</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_tril.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>diagonal</dt>
+<dt id="arg-diagonal">diagonal<a class="anchor" aria-label="anchor" href="#arg-diagonal"></a></dt>
 <dd><p>(int, optional) the diagonal to consider</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="tril-input-diagonal-out-null-gt-tensor-">tril(input, diagonal=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tril-input-diagonal-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
@@ -135,13 +119,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.8172  0.0000  0.0000  0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.2900 -0.3578  0.0000  0.0000  0.0000  0.0000</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8494 -0.3954  0.7822  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7947  0.0000  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5983 -0.5067  0.0000  0.0000  0.0000  0.0000</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.8538  0.6799 -0.4265  0.0000  0.0000  0.0000</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,6} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -150,14 +134,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_tril_indices.html b/reference/torch_tril_indices.html
index d84d483000..db7c130ba9 100644
--- a/reference/torch_tril_indices.html
+++ b/reference/torch_tril_indices.html
@@ -1,8 +1,5 @@
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tril_indices</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Tril_indices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_tril_indices.Rd</code></div>
     </div>
 
@@ -102,27 +84,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>row</dt>
+
+
+<dl><dt id="arg-row">row<a class="anchor" aria-label="anchor" href="#arg-row"></a></dt>
 <dd><p>(<code>int</code>) number of rows in the 2-D matrix.</p></dd>
 
 
-<dt>col</dt>
+<dt id="arg-col">col<a class="anchor" aria-label="anchor" href="#arg-col"></a></dt>
 <dd><p>(<code>int</code>) number of columns in the 2-D matrix.</p></dd>
 
 
-<dt>offset</dt>
+<dt id="arg-offset">offset<a class="anchor" aria-label="anchor" href="#arg-offset"></a></dt>
 <dd><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></dd>
 
 </dl></div>
@@ -133,7 +117,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-">tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the lower triangular part of a <code>row</code>-by-
@@ -154,18 +138,18 @@ <h2 id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-str
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_tril_indices</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_tril_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_tril_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -174,14 +158,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_triu.html b/reference/torch_triu.html
index dc8d5f7c14..dd63f7c7ba 100644
--- a/reference/torch_triu.html
+++ b/reference/torch_triu.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Triu"><title>Triu — torch_triu • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Triu — torch_triu"><meta property="og:description" content="Triu"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.14.0</small>
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-      <img src="" class="logo" alt=""><h1>Triu</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Triu</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_triu.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>diagonal</dt>
+<dt id="arg-diagonal">diagonal<a class="anchor" aria-label="anchor" href="#arg-diagonal"></a></dt>
 <dd><p>(int, optional) the diagonal to consider</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="triu-input-diagonal-out-null-gt-tensor-">triu(input, diagonal=0, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#triu-input-diagonal-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
@@ -136,14 +120,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_triu</span><span class="op">(</span><span class="va">b</span>, diagonal<span class="op">=</span><span class="op">-</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.4660 -0.2273 -0.6166 -1.9900 -0.7221 -0.9500</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1051 -0.7226 -0.2677  0.6145 -2.1794  0.7118</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000 -0.8139  1.0528 -0.1026  1.3411 -0.7295</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000  0.2327 -0.4739 -0.6007 -0.3636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1087 -0.8975 -1.0364 -2.0779 -2.4391  1.0559</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8972 -0.5021 -0.7000 -1.7786  0.3989 -0.1569</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000 -1.9678  1.9087  1.0738  1.5483 -0.7463</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0000  0.0000 -1.3872  0.3273  1.6337 -0.7628</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,6} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -152,14 +136,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index b90ae7daac..232e817e59 100644
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-advanced" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Advanced</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-advanced"><li><a class="dropdown-item" href="../articles/memory-management.html">Memory management</a></li>
+    <li><a class="dropdown-item" href="../articles/modifying-source-code.html">Building locally</a></li>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Triu_indices</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Triu_indices</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_triu_indices.Rd</code></div>
     </div>
 
@@ -102,27 +84,29 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>row</dt>
+
+
+<dl><dt id="arg-row">row<a class="anchor" aria-label="anchor" href="#arg-row"></a></dt>
 <dd><p>(<code>int</code>) number of rows in the 2-D matrix.</p></dd>
 
 
-<dt>col</dt>
+<dt id="arg-col">col<a class="anchor" aria-label="anchor" href="#arg-col"></a></dt>
 <dd><p>(<code>int</code>) number of columns in the 2-D matrix.</p></dd>
 
 
-<dt>offset</dt>
+<dt id="arg-offset">offset<a class="anchor" aria-label="anchor" href="#arg-offset"></a></dt>
 <dd><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></dd>
 
 </dl></div>
@@ -133,7 +117,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-">triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the indices of the upper triangular part of a <code>row</code> by
@@ -154,18 +138,18 @@ <h2 id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-str
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_triu_indices</span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_triu_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="op">-</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
 <span class="r-in"><span><span class="va">a</span> <span class="op">=</span> <span class="fu">torch_triu_indices</span><span class="op">(</span><span class="fl">4</span>, <span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">a</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -174,14 +158,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/torch_true_divide.html b/reference/torch_true_divide.html
index 03cfc7461e..ba4f4396e7 100644
--- a/reference/torch_true_divide.html
+++ b/reference/torch_true_divide.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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@@ -10,77 +7,62 @@
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-      <img src="" class="logo" alt=""><h1>TRUE_divide</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
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+      <h1>TRUE_divide</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_true_divide.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the dividend</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor or Scalar) the divisor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="true-divide-dividend-divisor-gt-tensor-">true_divide(dividend, divisor) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Performs "true division" that always computes the division
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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index 0ecee2eceb..28b16bc20e 100644
--- a/reference/torch_trunc.html
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@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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-      <img src="" class="logo" alt=""><h1>Trunc</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Trunc</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_trunc.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="trunc-input-out-null-gt-tensor-">trunc(input, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#trunc-input-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with the truncated integer values of
@@ -118,13 +102,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -1</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -133,14 +117,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_uint8.html b/reference/torch_uint8.html
new file mode 100644
index 0000000000..b522acdaf8
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@@ -0,0 +1,8 @@
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index 166e5188ca..189b14d2a0 100644
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-      <img src="" class="logo" alt=""><h1>Unbind</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Unbind</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_unbind.Rd</code></div>
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@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to unbind</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension to remove</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="unbind-input-dim-gt-seq-">unbind(input, dim=0) -&gt; seq <a class="anchor" aria-label="anchor" href="#unbind-input-dim-gt-seq-"></a></h2>
-    
+
 
 
 <p>Removes a tensor dimension.</p>
@@ -141,7 +125,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
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diff --git a/reference/torch_unique_consecutive.html b/reference/torch_unique_consecutive.html
index e96b67ea73..f5a47ecf9f 100644
--- a/reference/torch_unique_consecutive.html
+++ b/reference/torch_unique_consecutive.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Unique_consecutive</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Unique_consecutive</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_unique_consecutive.Rd</code></div>
     </div>
 
@@ -100,25 +82,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor</p></dd>
 
 
-<dt>return_inverse</dt>
+<dt id="arg-return-inverse">return_inverse<a class="anchor" aria-label="anchor" href="#arg-return-inverse"></a></dt>
 <dd><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></dd>
 
 
-<dt>return_counts</dt>
+<dt id="arg-return-counts">return_counts<a class="anchor" aria-label="anchor" href="#arg-return-counts"></a></dt>
 <dd><p>(bool) Whether to also return the counts for each unique        element.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the dimension to apply unique. If <code>NULL</code>, the unique of the        flattened input is returned. default: <code>NULL</code></p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="test-">TEST <a class="anchor" aria-label="anchor" href="#test-"></a></h2>
-    
+
 
 
 <p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
@@ -160,7 +144,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -169,14 +153,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Unsafe_chunk</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Unsafe_chunk</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_unsafe_chunk.Rd</code></div>
     </div>
 
@@ -95,21 +77,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the tensor to split</p></dd>
 
 
-<dt>chunks</dt>
+<dt id="arg-chunks">chunks<a class="anchor" aria-label="anchor" href="#arg-chunks"></a></dt>
 <dd><p>(int) number of chunks to return</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension along which to split the tensor</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="unsafe-chunk-input-chunks-dim-gt-list-of-tensors-">unsafe_chunk(input, chunks, dim=0) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#unsafe-chunk-input-chunks-dim-gt-list-of-tensors-"></a></h2>
-    
+
 
 
 <p>Works like <code><a href="torch_chunk.html">torch_chunk()</a></code> but without enforcing the autograd restrictions
@@ -117,7 +101,7 @@ <h2 id="unsafe-chunk-input-chunks-dim-gt-list-of-tensors-">unsafe_chunk(input, c
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>This function is safe to use as long as only the input, or only the outputs
 are modified inplace after calling this function. It is user's
@@ -126,7 +110,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 silently incorrect.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -135,14 +119,14 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
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-  
 
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diff --git a/reference/torch_unsafe_split.html b/reference/torch_unsafe_split.html
index a7b869967a..bc89367eb3 100644
--- a/reference/torch_unsafe_split.html
+++ b/reference/torch_unsafe_split.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Unsafe_split</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Unsafe_split</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_unsafe_split.Rd</code></div>
     </div>
 
@@ -95,22 +77,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) tensor to split.</p></dd>
 
 
-<dt>split_size</dt>
+<dt id="arg-split-size">split_size<a class="anchor" aria-label="anchor" href="#arg-split-size"></a></dt>
 <dd><p>(int) size of a single chunk or
 list of sizes for each chunk</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) dimension along which to split the tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-">unsafe_split(tensor, split_size_or_sections, dim=0) -&gt; List of Tensors <a class="anchor" aria-label="anchor" href="#unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-"></a></h2>
-    
+
 
 
 <p>Works like <code><a href="torch_split.html">torch_split()</a></code> but without enforcing the autograd restrictions
@@ -118,7 +102,7 @@ <h2 id="unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-">unsa
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>This function is safe to use as long as only the input, or only the outputs
 are modified inplace after calling this function. It is user's
@@ -127,7 +111,7 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 silently incorrect.</p>
     </div>
 
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -136,14 +120,14 @@ <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></
 </div>
 
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diff --git a/reference/torch_unsqueeze.html b/reference/torch_unsqueeze.html
index b8c0e84494..a43dea763b 100644
--- a/reference/torch_unsqueeze.html
+++ b/reference/torch_unsqueeze.html
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Unsqueeze</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Unsqueeze</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_unsqueeze.Rd</code></div>
     </div>
 
@@ -95,17 +77,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int) the index at which to insert the singleton dimension</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="unsqueeze-input-dim-gt-tensor-">unsqueeze(input, dim) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a new tensor with a dimension of size one inserted at the
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_vander.html b/reference/torch_vander.html
index df15d13755..2fdcb6d411 100644
--- a/reference/torch_vander.html
+++ b/reference/torch_vander.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Vander"><title>Vander — torch_vander • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Vander — torch_vander"><meta property="og:description" content="Vander"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Vander</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Vander</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_vander.Rd</code></div>
     </div>
 
@@ -95,23 +77,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>x</dt>
+
+
+<dl><dt id="arg-x">x<a class="anchor" aria-label="anchor" href="#arg-x"></a></dt>
 <dd><p>(Tensor) 1-D input tensor.</p></dd>
 
 
-<dt>N</dt>
+<dt id="arg-n">N<a class="anchor" aria-label="anchor" href="#arg-n"></a></dt>
 <dd><p>(int, optional) Number of columns in the output. If N is not specified,
 a square array is returned \((N = len(x))\).</p></dd>
 
 
-<dt>increasing</dt>
+<dt id="arg-increasing">increasing<a class="anchor" aria-label="anchor" href="#arg-increasing"></a></dt>
 <dd><p>(bool, optional) Order of the powers of the columns. If TRUE,
 the powers increase from left to right, if FALSE (the default) they are reversed.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="vander-x-n-none-increasing-false-gt-tensor-">vander(x, N=None, increasing=FALSE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#vander-x-n-none-increasing-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Generates a Vandermonde matrix.</p>
@@ -140,7 +124,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
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index 5db5fff2c7..1a7990cfcc 100644
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+  </ul></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/mlverse/torch/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Var</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Var</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_var.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>unbiased</dt>
+<dt id="arg-unbiased">unbiased<a class="anchor" aria-label="anchor" href="#arg-unbiased"></a></dt>
 <dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="var-input-unbiased-true-gt-tensor-">var(input, unbiased=TRUE) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#var-input-unbiased-true-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the variance of all elements in the <code>input</code> tensor.</p>
@@ -122,7 +106,7 @@ <h2 id="var-input-unbiased-true-gt-tensor-">var(input, unbiased=TRUE) -&gt; Tens
     </div>
     <div class="section level2">
     <h2 id="var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-">var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the variance of each row of the <code>input</code> tensor in the given
@@ -149,14 +133,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu">torch_var</span><span class="op">(</span><span class="va">a</span>, <span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.1741</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8066</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.8044</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2940</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7107</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.7116</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.3322</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6740</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -165,14 +149,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/torch_var_mean.html b/reference/torch_var_mean.html
index 676cbe952c..4bbcacafca 100644
--- a/reference/torch_var_mean.html
+++ b/reference/torch_var_mean.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Var_mean"><title>Var_mean — torch_var_mean • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Var_mean — torch_var_mean"><meta property="og:description" content="Var_mean"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Var_mean — torch_var_mean • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Var_mean — torch_var_mean"><meta name="description" content="Var_mean"><meta property="og:description" content="Var_mean"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Var_mean</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Var_mean</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_var_mean.Rd</code></div>
     </div>
 
@@ -95,25 +77,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 
-<dt>dim</dt>
+<dt id="arg-dim">dim<a class="anchor" aria-label="anchor" href="#arg-dim"></a></dt>
 <dd><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></dd>
 
 
-<dt>unbiased</dt>
+<dt id="arg-unbiased">unbiased<a class="anchor" aria-label="anchor" href="#arg-unbiased"></a></dt>
 <dd><p>(bool) whether to use the unbiased estimation or not</p></dd>
 
 
-<dt>keepdim</dt>
+<dt id="arg-keepdim">keepdim<a class="anchor" aria-label="anchor" href="#arg-keepdim"></a></dt>
 <dd><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="var-mean-input-unbiased-true-gt-tensor-tensor-">var_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the variance and mean of all elements in the <code>input</code> tensor.</p>
@@ -122,7 +106,7 @@ <h2 id="var-mean-input-unbiased-true-gt-tensor-tensor-">var_mean(input, unbiased
     </div>
     <div class="section level2">
     <h2 id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-">var_mean(input, dim, keepdim=False, unbiased=TRUE) -&gt; (Tensor, Tensor) <a class="anchor" aria-label="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a></h2>
-    
+
 
 
 <p>Returns the variance and mean of each row of the <code>input</code> tensor in the given
@@ -150,23 +134,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[1]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.2780</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.6386</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.5056</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  1.7292</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1897</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.4186</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.0193</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1092</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [[2]]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> torch_tensor</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0747</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1032</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> -0.7452</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  0.3950</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.0664</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> -0.1636</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  0.6071</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  1.1277</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{4} ]</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -175,14 +159,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Vdot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Vdot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_vdot.Rd</code></div>
     </div>
 
@@ -95,12 +77,14 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) first tensor in the dot product. Its conjugate is used
 if it's complex.</p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) second tensor in the dot product.</p></dd>
 
 </dl></div>
@@ -110,7 +94,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="vdot-input-other-out-none-gt-tensor-">vdot(input, other, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#vdot-input-other-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Computes the dot product (inner product) of two tensors. The vdot(a, b) function
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/torch_view_as_complex.html b/reference/torch_view_as_complex.html
index 5d3121fe8e..b4fcb5a3bc 100644
--- a/reference/torch_view_as_complex.html
+++ b/reference/torch_view_as_complex.html
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-      <img src="" class="logo" alt=""><h1>View_as_complex</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>View_as_complex</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_view_as_complex.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="view-as-complex-input-gt-tensor-">view_as_complex(input) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#view-as-complex-input-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a view of <code>input</code> as a complex tensor. For an input complex
@@ -112,7 +96,7 @@ <h2 id="view-as-complex-input-gt-tensor-">view_as_complex(input) -&gt; Tensor <a
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>torch_view_as_complex is only supported for tensors with
 <code>torch_dtype</code> <code><a href="torch_dtype.html">torch_float64()</a></code> and <code><a href="torch_dtype.html">torch_float32()</a></code>.  The input is
@@ -132,7 +116,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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@@ -141,14 +125,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_view_as_real.html b/reference/torch_view_as_real.html
index aba0b85e01..46f6da828b 100644
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+++ b/reference/torch_view_as_real.html
@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>View_as_real</h1>
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+
+      <h1>View_as_real</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_view_as_real.Rd</code></div>
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@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>self</dt>
+
+
+<dl><dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) the input tensor.</p></dd>
 
 </dl></div>
     <div class="section level2">
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-    
+
 
 
 <p>Returns a view of <code>input</code> as a real tensor. For an input complex tensor of
@@ -111,7 +95,7 @@ <h2 id="view-as-real-input-gt-tensor-">view_as_real(input) -&gt; Tensor <a class
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p><code>torch_view_as_real()</code> is only supported for tensors with <code>complex dtypes</code>.</p>
     </div>
@@ -128,7 +112,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -137,14 +121,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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index 217f2de40e..923706492a 100644
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Vstack</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
+
+      <h1>Vstack</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace.R" class="external-link"><code>R/gen-namespace.R</code></a></small>
       <div class="d-none name"><code>torch_vstack.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>tensors</dt>
+
+
+<dl><dt id="arg-tensors">tensors<a class="anchor" aria-label="anchor" href="#arg-tensors"></a></dt>
 <dd><p>(sequence of Tensors) sequence of tensors to concatenate</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="vstack-tensors-out-none-gt-tensor-">vstack(tensors, *, out=None) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#vstack-tensors-out-none-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Stack tensors in sequence vertically (row wise).</p>
@@ -130,7 +114,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{6,1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -139,14 +123,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/torch_where.html b/reference/torch_where.html
index a87eb9b02e..7992bdebc3 100644
--- a/reference/torch_where.html
+++ b/reference/torch_where.html
@@ -1,8 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Where"><title>Where — torch_where • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Where — torch_where"><meta property="og:description" content="Where"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Where — torch_where • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Where — torch_where"><meta name="description" content="Where"><meta property="og:description" content="Where"><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -10,77 +7,62 @@
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     <a class="navbar-brand me-2" href="../index.html">torch</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">0.13.0</small>
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+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item"><a class="nav-link" href="../news/index.html">Changelog</a></li>
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Where</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
+
+      <h1>Where</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/wrapers.R" class="external-link"><code>R/wrapers.R</code></a></small>
       <div class="d-none name"><code>torch_where.Rd</code></div>
     </div>
 
@@ -95,15 +77,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>condition</dt>
+
+
+<dl><dt id="arg-condition">condition<a class="anchor" aria-label="anchor" href="#arg-condition"></a></dt>
 <dd><p>(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y</p></dd>
 
 
-<dt>self</dt>
+<dt id="arg-self">self<a class="anchor" aria-label="anchor" href="#arg-self"></a></dt>
 <dd><p>(Tensor) values selected at indices where <code>condition</code> is <code>TRUE</code></p></dd>
 
 
-<dt>other</dt>
+<dt id="arg-other">other<a class="anchor" aria-label="anchor" href="#arg-other"></a></dt>
 <dd><p>(Tensor) values selected at indices where <code>condition</code> is <code>FALSE</code></p></dd>
 
 </dl></div>
@@ -114,7 +98,7 @@ <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     </div>
     <div class="section level2">
     <h2 id="where-condition-x-y-gt-tensor-">where(condition, x, y) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#where-condition-x-y-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Return a tensor of elements selected from either <code>x</code> or <code>y</code>, depending on <code>condition</code>.</p>
@@ -129,7 +113,7 @@ <h2 id="where-condition-x-y-gt-tensor-">where(condition, x, y) -&gt; Tensor <a c
     </div>
     <div class="section level2">
     <h2 id="where-condition-gt-tuple-of-longtensor-">where(condition) -&gt; tuple of LongTensor <a class="anchor" aria-label="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a></h2>
-    
+
 
 
 <p><code>torch_where(condition)</code> is identical to
@@ -140,19 +124,19 @@ <h2 id="where-condition-gt-tuple-of-longtensor-">where(condition) -&gt; tuple of
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="torch_is_installed.html">torch_is_installed</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
+<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span><span class="va">x</span> <span class="op">=</span> <span class="fu"><a href="torch_randn.html">torch_randn</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">y</span> <span class="op">=</span> <span class="fu"><a href="torch_ones.html">torch_ones</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">x</span></span></span>
 <span class="r-in"><span><span class="fu">torch_where</span><span class="op">(</span><span class="va">x</span> <span class="op">&gt;</span> <span class="fl">0</span>, <span class="va">x</span>, <span class="va">y</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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-      <img src="" class="logo" alt=""><h1>Zeros</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Zeros</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_zeros.Rd</code></div>
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@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>optional dimension names</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-">zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with the scalar value <code>0</code>, with the shape defined
@@ -151,7 +135,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{5} ]</span>
 </code></pre></div>
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-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -160,14 +144,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/torch_zeros_like.html b/reference/torch_zeros_like.html
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Zeros_like</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/HEAD/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
+
+      <h1>Zeros_like</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/creation-ops.R" class="external-link"><code>R/creation-ops.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-docs.R" class="external-link"><code>R/gen-namespace-docs.R</code></a>, <a href="https://github.com/mlverse/torch/blob/main/R/gen-namespace-examples.R" class="external-link"><code>R/gen-namespace-examples.R</code></a></small>
       <div class="d-none name"><code>torch_zeros_like.Rd</code></div>
     </div>
 
@@ -102,33 +84,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>input</dt>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
 <dd><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></dd>
 
 
-<dt>dtype</dt>
+<dt id="arg-dtype">dtype<a class="anchor" aria-label="anchor" href="#arg-dtype"></a></dt>
 <dd><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></dd>
 
 
-<dt>layout</dt>
+<dt id="arg-layout">layout<a class="anchor" aria-label="anchor" href="#arg-layout"></a></dt>
 <dd><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></dd>
 
 
-<dt>device</dt>
+<dt id="arg-device">device<a class="anchor" aria-label="anchor" href="#arg-device"></a></dt>
 <dd><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></dd>
 
 
-<dt>requires_grad</dt>
+<dt id="arg-requires-grad">requires_grad<a class="anchor" aria-label="anchor" href="#arg-requires-grad"></a></dt>
 <dd><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></dd>
 
 
-<dt>memory_format</dt>
+<dt id="arg-memory-format">memory_format<a class="anchor" aria-label="anchor" href="#arg-memory-format"></a></dt>
 <dd><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-">zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor <a class="anchor" aria-label="anchor" href="#zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a></h2>
-    
+
 
 
 <p>Returns a tensor filled with the scalar value <code>0</code>, with the same size as
@@ -137,7 +121,7 @@ <h2 id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-
     </div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
 the old <code>torch_zeros_like(input, out=output)</code> is equivalent to
@@ -157,7 +141,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{2,3} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -166,14 +150,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/unset_autocast.html b/reference/unset_autocast.html
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new file mode 100644
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index 1a9dfd41b4..522fa9a0f6 100644
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@@ -1,8 +1,5 @@
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-      <img src="" class="logo" alt=""><h1>Context-manager that enable anomaly detection for the autograd engine.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Context-manager that enable anomaly detection for the autograd engine.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>with_detect_anomaly.Rd</code></div>
     </div>
 
@@ -95,13 +77,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>code</dt>
+
+
+<dl><dt id="arg-code">code<a class="anchor" aria-label="anchor" href="#arg-code"></a></dt>
 <dd><p>Code that will be executed in the detect anomaly context.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    
+
 <ul><li><p>Running the forward pass with detection enabled will allow the backward
 pass to print the traceback of the forward operation that created the failing
 backward function.</p></li>
@@ -109,7 +93,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="warning">Warning<a class="anchor" aria-label="anchor" href="#warning"></a></h2>
-    
+
 
 <p>This mode should be enabled only for debugging as the different tests
 will slow down your program execution.</p>
@@ -133,31 +117,32 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="op">}</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error in (function (self, inputs, gradient, retain_graph, create_graph)  : </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Exception raised from unpack at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/saved_variable.cpp:194 (most recent call first):</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;) + 81 (0x10404eca1 in libc10.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt; const&amp;) + 98 (0x10404d342 in libc10.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr&lt;torch::autograd::Node&gt;) const + 2815 (0x124574d0f in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt;&amp;&amp;) + 168 (0x1236c7488 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #4: torch::autograd::Node::operator()(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt;&amp;&amp;) + 99 (0x1245456c3 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #5: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt;&amp;, torch::autograd::Node*, torch::autograd::InputBuffer&amp;, std::__1::shared_ptr&lt;torch::autograd::ReadyQueue&gt; const&amp;) + 3401 (0x12453ba89 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt; const&amp;) + 954 (0x12453a6da in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #7: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt; const&amp;, std::__1::shared_ptr&lt;torch::autograd::Node&gt;, torch::autograd::InputBuffer&amp;&amp;) + 374 (0x124544646 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #8: torch::autograd::Engine::execute(std::__1::vector&lt;torch::autograd::Edge, std::__1::allocator&lt;torch::autograd::Edge&gt;&gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, bool, bool, bool, std::__1::vector&lt;torch::autograd::Edge, std::__1::allocator&lt;torch::autograd::Edge&gt;&gt; const&amp;) + 2605 (0x12454268d in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #9: torch::autograd::run_backward(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, bool, bool, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, bool, bool) + 2129 (0x124528df1 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #10: torch::autograd::backward(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, c10::optional&lt;bool&gt;, bool, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;) + 104 (0x124529408 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #11: torch::autograd::VariableHooks::_backward(at::Tensor const&amp;, c10::ArrayRef&lt;at::Tensor&gt;, c10::optional&lt;at::Tensor&gt; const&amp;, c10::optional&lt;bool&gt;, bool) const + 435 (0x12457a9f3 in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #12: at::Tensor::_backward(c10::ArrayRef&lt;at::Tensor&gt;, c10::optional&lt;at::Tensor&gt; const&amp;, c10::optional&lt;bool&gt;, bool) const + 75 (0x120ebbeab in libtorch_cpu.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #13: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 427 (0x1068a3b2b in liblantern.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #14: std::__1::__function::__func&lt;cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_1, std::__1::allocator&lt;cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_1&gt;, void ()&gt;::operator()() + 54 (0x10513fbf6 in torchpkg.so)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #15: std::__1::packaged_task&lt;void ()&gt;::operator()() + 72 (0x10513df88 in torchpkg.so)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #16: EventLoop&lt;void&gt;::run() + 379 (0x10513ddbb in torchpkg.so)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #17: void* std::__1::__thread_proxy[abi:v160006]&lt;std::__1::tuple&lt;std::__1::unique_ptr&lt;std::__1::__thread_struct, std::__1::default_delete&lt;std::__1::__thread_struct&gt;&gt;, ThreadPool&lt;void&gt;::ThreadPool(int)::'lambda'()&gt;&gt;(void*) + 50 (0x10513db32 in torchpkg.so)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #18: _pthread_start + 125 (0x7ff805e541d3 in libsystem_pthread.dylib)</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> frame #19: thread_start + 15 (0x7ff805e4fbd3 in libsystem_pthread.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Exception raised from unpack at /Users/runner/work/libtorch-mac-m1/libtorch-mac-m1/pytorch/torch/csrc/autograd/saved_variable.cpp:187 (most recent call first):</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #0: std::__1::shared_ptr&lt;c10::(anonymous namespace)::PyTorchStyleBacktrace&gt; std::__1::make_shared[abi:ue170006]&lt;c10::(anonymous namespace)::PyTorchStyleBacktrace, c10::SourceLocation&amp;, void&gt;(c10::SourceLocation&amp;) + 121 (0x10df17639 in libc10.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #1: c10::Error::Error(c10::SourceLocation, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt;) + 54 (0x10df17776 in libc10.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #2: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string&lt;char, std::__1::char_traits&lt;char&gt;, std::__1::allocator&lt;char&gt;&gt; const&amp;) + 149 (0x10df14035 in libc10.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #3: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr&lt;torch::autograd::Node&gt;) const + 1987 (0x124d1e6f3 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #4: torch::autograd::generated::PowBackward1::apply(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt;&amp;&amp;) + 84 (0x123b951a4 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #5: torch::autograd::Node::operator()(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt;&amp;&amp;) + 104 (0x124ce5108 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #6: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt;&amp;, torch::autograd::Node*, torch::autograd::InputBuffer&amp;, std::__1::shared_ptr&lt;torch::autograd::ReadyQueue&gt; const&amp;) + 3039 (0x124cddbdf in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #7: torch::autograd::Engine::thread_main(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt; const&amp;) + 1140 (0x124cdc9d4 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #8: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr&lt;torch::autograd::GraphTask&gt; const&amp;, std::__1::shared_ptr&lt;torch::autograd::Node&gt;, torch::autograd::InputBuffer&amp;&amp;) + 415 (0x124ce455f in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #9: torch::autograd::Engine::execute(std::__1::vector&lt;torch::autograd::Edge, std::__1::allocator&lt;torch::autograd::Edge&gt;&gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, bool, bool, bool, std::__1::vector&lt;torch::autograd::Edge, std::__1::allocator&lt;torch::autograd::Edge&gt;&gt; const&amp;) + 1786 (0x124ce2dda in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #10: torch::autograd::run_backward(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, bool, bool, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, bool, bool) + 982 (0x124cca186 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #11: torch::autograd::backward(std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;, std::__1::optional&lt;bool&gt;, bool, std::__1::vector&lt;at::Tensor, std::__1::allocator&lt;at::Tensor&gt;&gt; const&amp;) + 107 (0x124cc968b in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #12: torch::autograd::VariableHooks::_backward(at::Tensor const&amp;, c10::ArrayRef&lt;at::Tensor&gt;, std::__1::optional&lt;at::Tensor&gt; const&amp;, std::__1::optional&lt;bool&gt;, bool) const + 296 (0x124d23918 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #13: at::Tensor::_backward(c10::ArrayRef&lt;at::Tensor&gt;, std::__1::optional&lt;at::Tensor&gt; const&amp;, std::__1::optional&lt;bool&gt;, bool) const + 73 (0x120ce9419 in libtorch_cpu.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #14: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 211 (0x111cea4c3 in liblantern.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #15: std::__1::__function::__func&lt;cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_2, std::__1::allocator&lt;cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_2&gt;, void ()&gt;::operator()() + 54 (0x110573836 in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #16: std::__1::packaged_task&lt;void ()&gt;::operator()() + 72 (0x110571908 in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #17: EventLoop&lt;void&gt;::run() + 413 (0x11057175d in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #18: void* std::__1::__thread_proxy[abi:v160006]&lt;std::__1::tuple&lt;std::__1::unique_ptr&lt;std::__1::__thread_struct, std::__1::default_delete&lt;std::__1::__thread_struct&gt;&gt;, ThreadPool&lt;void&gt;::ThreadPool(int)::'lambda'()&gt;&gt;(void*) + 50 (0x1105714b2 in torchpkg.so)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #19: _pthread_start + 125 (0x7ff80f7941d3 in libsystem_pthread.dylib)</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> frame #20: thread_start + 15 (0x7ff80f78fbd3 in libsystem_pthread.dylib)</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
@@ -166,14 +151,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
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diff --git a/reference/with_device.html b/reference/with_device.html
new file mode 100644
index 0000000000..4308240c94
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+++ b/reference/with_device.html
@@ -0,0 +1,8 @@
+<html>
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diff --git a/reference/with_enable_grad.html b/reference/with_enable_grad.html
index 7e70fc0387..6c4a313926 100644
--- a/reference/with_enable_grad.html
+++ b/reference/with_enable_grad.html
@@ -1,10 +1,7 @@
 <!DOCTYPE html>
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-<![endif]--><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Enable grad — with_enable_grad • torch</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Enable grad — with_enable_grad"><meta name="description" content="Context-manager that enables gradient calculation.
+Enables gradient calculation, if it has been disabled via with_no_grad."><meta property="og:description" content="Context-manager that enables gradient calculation.
+Enables gradient calculation, if it has been disabled via with_no_grad."><!-- Global site tag (gtag.js) - Google Analytics --><script async src="https://www.googletagmanager.com/gtag/js?id=G-9ZJSKW3L0N"></script><script>
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@@ -12,77 +9,62 @@
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-      <img src="" class="logo" alt=""><h1>Enable grad</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Enable grad</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>with_enable_grad.Rd</code></div>
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@@ -100,11 +82,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>code</dt>
+
+
+<dl><dt id="arg-code">code<a class="anchor" aria-label="anchor" href="#arg-code"></a></dt>
 <dd><p>code to be executed with gradient recording.</p></dd>
 
 
-<dt>.env</dt>
+<dt id="arg--env">.env<a class="anchor" aria-label="anchor" href="#arg--env"></a></dt>
 <dd><p>The environment to use for scoping.</p></dd>
 
 </dl></div>
@@ -115,7 +99,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
     </div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>local_enable_grad()</code>: Locally enable gradient computations.</p></li>
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@@ -137,7 +121,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ CPUFloatType{1} ]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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@@ -146,14 +130,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
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diff --git a/reference/with_no_grad.html b/reference/with_no_grad.html
index 77e7914ef1..f71683d185 100644
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-      <img src="" class="logo" alt=""><h1>Temporarily modify gradient recording.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/HEAD/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
+
+      <h1>Temporarily modify gradient recording.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/main/R/autograd.R" class="external-link"><code>R/autograd.R</code></a></small>
       <div class="d-none name"><code>with_no_grad.Rd</code></div>
     </div>
 
@@ -97,17 +79,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>code</dt>
+
+
+<dl><dt id="arg-code">code<a class="anchor" aria-label="anchor" href="#arg-code"></a></dt>
 <dd><p>code to be executed with no gradient recording.</p></dd>
 
 
-<dt>.env</dt>
+<dt id="arg--env">.env<a class="anchor" aria-label="anchor" href="#arg--env"></a></dt>
 <dd><p>The environment to use for scoping.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="functions">Functions<a class="anchor" aria-label="anchor" href="#functions"></a></h2>
-    
+
 <ul><li><p><code>local_no_grad()</code>: Disable autograd until it goes out of scope</p></li>
 </ul></div>
 
@@ -125,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [ Tensor (undefined) ]</span>
 </code></pre></div>
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diff --git a/reference/with_torch_manual_seed.html b/reference/with_torch_manual_seed.html
new file mode 100644
index 0000000000..320df37606
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new file mode 100644
index 0000000000..3228d5a6c0
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-[{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to torch","title":"Contributing to torch","text":"Many thanks wanting contribute torch! welcome kinds contributions, correcting typos via bug fixes feature additions.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to torch","text":"fix typos, spelling mistakes, grammatical errors, need make changes source .R file question, generated .Rd file. Thereafter, run tools/document.R regenerate documentation. (See Workflow .)","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"filing-bugs","dir":"","previous_headings":"","what":"Filing bugs","title":"Contributing to torch","text":"find bug torch, please open issue . Please provide detailed information reproduce bug. great also provide reprex.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"feature-requests","dir":"","previous_headings":"","what":"Feature requests","title":"Contributing to torch","text":"Feel free open issues , add feature-request tag. Also try searching ’s already open issue feature-request – case ’s better comment upvote instead opening new one.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Contributing to torch","text":"welcome contributed examples. Feel free open PR new examples. examples placed vignettes/examples folder. examples consist .R file .Rmd name just renders code. See mnist-mlp.R mnist-mlp.Rmd example. ’s important one can run example without manually downloading dataset/file. also add entry _pkgdown.yaml file example.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"code-contributions","dir":"","previous_headings":"","what":"Code contributions","title":"Contributing to torch","text":"many open issues github repo. ’s one item want work , can comment ask directions.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Code contributions","what":"Code style","title":"Contributing to torch","text":"New R code follow tidyverse style guide. can use styler package apply styles, simply run tools/style.sh script, also formats code removes whitespace. Please don’t re-style code nothing PR. New C/C++ code follow Google style guide. can use clang-format apply styles, simply run tools/style.sh, also formats code removes whitespace. Please don’t re-style code nothing PR. use roxygen2, Markdown syntax, build documentation package. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"requirements","dir":"","previous_headings":"Code contributions","what":"Requirements","title":"Contributing to torch","text":"R installation R Tools compilation (Windows) devtools package CMake compile lantern binaries","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"workflow","dir":"","previous_headings":"Code contributions","what":"Workflow","title":"Contributing to torch","text":"use devtools toolchain development, steps must done upfront. first time clone repository, run: first download LibTorch copy binaries deps folder working directory, , compile lantern binaries . command must run everytime modify lantern code, .e., code lives lantern/src. can run load torch, test interactively. Alternatively, run execute test suite. Finally, ’s important update documentation. Please always use custom tools/document.R script instead devtools::document(), need patch roxygen2 avoid running examples CRAN. ’re looking forward contributions!","code":"source(\"tools/buildlantern.R\") devtools::load_all() devtools::test()"},{"path":"https://torch.mlverse.org/docs/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Daniel Falbel Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/amp.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"Automatic Mixed Precision","text":"define model random training data showcase enabled AMP torch: Now let’s define model: train model without mixed precision can : enabled step 1. mixed precision, ie, allowing model run operations half precision possible, one simply run model computations (including loss computation) inside with_autocast context. additionally enable gradient scaling now introduce cuda_amp_grad_scaler() object use scale loss calling backward() also use wrap calls optimizer, can ‘unscale’ gradients actually updating weights. training loop now implemented :","code":"batch_size <- 512 # Try, for example, 128, 256, 513. in_size <- 4096 out_size <- 4096 num_layers <- 3 num_batches <- 50 epochs <- 3  # Creates data in default precision. # The same data is used for both default and mixed precision trials below. # You don't need to manually change inputs' dtype when enabling mixed precision. data <- lapply(1:num_batches, function(x) torch_randn(batch_size, in_size, device=\"cuda\")) targets <- lapply(1:num_batches, function(x) torch_randn(batch_size, out_size, device=\"cuda\")) make_model <- function(in_size, out_size, num_layers) {   layers <- list()   for (i in seq_len(num_layers-1)) {     layers <- c(layers, list(nn_linear(in_size, in_size), nn_relu()))   }   layers <- c(layers, list(nn_linear(in_size, out_size)))   nn_sequential(!!!layers)$cuda() } loss_fn <- nn_mse_loss()$cuda() net <- make_model(in_size, out_size, num_layers) opt <- optim_sgd(net$parameters, lr=0.1)  for (epoch in seq_len(epochs)) {   for (i in seq_along(data)) {          output <- net(data[[i]])     loss <- loss_fn(output, targets[[i]])        loss$backward()     opt$step()     opt$zero_grad()   } } loss_fn <- nn_mse_loss()$cuda() net <- make_model(in_size, out_size, num_layers) opt <- optim_sgd(net$parameters, lr=0.1)  for (epoch in seq_len(epochs)) {   for (i in seq_along(data)) {     with_autocast(device_type = \"cuda\", {       output <- net(data[[i]])       loss <- loss_fn(output, targets[[i]])       })          loss$backward()     opt$step()     opt$zero_grad()   } } loss_fn <- nn_mse_loss()$cuda() net <- make_model(in_size, out_size, num_layers) opt <- optim_sgd(net$parameters, lr=0.1) scaler <- cuda_amp_grad_scaler()  for (epoch in seq_len(epochs)) {   for (i in seq_along(data)) {     with_autocast(device_type = \"cuda\", {       output <- net(data[[i]])       loss <- loss_fn(output, targets[[i]])       })          scaler$scale(loss)$backward()     scaler$step(opt)     scaler$update()     opt$zero_grad()   } }"},{"path":"https://torch.mlverse.org/docs/articles/amp.html","id":"benchmark","dir":"Articles","previous_headings":"","what":"Benchmark","title":"Automatic Mixed Precision","text":"now write simple function allows us quickly switch feature can benchmark AMP:","code":"run <- function(autocast, scale) {   loss_fn <- nn_mse_loss()$cuda()   net <- make_model(in_size, out_size, num_layers)   opt <- optim_sgd(net$parameters, lr=0.1)   scaler <- cuda_amp_grad_scaler(enabled = scale)      for (epoch in seq_len(epochs)) {     for (i in seq_along(data)) {       with_autocast(enabled = autocast, device_type = \"cuda\", {         output <- net(data[[i]])         loss <- loss_fn(output, targets[[i]])         })              scaler$scale(loss)$backward()       scaler$step(opt)       scaler$update()       opt$zero_grad()     }   }   loss$item() }   system.time({run(autocast = FALSE, scale = FALSE)}) #>    user  system elapsed  #>   3.967   1.644   5.621 system.time({run(autocast = TRUE, scale = FALSE)}) #>    user  system elapsed  #>   1.857   0.591   2.462 system.time({run(autocast = TRUE, scale = TRUE)}) #>    user  system elapsed  #>   3.434   0.114   3.550"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"basic-univariate-distributions","dir":"Articles","previous_headings":"","what":"Basic univariate distributions","title":"Distributions","text":"Let’s start create new instance normal distribution: can draw samples : , draw multiple samples: can evaluate log probability values: , evaluate multiple log probabilities:","code":"n <- distr_normal(loc = 0, scale = 1) n #> torch_Normal () n$sample() #> torch_tensor #>  0.6614 #> [ CPUFloatType{1} ] n$sample(3) #> torch_tensor #>  0.2669 #>  0.0617 #>  0.6213 #> [ CPUFloatType{3,1} ] n$log_prob(0) #> torch_tensor #> -0.9189 #> [ CPUFloatType{1} ] log(dnorm(0)) # equivalent R code #> [1] -0.9189385 n$log_prob(c(0, 2, 4)) #> torch_tensor #> -0.9189 #> -2.9189 #> -8.9189 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"multiple-distributions","dir":"Articles","previous_headings":"","what":"Multiple distributions","title":"Distributions","text":"distribution can take tensor ’s parameters: object represents 3 independent Bernoulli distributions, one element tensor. can sample single observation: , batch n observations:","code":"b <- distr_bernoulli(probs = torch_tensor(c(0.25, 0.5, 0.75))) b #> torch_Bernoulli () b$sample() #> torch_tensor #>  0 #>  1 #>  1 #> [ CPUFloatType{3} ] b$sample(6) #> torch_tensor #>  0  0  1 #>  0  1  1 #>  0  0  1 #>  0  1  1 #>  0  1  1 #>  0  0  1 #> [ CPUFloatType{6,3} ]"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"using-distributions-within-models","dir":"Articles","previous_headings":"","what":"Using distributions within models","title":"Distributions","text":"log_prob method distributions can differentiated, thus, distributions can used train models torch. Let’s implement Gaussian linear model, first let’s simulate data plot:  can now define model: can now train model : can see parameter estimates : quickly compare glm() function:","code":"x <- torch_randn(100, 1) y <- 2*x + 1 + torch_randn(100, 1) plot(as.numeric(x), as.numeric(y)) GaussianLinear <- nn_module(   initialize = function() {     # this linear predictor will estimate the mean of the normal distribution     self$linear <- nn_linear(1, 1)     # this parameter will hold the estimate of the variability     self$scale <- nn_parameter(torch_ones(1))   },   forward = function(x) {     # we estimate the mean     loc <- self$linear(x)     # return a normal distribution     distr_normal(loc, self$scale)   } )  model <- GaussianLinear() opt <- optim_sgd(model$parameters, lr = 0.1)  for (i in 1:100) {   opt$zero_grad()   d <- model(x)   loss <- torch_mean(-d$log_prob(y))   loss$backward()   opt$step()   if (i %% 10 == 0)     cat(\"iter: \", i, \" loss: \", loss$item(), \"\\n\") } #> iter:  10  loss:  1.975726  #> iter:  20  loss:  1.790831  #> iter:  30  loss:  1.64495  #> iter:  40  loss:  1.532009  #> iter:  50  loss:  1.478054  #> iter:  60  loss:  1.465937  #> iter:  70  loss:  1.464229  #> iter:  80  loss:  1.464002  #> iter:  90  loss:  1.463971  #> iter:  100  loss:  1.463966 model$parameters #> $linear.weight #> torch_tensor #>  2.1256 #> [ CPUFloatType{1,1} ][ requires_grad = TRUE ] #>  #> $linear.bias #> torch_tensor #>  1.1215 #> [ CPUFloatType{1} ][ requires_grad = TRUE ] #>  #> $scale #> torch_tensor #>  1.0461 #> [ CPUFloatType{1} ][ requires_grad = TRUE ] summary(glm(as.numeric(y) ~ as.numeric(x))) #>  #> Call: #> glm(formula = as.numeric(y) ~ as.numeric(x)) #>  #> Coefficients: #>               Estimate Std. Error t value Pr(>|t|)     #> (Intercept)     1.1226     0.1057   10.62   <2e-16 *** #> as.numeric(x)   2.1259     0.1009   21.08   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for gaussian family taken to be 1.116565) #>  #>     Null deviance: 605.56  on 99  degrees of freedom #> Residual deviance: 109.42  on 98  degrees of freedom #> AIC: 298.79 #>  #> Number of Fisher Scoring iterations: 2"},{"path":"https://torch.mlverse.org/docs/articles/extending-autograd.html","id":"note","dir":"Articles","previous_headings":"","what":"Note","title":"Extending Autograd","text":"’s user’s responsibility use special functions forward’s ctx properly order ensure new autograd_function works properly autograd engine. save_for_backward() must used saving input ouput forward used later backward. mark_dirty() must used mark input modified inplace forward function. mark_non_differentiable() must used tell engine output differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/extending-autograd.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Extending Autograd","text":"can find code linear function: , give additional example function parametrized non-Tensor arguments:","code":"linear <- autograd_function(   forward = function(ctx, input, weight, bias = NULL) {     ctx$save_for_backward(input = input, weight = weight, bias = bias)     output <- input$mm(weight$t())     if (!is.null(bias))       output <- output + bias$unsqueeze(0)$expand_as(output)          output   },   backward = function(ctx, grad_output) {          s <- ctx$saved_variables          grads <- list(       input = NULL,       weight = NULL,       bias = NULL     )          if (ctx$needs_input_grad$input)       grads$input <- grad_output$mm(s$weight)          if (ctx$needs_input_grad$weight)       grads$weight <- grad_output$t()$mm(s$input)          if (!is.null(s$bias) && ctx$needs_input_grad$bias)       grads$bias <- grad_output$sum(dim = 0)          grads   } ) mul_constant <- autograd_function(   forward = function(ctx, tensor, constant) {     ctx$save_for_backward(constant = constant)     tensor * constant   },   backward = function(ctx, grad_output) {     v <- ctx$saved_variables     list(       tensor = grad_output * v$constant     )   } ) x <- torch_tensor(1, requires_grad = TRUE) o <- mul_constant(x, 2) o$backward() x$grad #> torch_tensor #>  2 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"single-element-indexing","dir":"Articles","previous_headings":"","what":"Single element indexing","title":"Indexing tensors","text":"Single element indexing 1-D tensors works mostly expected. Like R, 1-based. Unlike R though, accepts negative indices indexing end array. (R, negative indices used remove elements.) can also subset matrices higher dimensions arrays using syntax: Note one indexes multidimensional tensor fewer indices dimensions, torch’s behaviour differs R, flattens array. torch, missing indices considered complete slices :.","code":"x <- torch_tensor(1:10) x[1] #> torch_tensor #> 1 #> [ CPULongType{} ] x[-1] #> torch_tensor #> 10 #> [ CPULongType{} ] x <- x$reshape(shape = c(2,5)) x #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #> [ CPULongType{2,5} ] x[1,3] #> torch_tensor #> 3 #> [ CPULongType{} ] x[1,-1] #> torch_tensor #> 5 #> [ CPULongType{} ] x[1] #> torch_tensor #>  1 #>  2 #>  3 #>  4 #>  5 #> [ CPULongType{5} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"slicing-and-striding","dir":"Articles","previous_headings":"","what":"Slicing and striding","title":"Indexing tensors","text":"possible slice stride arrays extract sub-arrays number dimensions, different sizes original. best illustrated examples: can also use 1:10:2 syntax means: range 1 10, take every second item. example: Another special syntax N, meaning size specified dimension. Note: slicing behavior relies Non Standard Evaluation. requires expression passed [ exactly resulting R vector. allow dynamic dynamic indices, can create new slice using slc function. example: equivalent :","code":"x <- torch_tensor(1:10) x #> torch_tensor #>   1 #>   2 #>   3 #>   4 #>   5 #>   6 #>   7 #>   8 #>   9 #>  10 #> [ CPULongType{10} ] x[2:5] #> torch_tensor #>  2 #>  3 #>  4 #>  5 #> [ CPULongType{4} ] x[1:(-7)] #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPULongType{4} ] x[1:5:2] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ] x[5:N] #> torch_tensor #>   5 #>   6 #>   7 #>   8 #>   9 #>  10 #> [ CPULongType{6} ] x[1:5:2] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ] x[slc(start = 1, end = 5, step = 2)] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"getting-the-complete-dimension","dir":"Articles","previous_headings":"","what":"Getting the complete dimension","title":"Indexing tensors","text":"Like R, can take elements dimension leaving index empty. Consider matrix: following syntax give first row: give first 2 columns:","code":"x <- torch_randn(2, 3) x #> torch_tensor #> -1.1594 -1.9351  1.0987 #> -0.2400  0.9302  0.4572 #> [ CPUFloatType{2,3} ] x[1,] #> torch_tensor #> -1.1594 #> -1.9351 #>  1.0987 #> [ CPUFloatType{3} ] x[,1:2] #> torch_tensor #> -1.1594 -1.9351 #> -0.2400  0.9302 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"dropping-dimensions","dir":"Articles","previous_headings":"","what":"Dropping dimensions","title":"Indexing tensors","text":"default, indexing single integer, dimension dropped avoid singleton dimension: can optionally use drop = FALSE argument avoid dropping dimension.","code":"x <- torch_randn(2, 3) x[1,]$shape #> [1] 3 x[1,,drop = FALSE]$shape #> [1] 1 3"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"adding-a-new-dimension","dir":"Articles","previous_headings":"","what":"Adding a new dimension","title":"Indexing tensors","text":"’s possible add new dimension tensor using index-like syntax: can also use NULL instead newaxis:","code":"x <- torch_tensor(c(10)) x$shape #> [1] 1 x[, newaxis]$shape #> [1] 1 1 x[, newaxis, newaxis]$shape #> [1] 1 1 1 x[,NULL]$shape #> [1] 1 1"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"dealing-with-variable-number-of-indices","dir":"Articles","previous_headings":"","what":"Dealing with variable number of indices","title":"Indexing tensors","text":"Sometimes don’t know many dimensions tensor , know last available dimension, first one. subsume others, can use ..:","code":"z <- torch_tensor(1:125)$reshape(c(5,5,5)) z[1,..] #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #>  11  12  13  14  15 #>  16  17  18  19  20 #>  21  22  23  24  25 #> [ CPULongType{5,5} ] z[..,1] #> torch_tensor #>    1    6   11   16   21 #>   26   31   36   41   46 #>   51   56   61   66   71 #>   76   81   86   91   96 #>  101  106  111  116  121 #> [ CPULongType{5,5} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"indexing-with-vectors","dir":"Articles","previous_headings":"","what":"Indexing with vectors","title":"Indexing tensors","text":"Vector indexing also supported care must taken regarding performance , general much less performant slice based indexing. Note: Starting version 0.5.0, vector indexing torch follows R semantics, prior behavior similar numpy’s advanced indexing. use old behavior, consider using ?torch_index, ?torch_index_put torch_index_put_. can also use boolean vectors, example: examples also work index long boolean tensors, instead R vectors. ’s also possible index multi-dimensional boolean tensors:","code":"x <- torch_randn(4,4) x[c(1,3), c(1,3)] #> torch_tensor #>  0.6781 -0.5159 #> -1.0444 -1.0188 #> [ CPUFloatType{2,2} ] x[c(TRUE, FALSE, TRUE, FALSE), c(TRUE, FALSE, TRUE, FALSE)] #> torch_tensor #>  0.6781 -0.5159 #> -1.0444 -1.0188 #> [ CPUFloatType{2,2} ] x <- torch_tensor(rbind(   c(1,2,3),   c(4,5,6) )) x[x>3] #> torch_tensor #>  4 #>  5 #>  6 #> [ CPUFloatType{3} ]"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu","dir":"Articles","previous_headings":"Windows","what":"CPU","title":"Installation","text":"don’t GPU want install CPU version torch, can install : Windows distributions don’t Visual Studio runtime pre-installed observe error like: See instructions install .","code":"install.packages(\"torch\") Error in cpp_lantern_init(normalizePath(install_path())): C:\\Users\\User\\Documents\\R\\R-4.0.2\\library\\torch\\deps\\lantern.dll - The specified module could not be found."},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu","dir":"Articles","previous_headings":"Windows","what":"GPU","title":"Installation","text":"torch specific requirements terms CUDA CUDNN versions supports. recomment installing torch using pre-built binaries. See (#pre-built) information. Since version 0.1.1 torch supports GPU installation Windows. order use GPU’s torch need : CUDA compatible NVIDIA GPU. can find CUDA compatible GPU . properly installed NVIDIA CUDA toolkit version 11.7. CUDA v11.7, follow installation instructions . Note: version CUDA toolkit must match exactly ’s mentioed . installed cuDNN - version compatible CUDA v11.7. Follow installation instructions available . installed pre-requisites can install torch : followed default installation locations detect CUDA software installed automatically download GPU enabled Lantern binaries. can also specify CUDA env var something like Sys.setenv(CUDA=\"11.7\") want force specific version CUDA toolkit.","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu-1","dir":"Articles","previous_headings":"MacOS","what":"CPU","title":"Installation","text":"support CPU builds torch MacOS. MacOS can install torch :","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu-1","dir":"Articles","previous_headings":"MacOS","what":"GPU","title":"Installation","text":"Apple Silicon architecture support GPU MPS:","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu-2","dir":"Articles","previous_headings":"Linux","what":"CPU","title":"Installation","text":"install cpu version torch can run:","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu-2","dir":"Articles","previous_headings":"Linux","what":"GPU","title":"Installation","text":"torch specific requirements terms CUDA CUDNN versions supports. recomment installing torch using pre-built binaries. See (#pre-built) information. install GPU version torch linux must verify : NVIDIA CUDA compatible GPU. can find CUDA compatible GPU . correctly installed NVIDIA CUDA Toolkit versions 11.6 11.7, follow instructions . installed cuDNN (version compatible CUDA version). Follow installation instructions available . installed pre-requisites can install torch : followed default installation locations detect CUDA software installed automatically download GPU enabled Lantern binaries. can also specify CUDA env var something like Sys.setenv(CUDA=\"11.7\") want force specific version CUDA toolkit.","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"pre-built","dir":"Articles","previous_headings":"","what":"Installing from pre-built binaries","title":"Installation","text":"torch v0.9.1.9000 ’s now possible install torch pre-built package binaries CRAN like repository hosted Google Cloud Storage. currently provide pre-built binaries CPU (macOS, Linux Windows) GPU (Windows Linux). Packages provided CRAN-like repository bundles necessary execution, including CUDA CUDNN case GPU builds. means installing agree included software licenses. See PyTorch’s LICENSE CUDA libraries EULA. installing pre-built binaries, don’t need manually install CUDA cuDNN. CUDA installed, doesn’t need match installation ‘kind’ chosen . install pre-built binaries, can use following:","code":"options(timeout = 600) # increasing timeout is recommended since we will be downloading a 2GB file. # For Windows and Linux: \"cpu\", \"cu117\" are the only currently supported # For MacOS the supported are: \"cpu-intel\" or \"cpu-m1\" kind <- \"cu118\" version <- available.packages()[\"torch\",\"Version\"] options(repos = c(   torch = sprintf(\"https://torch-cdn.mlverse.org/packages/%s/%s/\", kind, version),   CRAN = \"https://cloud.r-project.org\" # or any other from which you want to install the other R dependencies. )) install.packages(\"torch\", type = \"binary\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"large-file-download-timeout","dir":"Articles","previous_headings":"Troubleshooting","what":"Large file download timeout","title":"Installation","text":"encounter timeout library download, , downloads end-warning : means encounter download timeout. , increase timeout value install_torch() like","code":"Warning messages: 1: In utils::download.file(library_url, temp_file) :   downloaded length 44901568 != reported length 141774525 2: In utils::download.file(library_url, temp_file) :   URL '...': Timeout of 60 seconds was reached 3: Failed to install Torch, manually run install_torch(). download from 'https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.7.1.zip' failed install_torch(timeout = 600)"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"file-based-download","dir":"Articles","previous_headings":"Troubleshooting","what":"File based download","title":"Installation","text":"cases reach download servers machine intend install torch , last resort install Torch Lantern library files. done 3 steps : 1- get download URLs files. 2- save files machine filesystem. use /tmp/ example . 3- install torch files","code":"get_install_libs_url() # then after making both files available into /tmp/ Sys.setenv(TORCH_URL=\"/tmp/libtorch-v1.13.1.zip\") Sys.setenv(LANTERN_URL=\"/tmp/lantern-0.9.1.9001+cpu+arm64-Darwin.zip\") torch::install_torch()"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"installing-older-versions","dir":"Articles","previous_headings":"Troubleshooting","what":"Installing older versions","title":"Installation","text":"v0.13.0 torch shifted using Google Cloud Storage service AWS S3 storage service required Lantern binaries. keep files GCS bucket long possible, might need remove point time. files backed new AWS S3 bucket using file structure, torch tries download URL starting https://storage.googleapis.com/torch-lantern-builds, now replace https://torch-cdn.mlverse.org. torch versions v0.10.0 v0.12.0 (included), able set environment variable LANTERN_BASE_URL=https://torch-cdn.mlverse.org/binaries/ point new address binaries. older versions torch, might need manually download file new address extract expected TORCH_HOME directory. Feel free open issue GitHub need help .","code":""},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"datasets-and-data-loaders","dir":"Articles","previous_headings":"","what":"Datasets and data loaders","title":"Loading data","text":"Central data ingestion preprocessing datasets data loaders. dataset object holds data use, data loader object load data dataset providing way access subsets data. using datasets data loaders process clearly organizing data passing components torch package, model training. Built torch premade datasets commonly used machine learning, MNIST handwriting dataset (mnist_dataset()). prebuilt datasets relate image recognition natural language processing. example use MNIST dataset dataloader. First, minst_dataset() function used create ds Dataset object. dataloader dl created query data. Finally, dataloader used coro::loop() iterate batches data: See vignettes/examples/mnist-cnn.R complete example. common situation unique set data isn’t included package ’ll need make custom Dataset subclass using dataset() function. custom Dataset subclass abstract R6 container data. need know information particular dataset, iterate . minimum, using dataset() create custom Dataset class ’ll want define following: name - convenience, keep track type data initialize - member function defining create object class. parameters, objects class , can give specific parameters usually different objects different data. .getitem - member function called dataloader goes pull new batch data. can include preprocessing function needed. Note function called extremely frequently, ’s advantageous make fast. .length - return amount data dataset, helpful users.","code":"# Create a dataset from included data ds <- mnist_dataset(   dir,    download = TRUE,    transform = function(x) {     x <- x$to(dtype = torch_float())/256     x[newaxis,..]   } )  # Create the loader to query the data in batches dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)  coro::loop(for (b in dl)) { # use the data from each batch `b` here # ... })"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"example-of-using-a-custom-dataset","dir":"Articles","previous_headings":"","what":"Example of using a custom Dataset","title":"Loading data","text":"may sound complicated base logic steps–complexity often comes data involved preprocessing . show create Dataset class train Allison Horst's penguins. addition, number helper functions can defined. , assume penguins already loaded, preprocessing consists removing lines NA values, transforming factors numbers starting 0, converting R data types torch tensors. .getitem, essentially decide data going used: variables besides species go x, predictor, species constitute y, target. Predictor target returned list, accessed batch[[1]] batch[[2]] training. Let’s create dataset , query ’s length, look first item: able iterate tuxes, need data loader (override default batch size 1): Calling .length() data loader (opposed dataset) return number batches : can create iterator inspect first batch: train network, can use coro::loop() iterate batches.","code":"library(palmerpenguins) library(magrittr)  penguins #> # A tibble: 344 × 8 #>    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g #>    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> #>  1 Adelie  Torgersen           39.1          18.7               181        3750 #>  2 Adelie  Torgersen           39.5          17.4               186        3800 #>  3 Adelie  Torgersen           40.3          18                 195        3250 #>  4 Adelie  Torgersen           NA            NA                  NA          NA #>  5 Adelie  Torgersen           36.7          19.3               193        3450 #>  6 Adelie  Torgersen           39.3          20.6               190        3650 #>  7 Adelie  Torgersen           38.9          17.8               181        3625 #>  8 Adelie  Torgersen           39.2          19.6               195        4675 #>  9 Adelie  Torgersen           34.1          18.1               193        3475 #> 10 Adelie  Torgersen           42            20.2               190        4250 #> # ℹ 334 more rows #> # ℹ 2 more variables: sex <fct>, year <int> penguins_dataset <- dataset(      name = \"penguins_dataset\",      initialize = function() {     self$data <- self$prepare_penguin_data()   },      .getitem = function(index) {          x <- self$data[index, 2:-1]     y <- self$data[index, 1]$to(torch_long())          list(x, y)   },      .length = function() {     self$data$size()[[1]]   },      prepare_penguin_data = function() {          input <- na.omit(penguins)      # conveniently, the categorical data are already factors     input$species <- as.numeric(input$species)     input$island <- as.numeric(input$island)     input$sex <- as.numeric(input$sex)          input <- as.matrix(input)     torch_tensor(input)   } ) tuxes <- penguins_dataset() tuxes$.length() #> [1] 333 tuxes$.getitem(1) #> [[1]] #> torch_tensor #>     3.0000 #>    39.1000 #>    18.7000 #>   181.0000 #>  3750.0000 #>     2.0000 #>  2007.0000 #> [ CPUFloatType{7} ] #>  #> [[2]] #> torch_tensor #> 1 #> [ CPULongType{} ] dl <- tuxes %>% dataloader(batch_size = 8) dl$.length() #> [1] 42 iter <- dl$.iter() b <- iter$.next() b #> [[1]] #> torch_tensor #>     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000 #>     3.0000    39.5000    17.4000   186.0000  3800.0000     1.0000  2007.0000 #>     3.0000    40.3000    18.0000   195.0000  3250.0000     1.0000  2007.0000 #>     3.0000    36.7000    19.3000   193.0000  3450.0000     1.0000  2007.0000 #>     3.0000    39.3000    20.6000   190.0000  3650.0000     2.0000  2007.0000 #>     3.0000    38.9000    17.8000   181.0000  3625.0000     1.0000  2007.0000 #>     3.0000    39.2000    19.6000   195.0000  4675.0000     2.0000  2007.0000 #>     3.0000    41.1000    17.6000   182.0000  3200.0000     1.0000  2007.0000 #> [ CPUFloatType{8,7} ] #>  #> [[2]] #> torch_tensor #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #> [ CPULongType{8} ]"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"training-with-data-loaders","dir":"Articles","previous_headings":"Example of using a custom Dataset","what":"Training with data loaders","title":"Loading data","text":"example network simple. (reality, want treat island categorical variable , either one-hot-encode embed .) still need optimizer: ’re ready train: example trained deep learning model using dataset() define custom class loaded batches data loader. using dataset data loader able write code split data preprocessing setup model training .","code":"net <- nn_module(   \"PenguinNet\",   initialize = function() {     self$fc1 <- nn_linear(7, 32)     self$fc2 <- nn_linear(32, 3)   },   forward = function(x) {     x %>%        self$fc1() %>%        nnf_relu() %>%        self$fc2() %>%        nnf_log_softmax(dim = 1)   } )  model <- net() optimizer <- optim_sgd(model$parameters, lr = 0.01) for (epoch in 1:10) {      l <- c()      coro::loop(for (b in dl) {     optimizer$zero_grad()     output <- model(b[[1]])     loss <- nnf_nll_loss(output, b[[2]])     loss$backward()     optimizer$step()     l <- c(l, loss$item())   })      cat(sprintf(\"Loss at epoch %d: %3f\\n\", epoch, mean(l))) } #> Loss at epoch 1: 560.068096 #> Loss at epoch 2: 2.068251 #> Loss at epoch 3: 2.068251 #> Loss at epoch 4: 2.068251 #> Loss at epoch 5: 2.068251 #> Loss at epoch 6: 2.068251 #> Loss at epoch 7: 2.068251 #> Loss at epoch 8: 2.068251 #> Loss at epoch 9: 2.068251 #> Loss at epoch 10: 2.068251"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"notes-on-efficiency","dir":"Articles","previous_headings":"","what":"Notes on efficiency","title":"Loading data","text":"using datasets data loaders may find certain conditions code running slowly ’d expect. situations overhead using dataloaders datasets can impact overall performance. may change time R/C++ integration Torch improves, now workarounds:","code":""},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"use--getbatch-instead-of--getitem","dir":"Articles","previous_headings":"Notes on efficiency","what":"Use .getbatch() instead of .getitem()","title":"Loading data","text":"default dataloader use .getitem() member function pull single datapoint individually. can speed switching using .getbatch() pull datapoints batch : many instances change exactly replace just .getitem .getbatch since often .getitem function written handle vectors indices. penguins example .getitem function used index select rows, work fine vector instead","code":"penguins_dataset_batching <- dataset(      name = \"penguins_dataset_batching\",      initialize = function() {     self$data <- self$prepare_penguin_data()   },      # the only change is that this went from .getitem to .getbatch   .getbatch = function(index) {          x <- self$data[index, 2:-1]     y <- self$data[index, 1]$to(torch_long())          list(x, y)   },      .length = function() {     self$data$size()[[1]]   },      prepare_penguin_data = function() {          input <- na.omit(penguins)      # conveniently, the categorical data are already factors     input$species <- as.numeric(input$species)     input$island <- as.numeric(input$island)     input$sex <- as.numeric(input$sex)          input <- as.matrix(input)     torch_tensor(input)   } )"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"remove-dataset-dataloader-and-manually-define-the-function-calls","dir":"Articles","previous_headings":"Notes on efficiency","what":"Remove dataset dataloader and manually define the function calls","title":"Loading data","text":"switching .getbatch provide benefit expecting also remove dataset entirely manually pass data. point trading readability code convenience speed.","code":"input <- na.omit(penguins)  # conveniently, the categorical data are already factors input$species <- as.numeric(input$species) input$island <- as.numeric(input$island) input$sex <- as.numeric(input$sex)  input <- as.matrix(input) input <- torch_tensor(input)  data_x <- input[, 2:-1] data_y <- input[, 1]$to(torch_long())  batch_size <- 8 num_data_points <- data_y$size(1) num_batches <- floor(num_data_points/batch_size)  for(epoch in 1:10){    # rearrange the data each epoch   permute <- torch_randperm(num_data_points) + 1L   data_x <- data_x[permute]   data_y <- data_y[permute]      # manually loop through the batches   for(batch_idx in 1:num_batches){      # here index is a vector of the indices in the batch     index <- (batch_size*(batch_idx-1) + 1):(batch_idx*batch_size)          x <- data_x[index]     y <- data_y[index]$to(torch_long())      optimizer$zero_grad()     output <- model(x)     loss <- nnf_nll_loss(output, y)     loss$backward()     optimizer$step()     l <- c(l, loss$item())   }      cat(sprintf(\"Loss at epoch %d: %3f\\n\", epoch, mean(l))) }"},{"path":"https://torch.mlverse.org/docs/articles/memory-management.html","id":"cpu","dir":"Articles","previous_headings":"","what":"CPU","title":"Memory management","text":"CPU, torch possibly call R garbage collector two moments: Every 4GB memory allocated LibTorch make call R garbage collector cleans dangling tensors. 4GB threshold can controled setting option torch.threshold_call_gc, example using: option must set calling library(torch) calling torch function first time, setting applied torch starts . torch fails allocating enough memory creating new tensor, garbage collector called allocation retried. Note: OS’s (specially UNIX based) ’s hard allocation fail ’s large, system tries use swap. much swaping used ’s possible system hangs completely. R session hanging, convinced enough memory operations, try setting lower value torch.threshold_call_gc option, call GC often make sure tensors quickly released memory. Note though, calling GC often adds lot overhead, probably slow program execution.","code":"options(torch.threshold_call_gc = 4000)"},{"path":"https://torch.mlverse.org/docs/articles/memory-management.html","id":"cuda","dir":"Articles","previous_headings":"","what":"CUDA","title":"Memory management","text":"CUDA memory tends scarcer CPU memory, also, allocation must faster otherwise allocation overhead can counterbalance speed GPU. make allocations fast avoid segmentation, LibTorch uses caching allocator manage GPU memory, ie. LibTorch allocated CUDA memory won’t give back operation system, instead reuses memory future allocations. means nvidia-smi nvtop report amount memory used tensors, memory LibTorch reserved OS. can use torch::cuda_memory_summary() query exactly memory used LibTorch. Like CPU allocator, torch’s CUDA allocator also call R garbage collector situations cleanup tensors might dangling. torch’s implementation R garbage collector called whenever reusing cached block fails. case, GC called retry getting new block. However, unlike CPU case, allocations failures rare, reusing block common programs LibTorch never reserves large chunk memory, causing GC called almost every allocation (calling GC time consuming). fix , torch allocator call faster GC moments make full collection others. don’t make collection current reserved memory (cached memory) divided total GPU memory smaller 20%. can controlled torch.cuda_allocator_reserved_rate default 0.2. make full collection current allocated memory (memory used tensors) divided total device memory larger 80%. can controlled via torch.cuda_allocator_allocated_rate default 0.8. make full collection current allocated memory larger divided current reserved memory larger 80%. controlled torch.cuda_allocator_allocated_reserved_rate default 0.8. cases light collection made. Equivalent calling gc(full = FALSE) R. options can help tuning allocation performance depending program running. Besides R specific options can set LibTorch options via environment variables described . behavior caching allocator can controlled via environment variable PYTORCH_CUDA_ALLOC_CONF. format PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2><value2>... Available options: max_split_size_mb prevents allocator splitting blocks larger size (MB). can help prevent fragmentation may allow borderline workloads complete without running memory. Performance cost can range ‘zero’ ‘substatial’ depending allocation patterns. Default value unlimited, .e. blocks can split. memory_stats() memory_summary() methods useful tuning. option used last resort workload aborting due ‘memory’ showing large amount inactive split blocks. roundup_power2_divisions helps rounding requested allocation size nearest power-2 division making better use blocks. current CUDACachingAllocator, sizes rounded multiple blocks size 512, works fine smaller sizes. However, can inefficient large near-allocations go different size blocks re-use blocks minimized. might create lots unused blocks waste GPU memory capacity. option enables rounding allocation size nearest power-2 division. example, need round-size 1200 number divisions 4, size 1200 lies 1024 2048 4 divisions , values 1024, 1280, 1536, 1792. , allocation size 1200 rounded 1280 nearest ceiling power-2 division. garbage_collection_threshold helps actively reclaiming unused GPU memory avoid triggering expensive sync--reclaim-operation (release_cached_blocks), can unfavorable latency-critical GPU applications (e.g., servers). Upon setting threshold (e.g., 0.8), allocator start reclaiming GPU memory blocks GPU memory capacity usage exceeds threshold (.e., 80% total memory allocated GPU application). algorithm prefers free old & unused blocks first avoid freeing blocks actively reused. threshold value greater 0.0 less 1.0. Notice garbage collector refered R garbage collector LibTorch’s collector, releases memory cache OS.","code":""},{"path":"https://torch.mlverse.org/docs/articles/memory-management.html","id":"mps","dir":"Articles","previous_headings":"","what":"MPS","title":"Memory management","text":"Memory management MPS devices similar strategy used CUDA devices, except ’s currently configuration tuning possible. R garbage collector called whenever ’s available memory GPU thus, possibly deleting Tensors. Allocation retried fails, OOM error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Modifying torch source code","text":"torch R package composed multiple abstraction layers R code finally calls LibTorch code. Depending part code want modify might need different build requirements setting . special requirements project , ’s always possible use standard devtools tools work torch. guide intended contributors torch codebase quickly get set able modify source code without spending much time understanding internals. torch R package composed following layers, going top : R code calling torch_ functions. R code calling cpp_torch_ functions. Rcpp (C++) code calling lantern_torch_ functions. Lantern (C++) code calling LibTorch functions. Depending function 2, 3 4. might auto-generated (via tools/torchgen) package . next sections explain contribute code depending layer modifying.","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"r-source-code","dir":"Articles","previous_headings":"","what":"R source code","title":"Modifying torch source code","text":"Modifying R source code simplest case shouldn’t required contributors tools standard R development toolbox (like RTools Windows C++ compiler MacOS). standard devtools just work . Ie, can clone project GitHub, modify R source code, including documentation run devtools::load_all() first time run devtools::load_all(), torch installation prompt appear LibTorch lantern binaries downloaded installed inside inst directory installation. Subsequent calls work expected. Note loading torch twice without restarting R session causes session crash. Unbfortunatelly don’t know yet fix bug ’s related problems correctly unloading shared libraries. restart session calling devtools::load_all() . Also note functions might call devtools::load_all() internally, like eg. devtools::test() devtools::document(). Also, running Build Check RStudio pane fail, calls Rcpp::CompileAttributes without calling Makevars file instructions, required torch work correctly. use devtools::check() pass argument devtools::check(document=FALSE). reason described .","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"documentation","dir":"Articles","previous_headings":"R source code","what":"Documentation","title":"Modifying torch source code","text":"torch registers roxygen2 roclets handle examples sessions roxygen2 documentation. Unfortunately unaware method automatically register roclets without messing DESCRIPTION’s collate field. recommended way update documentation torch using tools/document.R script. can either call source(\"tools/document.R\") , recommended, run: terminal. advantage since devtools::document() called internally calls pkgload::load_all(), won’t crash R session forget restart session (see note ).","code":"Rscript tools/document.R"},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"rcpp-code","dir":"Articles","previous_headings":"","what":"Rcpp code","title":"Modifying torch source code","text":"Modifying Rcpp source code src similar modifying C++ R packages. pay attention calling lantern_* functions though. lantern_* functions return void* leak referring objects correctly freed use. best way handle always assign variable correct type defined inst/include/torch_types.h specially defined C++ namespace torch {}. use kind objects also gain ability return R without write custom Rcpp code implemented SEXP() operator.","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"lantern-code","dir":"Articles","previous_headings":"","what":"Lantern code","title":"Modifying torch source code","text":"order modify Lantern source code first need make sure environment variable BUILD_LANTERN set 1. avoid forgetting set can add .Renviron using, eg usethis::edit_r_environ(). flag trigger configure file call lantern target Makevars. Building lantern requires CMake path. can install CMake major platforms following instructions install page. BUILD_LANTERN=1 run devtools::load_all(), Lantern compiled part workflow. see new directory called build-lantern package directory containing files related Lantern build. Lantern source code located src/lantern/src. lantern CMakeLists file located src/lantern/CMakeLists.txt. can also take look lantern target src/Makevars.get sense CMake commands called building Lantern source. default, rebuilding Lantern incremental, ie, calling devtools::load_all() rebuild modified parts Lantern code. Depending extend modifications Lantern source code might need completely clean build directory, can simply removing build-lantern directory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/python-to-r.html","id":"loading-models-saved-in-python","dir":"Articles","previous_headings":"","what":"Loading models saved in python","title":"Python to R","text":"Currently way load models python rewrite model architecture R. parameter names must identical. complete example Python R shown . extension Serialization vignette. artificial neural net implemented Python. Note final line uses torch.save(). saved .pth object can load R. example use case training model Python using Shiny develop GUI predictions trained model.","code":"import torch import numpy as np  #Make up data  madeUpData_x = np.random.rand(1000,100) madeUpData_y = np.random.rand(1000)  #Convert to categorical madeUpData_y = madeUpData_y.round()  train_py_X = torch.from_numpy(madeUpData_x).float()  train_py_Y = torch.from_numpy(madeUpData_y).float()  #Note that this class must be replicated identically in R class simpleMLP(torch.nn.Module):     def __init__(self):         super(simpleMLP, self).__init__()         self.modelFit = torch.nn.Sequential(             torch.nn.Linear(100,20),             torch.nn.ReLU(),             torch.nn.Linear(20,1),             torch.nn.Sigmoid())                  def forward(self, x):         x =self.modelFit(x)          return x  model = simpleMLP()   def modelTrainer(data_X,data_Y,model):     criterion = torch.nn.BCELoss()     optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)      for epoch in range(100):          optimizer.zero_grad()          yhat = model(data_X)          loss = criterion(yhat,data_Y.unsqueeze(1))          loss.backward()         optimizer.step()  modelTrainer(data_X = train_py_X,data_Y = train_py_Y,model = model)  #----------------------------------------------------------------- #save the model   #Note that model.state_dict() comes out as an ordered dictionary #The code below converts to a dictionary stateDict = dict(model.state_dict())  #Note the argument _use_new_zipfile_serialization torch.save(stateDict,f=\"path/babyTest.pth\",            _use_new_zipfile_serialization=True) library(torch)  #Make up some test data #note that proper installation of torch will yield no errors when we run #this code y <- torch_tensor(array(runif(8),dim = c(2,2,2)),dtype = torch_float64())  #Note the identical names between the Python class definition and our #class definition simpleMLP <- torch::nn_module(   \"simpleMLP\",   initialize = function(){          self$modelFit <- nn_sequential(nn_linear(100,20),                                    nn_relu(),                                    nn_linear(20,1),                                    nn_sigmoid())        },   forward = function(x){     self$modelFit(x)     } )   model <- simpleMLP()  state_dict <- torch::load_state_dict(\"p/babyTest.pth\") model$load_state_dict(state_dict)  #Note that the dtype set in R has to match the made up data from Python #More generally if reading new data into R you must ensure that it matches the  #dtype that the model was trained with in Python newData = torch_tensor(array(rnorm(n=1000),dim=c(10,100)),dtype=torch_float32())  predictMe <- model(newData)"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-tensors","dir":"Articles","previous_headings":"","what":"Saving tensors","title":"Serialization","text":"can save object type torch_tensor disk using:","code":"x <- torch_randn(10, 10) torch_save(x, \"tensor.pt\") x_ <- torch_load(\"tensor.pt\")  torch_allclose(x, x_) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-modules","dir":"Articles","previous_headings":"","what":"Saving modules","title":"Serialization","text":"torch_save torch_load functions also work nn_modules objects. saving nn_module, object serialized including model structure ’s state.","code":"module <- nn_module(   \"my_module\",   initialize = function() {     self$fc1 <- nn_linear(10, 10)     self$fc2 <- nn_linear(10, 1)   },   forward = function(x) {     x %>%        self$fc1() %>%        self$fc2()   } )  model <- module() torch_save(model, \"model.pt\") model_ <- torch_load(\"model.pt\")  # input tensor x <- torch_randn(50, 10) torch_allclose(model(x), model_(x)) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"loading-models-saved-in-python","dir":"Articles","previous_headings":"","what":"Loading models saved in python","title":"Serialization","text":"Currently way load models python rewrite model architecture R. parameter names must identical. can save PyTorch model state_dict using: can reload state dict R reload model : can find working examples torchvision. example AlexNet model.","code":"torch.save(model, fpath, _use_new_zipfile_serialization=True) state_dict <- load_state_dict(fpath) model <- Model() model$load_state_dict(state_dict)"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-optimizer-state","dir":"Articles","previous_headings":"","what":"Saving optimizer state","title":"Serialization","text":"can save state optimizers can continue training exact position. order use state_dict() load_state_dict() methods optimizer combined torch_save:","code":"model <- nn_linear(1, 1) opt <- optim_adam(model$parameters)  train_x <- torch_randn(100, 1) train_y <- torch_randn(100, 1)  loss <- nnf_mse_loss(model(train_x), train_y) loss$backward() opt$step() #> NULL # Now let's save the optimizer state tmp <- tempfile() torch_save(opt$state_dict(), tmp)  # And now let's create a new optimizer and load back opt2 <- optim_adam(model$parameters) opt2$load_state_dict(torch_load(tmp))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numpy_t","dir":"Articles > Tensor","previous_headings":"","what":"numpy_T","title":"Tensor objects","text":"Tensor dimensions reversed. n number dimensions x, x$numpy_T() equivalent x$permute(n, n-1, ..., 1).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"abs","dir":"Articles > Tensor","previous_headings":"","what":"abs","title":"Tensor objects","text":"abs() -> Tensor See ?torch_abs","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"abs_","dir":"Articles > Tensor","previous_headings":"","what":"abs_","title":"Tensor objects","text":"abs_() -> Tensor -place version $abs","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"absolute","dir":"Articles > Tensor","previous_headings":"","what":"absolute","title":"Tensor objects","text":"absolute() -> Tensor Alias [$abs()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"absolute_","dir":"Articles > Tensor","previous_headings":"","what":"absolute_","title":"Tensor objects","text":"absolute_() -> Tensor -place version $absolute Alias [$abs_()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acos","dir":"Articles > Tensor","previous_headings":"","what":"acos","title":"Tensor objects","text":"acos() -> Tensor See ?torch_acos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acos_","dir":"Articles > Tensor","previous_headings":"","what":"acos_","title":"Tensor objects","text":"acos_() -> Tensor -place version $acos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acosh","dir":"Articles > Tensor","previous_headings":"","what":"acosh","title":"Tensor objects","text":"acosh() -> Tensor See ?torch_acosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acosh_","dir":"Articles > Tensor","previous_headings":"","what":"acosh_","title":"Tensor objects","text":"acosh_() -> Tensor -place version $acosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"add","dir":"Articles > Tensor","previous_headings":"","what":"add","title":"Tensor objects","text":"add(, *, alpha=1) -> Tensor Add scalar tensor self tensor. alpha specified, element scaled alpha used. tensor, shape must broadcastable shape underlying tensor See ?torch_add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"add_","dir":"Articles > Tensor","previous_headings":"","what":"add_","title":"Tensor objects","text":"add_(, *, alpha=1) -> Tensor -place version $add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addbmm","dir":"Articles > Tensor","previous_headings":"","what":"addbmm","title":"Tensor objects","text":"addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor See ?torch_addbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addbmm_","dir":"Articles > Tensor","previous_headings":"","what":"addbmm_","title":"Tensor objects","text":"addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor -place version $addbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcdiv","dir":"Articles > Tensor","previous_headings":"","what":"addcdiv","title":"Tensor objects","text":"addcdiv(tensor1, tensor2, *, value=1) -> Tensor See ?torch_addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcdiv_","dir":"Articles > Tensor","previous_headings":"","what":"addcdiv_","title":"Tensor objects","text":"addcdiv_(tensor1, tensor2, *, value=1) -> Tensor -place version $addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcmul","dir":"Articles > Tensor","previous_headings":"","what":"addcmul","title":"Tensor objects","text":"addcmul(tensor1, tensor2, *, value=1) -> Tensor See ?torch_addcmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcmul_","dir":"Articles > Tensor","previous_headings":"","what":"addcmul_","title":"Tensor objects","text":"addcmul_(tensor1, tensor2, *, value=1) -> Tensor -place version $addcmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmm","dir":"Articles > Tensor","previous_headings":"","what":"addmm","title":"Tensor objects","text":"addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor See ?torch_addmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmm_","dir":"Articles > Tensor","previous_headings":"","what":"addmm_","title":"Tensor objects","text":"addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor -place version $addmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmv","dir":"Articles > Tensor","previous_headings":"","what":"addmv","title":"Tensor objects","text":"addmv(mat, vec, *, beta=1, alpha=1) -> Tensor See ?torch_addmv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmv_","dir":"Articles > Tensor","previous_headings":"","what":"addmv_","title":"Tensor objects","text":"addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor -place version $addmv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addr","dir":"Articles > Tensor","previous_headings":"","what":"addr","title":"Tensor objects","text":"addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor See ?torch_addr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addr_","dir":"Articles > Tensor","previous_headings":"","what":"addr_","title":"Tensor objects","text":"addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor -place version $addr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"align_as","dir":"Articles > Tensor","previous_headings":"","what":"align_as","title":"Tensor objects","text":"align_as() -> Tensor Permutes dimensions self tensor match dimension order tensor, adding size-one dims new names. operation useful explicit broadcasting names (see examples). dims self must named order use method. resulting tensor view original tensor. dimension names self must present $names. may contain named dimensions self$names; output tensor size-one dimension new names. align tensor specific order, use $align_to.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples","dir":"Articles > Tensor","previous_headings":"align_as","what":"Examples:","title":"Tensor objects","text":"","code":"# Example 1: Applying a mask mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H')) imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C')) imgs$masked_fill_(mask$align_as(imgs), 0)  # Example 2: Applying a per-channel-scale scale_channels <- function(input, scale) {   scale <- scale$refine_names(\"C\")   input * scale$align_as(input) }  num_channels <- 3 scale <- torch_randn(num_channels, names='C') imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C')) more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W')) videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))  # scale_channels is agnostic to the dimension order of the input scale_channels(imgs, scale) scale_channels(more_imgs, scale) scale_channels(videos, scale)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning","dir":"Articles > Tensor","previous_headings":"align_as","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"align_to","dir":"Articles > Tensor","previous_headings":"","what":"align_to","title":"Tensor objects","text":"Permutes dimensions self tensor match order specified names, adding size-one dims new names. dims self must named order use method. resulting tensor view original tensor. dimension names self must present names. names may contain additional names self$names; output tensor size-one dimension new names.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments","dir":"Articles > Tensor","previous_headings":"align_to","what":"Arguments:","title":"Tensor objects","text":"names (iterable str): desired dimension ordering output tensor. May contain one Ellipsis expanded unmentioned dim names self.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-1","dir":"Articles > Tensor","previous_headings":"align_to > Examples:","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"all","dir":"Articles > Tensor","previous_headings":"","what":"all","title":"Tensor objects","text":"() -> bool Returns TRUE elements tensor TRUE, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-2","dir":"Articles > Tensor","previous_headings":"all","what":"Examples:","title":"Tensor objects","text":"(dim, keepdim=FALSE, =NULL) -> Tensor Returns TRUE elements row tensor given dimension dim TRUE, FALSE otherwise. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see ?torch_squeeze()), resulting output tensor 1 fewer dimension input.","code":"a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$all()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-1","dir":"Articles > Tensor","previous_headings":"all","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension reduce keepdim (bool): whether output tensor dim retained (Tensor, optional): output tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-3","dir":"Articles > Tensor","previous_headings":"all","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_rand(4, 2)$to(dtype = torch_bool()) a a$all(dim=2) a$all(dim=1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"allclose","dir":"Articles > Tensor","previous_headings":"","what":"allclose","title":"Tensor objects","text":"allclose(, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor See ?torch_allclose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"angle","dir":"Articles > Tensor","previous_headings":"","what":"angle","title":"Tensor objects","text":"angle() -> Tensor See ?torch_angle","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"any","dir":"Articles > Tensor","previous_headings":"","what":"any","title":"Tensor objects","text":"() -> bool Returns TRUE elements tensor TRUE, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-4","dir":"Articles > Tensor","previous_headings":"any","what":"Examples:","title":"Tensor objects","text":"(dim, keepdim=FALSE, =NULL) -> Tensor Returns TRUE elements row tensor given dimension dim TRUE, FALSE otherwise. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see ?torch_squeeze()), resulting output tensor 1 fewer dimension input.","code":"a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$any()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-2","dir":"Articles > Tensor","previous_headings":"any","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension reduce keepdim (bool): whether output tensor dim retained (Tensor, optional): output tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-5","dir":"Articles > Tensor","previous_headings":"any","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_randn(4, 2) < 0 a a$any(2) a$any(1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"apply_","dir":"Articles > Tensor","previous_headings":"","what":"apply_","title":"Tensor objects","text":"apply_(callable) -> Tensor Applies function callable element tensor, replacing element value returned callable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note","dir":"Articles > Tensor","previous_headings":"apply_","what":"Note:","title":"Tensor objects","text":"function works CPU tensors used code sections require high performance.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argmax","dir":"Articles > Tensor","previous_headings":"","what":"argmax","title":"Tensor objects","text":"argmax(dim=NULL, keepdim=FALSE) -> LongTensor See ?torch_argmax","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argmin","dir":"Articles > Tensor","previous_headings":"","what":"argmin","title":"Tensor objects","text":"argmin(dim=NULL, keepdim=FALSE) -> LongTensor See ?torch_argmin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argsort","dir":"Articles > Tensor","previous_headings":"","what":"argsort","title":"Tensor objects","text":"argsort(dim=-1, descending=FALSE) -> LongTensor See ?torch_argsort","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"as_strided","dir":"Articles > Tensor","previous_headings":"","what":"as_strided","title":"Tensor objects","text":"as_strided(size, stride, storage_offset=0) -> Tensor See [torch_as_strided()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"as_subclass","dir":"Articles > Tensor","previous_headings":"","what":"as_subclass","title":"Tensor objects","text":"as_subclass(cls) -> Tensor Makes cls instance data pointer self. Changes output mirror changes self, output stays attached autograd graph. cls must subclass Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asin","dir":"Articles > Tensor","previous_headings":"","what":"asin","title":"Tensor objects","text":"asin() -> Tensor See ?torch_asin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asin_","dir":"Articles > Tensor","previous_headings":"","what":"asin_","title":"Tensor objects","text":"asin_() -> Tensor -place version $asin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asinh","dir":"Articles > Tensor","previous_headings":"","what":"asinh","title":"Tensor objects","text":"asinh() -> Tensor See ?torch_asinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asinh_","dir":"Articles > Tensor","previous_headings":"","what":"asinh_","title":"Tensor objects","text":"asinh_() -> Tensor -place version $asinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan","dir":"Articles > Tensor","previous_headings":"","what":"atan","title":"Tensor objects","text":"atan() -> Tensor See ?torch_atan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan2","dir":"Articles > Tensor","previous_headings":"","what":"atan2","title":"Tensor objects","text":"atan2() -> Tensor See [torch_atan2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan2_","dir":"Articles > Tensor","previous_headings":"","what":"atan2_","title":"Tensor objects","text":"atan2_() -> Tensor -place version $atan2","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan_","dir":"Articles > Tensor","previous_headings":"","what":"atan_","title":"Tensor objects","text":"atan_() -> Tensor -place version $atan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atanh","dir":"Articles > Tensor","previous_headings":"","what":"atanh","title":"Tensor objects","text":"atanh() -> Tensor See ?torch_atanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atanh_","dir":"Articles > Tensor","previous_headings":"","what":"atanh_","title":"Tensor objects","text":"-place version $atanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"backward","dir":"Articles > Tensor","previous_headings":"","what":"backward","title":"Tensor objects","text":"Computes gradient current tensor w.r.t. graph leaves. graph differentiated using chain rule. tensor non-scalar (.e. data one element) requires gradient, function additionally requires specifying gradient. tensor matching type location, contains gradient differentiated function w.r.t. self. function accumulates gradients leaves - might need zero $grad attributes set NULL calling . See Default gradient layouts<default-grad-layouts> details memory layout accumulated gradients.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-3","dir":"Articles > Tensor","previous_headings":"backward","what":"Arguments:","title":"Tensor objects","text":"gradient (Tensor NULL): Gradient w.r.t. tensor. tensor, automatically converted Tensor require grad unless create_graph TRUE. NULL values can specified scalar Tensors ones don’t require grad. NULL value acceptable argument optional. retain_graph (bool, optional): FALSE, graph used compute grads freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional): TRUE, graph derivative constructed, allowing compute higher order derivative products. Defaults FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"baddbmm","dir":"Articles > Tensor","previous_headings":"","what":"baddbmm","title":"Tensor objects","text":"baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor See ?torch_baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"baddbmm_","dir":"Articles > Tensor","previous_headings":"","what":"baddbmm_","title":"Tensor objects","text":"baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor -place version $baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bernoulli","dir":"Articles > Tensor","previous_headings":"","what":"bernoulli","title":"Tensor objects","text":"bernoulli(*, generator=NULL) -> Tensor Returns result tensor \\(\\texttt{result[]}\\) independently sampled \\(\\text{Bernoulli}(\\texttt{self[]})\\). self must floating point dtype, result dtype. See ?torch_bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bernoulli_","dir":"Articles > Tensor","previous_headings":"","what":"bernoulli_","title":"Tensor objects","text":"bernoulli_(p=0.5, *, generator=NULL) -> Tensor Fills location self independent sample \\(\\text{Bernoulli}(\\texttt{p})\\). self can integral dtype. bernoulli_(p_tensor, *, generator=NULL) -> Tensor p_tensor tensor containing probabilities used drawing binary random number. \\(\\text{}^{th}\\) element self tensor set value sampled \\(\\text{Bernoulli}(\\texttt{p\\_tensor[]})\\). self can integral dtype, p_tensor must floating point dtype. See also $bernoulli ?torch_bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bfloat16","dir":"Articles > Tensor","previous_headings":"","what":"bfloat16","title":"Tensor objects","text":"bfloat16(memory_format=torch_preserve_format) -> Tensor self$bfloat16() equivalent self$(torch_bfloat16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-4","dir":"Articles > Tensor","previous_headings":"bfloat16","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bincount","dir":"Articles > Tensor","previous_headings":"","what":"bincount","title":"Tensor objects","text":"bincount(weights=NULL, minlength=0) -> Tensor See ?torch_bincount","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_and","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_and","title":"Tensor objects","text":"bitwise_and() -> Tensor See [torch_bitwise_and()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_and_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_and_","title":"Tensor objects","text":"bitwise_and_() -> Tensor -place version $bitwise_and","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_not","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_not","title":"Tensor objects","text":"bitwise_not() -> Tensor See [torch_bitwise_not()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_not_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_not_","title":"Tensor objects","text":"bitwise_not_() -> Tensor -place version $bitwise_not","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_or","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_or","title":"Tensor objects","text":"bitwise_or() -> Tensor See [torch_bitwise_or()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_or_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_or_","title":"Tensor objects","text":"bitwise_or_() -> Tensor -place version $bitwise_or","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_xor","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_xor","title":"Tensor objects","text":"bitwise_xor() -> Tensor See [torch_bitwise_xor()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_xor_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_xor_","title":"Tensor objects","text":"bitwise_xor_() -> Tensor -place version $bitwise_xor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bmm","dir":"Articles > Tensor","previous_headings":"","what":"bmm","title":"Tensor objects","text":"bmm(batch2) -> Tensor See ?torch_bmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bool","dir":"Articles > Tensor","previous_headings":"","what":"bool","title":"Tensor objects","text":"bool(memory_format=torch_preserve_format) -> Tensor self$bool() equivalent self$(torch_bool). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-5","dir":"Articles > Tensor","previous_headings":"bool","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"byte","dir":"Articles > Tensor","previous_headings":"","what":"byte","title":"Tensor objects","text":"byte(memory_format=torch_preserve_format) -> Tensor self$byte() equivalent self$(torch_uint8). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-6","dir":"Articles > Tensor","previous_headings":"byte","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cauchy_","dir":"Articles > Tensor","previous_headings":"","what":"cauchy_","title":"Tensor objects","text":"cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor Fills tensor numbers drawn Cauchy distribution: \\[ f(x) = \\dfrac{1}{\\pi} \\dfrac{\\sigma}{(x - \\text{median})^2 + \\sigma^2} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ceil","dir":"Articles > Tensor","previous_headings":"","what":"ceil","title":"Tensor objects","text":"ceil() -> Tensor See ?torch_ceil","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ceil_","dir":"Articles > Tensor","previous_headings":"","what":"ceil_","title":"Tensor objects","text":"ceil_() -> Tensor -place version $ceil","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"char","dir":"Articles > Tensor","previous_headings":"","what":"char","title":"Tensor objects","text":"char(memory_format=torch_preserve_format) -> Tensor self$char() equivalent self$(torch_int8). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-7","dir":"Articles > Tensor","previous_headings":"char","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky","dir":"Articles > Tensor","previous_headings":"","what":"cholesky","title":"Tensor objects","text":"cholesky(upper=FALSE) -> Tensor See ?torch_cholesky","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky_inverse","dir":"Articles > Tensor","previous_headings":"","what":"cholesky_inverse","title":"Tensor objects","text":"cholesky_inverse(upper=FALSE) -> Tensor See [torch_cholesky_inverse()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky_solve","dir":"Articles > Tensor","previous_headings":"","what":"cholesky_solve","title":"Tensor objects","text":"cholesky_solve(input2, upper=FALSE) -> Tensor See [torch_cholesky_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"chunk","dir":"Articles > Tensor","previous_headings":"","what":"chunk","title":"Tensor objects","text":"chunk(chunks, dim=0) -> List Tensors See ?torch_chunk","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clamp","dir":"Articles > Tensor","previous_headings":"","what":"clamp","title":"Tensor objects","text":"clamp(min, max) -> Tensor See ?torch_clamp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clamp_","dir":"Articles > Tensor","previous_headings":"","what":"clamp_","title":"Tensor objects","text":"clamp_(min, max) -> Tensor -place version $clamp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clone","dir":"Articles > Tensor","previous_headings":"","what":"clone","title":"Tensor objects","text":"clone(memory_format=torch_preserve_format()) -> Tensor Returns copy self tensor. copy size data type self.","code":"x <- torch_tensor(1) y <- x$clone()  x$add_(1) y"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-1","dir":"Articles > Tensor","previous_headings":"clone","what":"Note:","title":"Tensor objects","text":"Unlike copy_(), function recorded computation graph. Gradients propagating cloned tensor propagate original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-8","dir":"Articles > Tensor","previous_headings":"clone","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"conj","dir":"Articles > Tensor","previous_headings":"","what":"conj","title":"Tensor objects","text":"conj() -> Tensor See ?torch_conj","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"contiguous","dir":"Articles > Tensor","previous_headings":"","what":"contiguous","title":"Tensor objects","text":"contiguous(memory_format=torch_contiguous_format) -> Tensor Returns contiguous memory tensor containing data self tensor. self tensor already specified memory format, function returns self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-9","dir":"Articles > Tensor","previous_headings":"contiguous","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_contiguous_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"copy_","dir":"Articles > Tensor","previous_headings":"","what":"copy_","title":"Tensor objects","text":"copy_(src, non_blocking=FALSE) -> Tensor Copies elements src self tensor returns self. src tensor must broadcastable self tensor. may different data type reside different device.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-10","dir":"Articles > Tensor","previous_headings":"copy_","what":"Arguments:","title":"Tensor objects","text":"src (Tensor): source tensor copy non_blocking (bool): TRUE copy CPU GPU, copy may occur asynchronously respect host. cases, argument effect.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cos","dir":"Articles > Tensor","previous_headings":"","what":"cos","title":"Tensor objects","text":"cos() -> Tensor See ?torch_cos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cos_","dir":"Articles > Tensor","previous_headings":"","what":"cos_","title":"Tensor objects","text":"cos_() -> Tensor -place version $cos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cosh","dir":"Articles > Tensor","previous_headings":"","what":"cosh","title":"Tensor objects","text":"cosh() -> Tensor See ?torch_cosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cosh_","dir":"Articles > Tensor","previous_headings":"","what":"cosh_","title":"Tensor objects","text":"cosh_() -> Tensor -place version $cosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cpu","dir":"Articles > Tensor","previous_headings":"","what":"cpu","title":"Tensor objects","text":"cpu(memory_format=torch_preserve_format) -> Tensor Returns copy object CPU memory. object already CPU memory correct device, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-11","dir":"Articles > Tensor","previous_headings":"cpu","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cross","dir":"Articles > Tensor","previous_headings":"","what":"cross","title":"Tensor objects","text":"cross(, dim=-1) -> Tensor See ?torch_cross","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cuda","dir":"Articles > Tensor","previous_headings":"","what":"cuda","title":"Tensor objects","text":"cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -> Tensor Returns copy object CUDA memory. object already CUDA memory correct device, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-12","dir":"Articles > Tensor","previous_headings":"cuda","what":"Arguments:","title":"Tensor objects","text":"device (torch_device): destination GPU device. Defaults current CUDA device. non_blocking (bool): TRUE source pinned memory, copy asynchronous respect host. Otherwise, argument effect. Default: FALSE. memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cummax","dir":"Articles > Tensor","previous_headings":"","what":"cummax","title":"Tensor objects","text":"cummax(dim) -> (Tensor, Tensor) See ?torch_cummax","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cummin","dir":"Articles > Tensor","previous_headings":"","what":"cummin","title":"Tensor objects","text":"cummin(dim) -> (Tensor, Tensor) See ?torch_cummin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cumprod","dir":"Articles > Tensor","previous_headings":"","what":"cumprod","title":"Tensor objects","text":"cumprod(dim, dtype=NULL) -> Tensor See ?torch_cumprod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cumsum","dir":"Articles > Tensor","previous_headings":"","what":"cumsum","title":"Tensor objects","text":"cumsum(dim, dtype=NULL) -> Tensor See ?torch_cumsum","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"data_ptr","dir":"Articles > Tensor","previous_headings":"","what":"data_ptr","title":"Tensor objects","text":"data_ptr() -> int Returns address first element self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"deg2rad","dir":"Articles > Tensor","previous_headings":"","what":"deg2rad","title":"Tensor objects","text":"deg2rad() -> Tensor See [torch_deg2rad()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"deg2rad_","dir":"Articles > Tensor","previous_headings":"","what":"deg2rad_","title":"Tensor objects","text":"deg2rad_() -> Tensor -place version $deg2rad","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dense_dim","dir":"Articles > Tensor","previous_headings":"","what":"dense_dim","title":"Tensor objects","text":"dense_dim() -> int self sparse COO tensor (.e., torch_sparse_coo layout), returns number dense dimensions. Otherwise, throws error. See also $sparse_dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dequantize","dir":"Articles > Tensor","previous_headings":"","what":"dequantize","title":"Tensor objects","text":"dequantize() -> Tensor Given quantized Tensor, dequantize return dequantized float Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"det","dir":"Articles > Tensor","previous_headings":"","what":"det","title":"Tensor objects","text":"det() -> Tensor See ?torch_det","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"detach","dir":"Articles > Tensor","previous_headings":"","what":"detach","title":"Tensor objects","text":"Returns new Tensor, detached current graph. result never require gradient.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-2","dir":"Articles > Tensor","previous_headings":"detach","what":"Note:","title":"Tensor objects","text":"Returned Tensor shares storage original one. -place modifications either seen, may trigger errors correctness checks. IMPORTANT NOTE: Previously, -place size / stride / storage changes (resize_ / resize_as_ / set_ / transpose_) returned tensor also update original tensor. Now, -place changes update original tensor anymore, instead trigger error. sparse tensors: -place indices / values changes (zero_ / copy_ / add_) returned tensor update original tensor anymore, instead trigger error.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"detach_","dir":"Articles > Tensor","previous_headings":"","what":"detach_","title":"Tensor objects","text":"Detaches Tensor graph created , making leaf. Views detached -place.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"device","dir":"Articles > Tensor","previous_headings":"","what":"device","title":"Tensor objects","text":"torch_device Tensor .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diag","dir":"Articles > Tensor","previous_headings":"","what":"diag","title":"Tensor objects","text":"diag(diagonal=0) -> Tensor See ?torch_diag","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diag_embed","dir":"Articles > Tensor","previous_headings":"","what":"diag_embed","title":"Tensor objects","text":"diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor See [torch_diag_embed()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diagflat","dir":"Articles > Tensor","previous_headings":"","what":"diagflat","title":"Tensor objects","text":"diagflat(offset=0) -> Tensor See ?torch_diagflat","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diagonal","dir":"Articles > Tensor","previous_headings":"","what":"diagonal","title":"Tensor objects","text":"diagonal(offset=0, dim1=0, dim2=1) -> Tensor See ?torch_diagonal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"digamma","dir":"Articles > Tensor","previous_headings":"","what":"digamma","title":"Tensor objects","text":"digamma() -> Tensor See ?torch_digamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"digamma_","dir":"Articles > Tensor","previous_headings":"","what":"digamma_","title":"Tensor objects","text":"digamma_() -> Tensor -place version $digamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dim","dir":"Articles > Tensor","previous_headings":"","what":"dim","title":"Tensor objects","text":"dim() -> int Returns number dimensions self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dist","dir":"Articles > Tensor","previous_headings":"","what":"dist","title":"Tensor objects","text":"dist(, p=2) -> Tensor See ?torch_dist","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"div","dir":"Articles > Tensor","previous_headings":"","what":"div","title":"Tensor objects","text":"div(value) -> Tensor See ?torch_div","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"div_","dir":"Articles > Tensor","previous_headings":"","what":"div_","title":"Tensor objects","text":"div_(value) -> Tensor -place version $div","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dot","dir":"Articles > Tensor","previous_headings":"","what":"dot","title":"Tensor objects","text":"dot(tensor2) -> Tensor See ?torch_dot","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"double","dir":"Articles > Tensor","previous_headings":"","what":"double","title":"Tensor objects","text":"double(memory_format=torch_preserve_format) -> Tensor self$double() equivalent self$(torch_float64). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-13","dir":"Articles > Tensor","previous_headings":"double","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eig","dir":"Articles > Tensor","previous_headings":"","what":"eig","title":"Tensor objects","text":"eig(eigenvectors=FALSE) -> (Tensor, Tensor) See ?torch_eig","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"element_size","dir":"Articles > Tensor","previous_headings":"","what":"element_size","title":"Tensor objects","text":"element_size() -> int Returns size bytes individual element.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-6","dir":"Articles > Tensor","previous_headings":"element_size","what":"Examples:","title":"Tensor objects","text":"","code":"torch_tensor(c(1))$element_size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eq","dir":"Articles > Tensor","previous_headings":"","what":"eq","title":"Tensor objects","text":"eq() -> Tensor See ?torch_eq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eq_","dir":"Articles > Tensor","previous_headings":"","what":"eq_","title":"Tensor objects","text":"eq_() -> Tensor -place version $eq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"equal","dir":"Articles > Tensor","previous_headings":"","what":"equal","title":"Tensor objects","text":"equal() -> bool See ?torch_equal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erf","dir":"Articles > Tensor","previous_headings":"","what":"erf","title":"Tensor objects","text":"erf() -> Tensor See ?torch_erf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erf_","dir":"Articles > Tensor","previous_headings":"","what":"erf_","title":"Tensor objects","text":"erf_() -> Tensor -place version $erf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfc","dir":"Articles > Tensor","previous_headings":"","what":"erfc","title":"Tensor objects","text":"erfc() -> Tensor See ?torch_erfc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfc_","dir":"Articles > Tensor","previous_headings":"","what":"erfc_","title":"Tensor objects","text":"erfc_() -> Tensor -place version $erfc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfinv","dir":"Articles > Tensor","previous_headings":"","what":"erfinv","title":"Tensor objects","text":"erfinv() -> Tensor See ?torch_erfinv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfinv_","dir":"Articles > Tensor","previous_headings":"","what":"erfinv_","title":"Tensor objects","text":"erfinv_() -> Tensor -place version $erfinv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exp","dir":"Articles > Tensor","previous_headings":"","what":"exp","title":"Tensor objects","text":"exp() -> Tensor See ?torch_exp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exp_","dir":"Articles > Tensor","previous_headings":"","what":"exp_","title":"Tensor objects","text":"exp_() -> Tensor -place version $exp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expand","dir":"Articles > Tensor","previous_headings":"","what":"expand","title":"Tensor objects","text":"expand(*sizes) -> Tensor Returns new view self tensor singleton dimensions expanded larger size. Passing -1 size dimension means changing size dimension. Tensor can also expanded larger number dimensions, new ones appended front. new dimensions, size set -1. Expanding tensor allocate new memory, creates new view existing tensor dimension size one expanded larger size setting stride 0. dimension size 1 can expanded arbitrary value without allocating new memory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-14","dir":"Articles > Tensor","previous_headings":"expand","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): desired expanded size","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-2","dir":"Articles > Tensor","previous_headings":"expand","what":"Warning:","title":"Tensor objects","text":"one element expanded tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-7","dir":"Articles > Tensor","previous_headings":"expand","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(c(1,2,3), ncol = 1)) x$size() x$expand(c(3, 4)) x$expand(c(-1, 4))  # -1 means not changing the size of that dimension"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expand_as","dir":"Articles > Tensor","previous_headings":"","what":"expand_as","title":"Tensor objects","text":"expand_as() -> Tensor Expand tensor size . self$expand_as() equivalent self$expand(.size()). Please see $expand information expand.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-15","dir":"Articles > Tensor","previous_headings":"expand_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor size .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expm1","dir":"Articles > Tensor","previous_headings":"","what":"expm1","title":"Tensor objects","text":"expm1() -> Tensor See [torch_expm1()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expm1_","dir":"Articles > Tensor","previous_headings":"","what":"expm1_","title":"Tensor objects","text":"expm1_() -> Tensor -place version $expm1","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exponential_","dir":"Articles > Tensor","previous_headings":"","what":"exponential_","title":"Tensor objects","text":"exponential_(lambd=1, *, generator=NULL) -> Tensor Fills self tensor elements drawn exponential distribution: \\[ f(x) = \\lambda e^{-\\lambda x} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fft","dir":"Articles > Tensor","previous_headings":"","what":"fft","title":"Tensor objects","text":"fft(signal_ndim, normalized=FALSE) -> Tensor See ?torch_fft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fill_","dir":"Articles > Tensor","previous_headings":"","what":"fill_","title":"Tensor objects","text":"fill_(value) -> Tensor Fills self tensor specified value.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fill_diagonal_","dir":"Articles > Tensor","previous_headings":"","what":"fill_diagonal_","title":"Tensor objects","text":"fill_diagonal_(fill_value, wrap=FALSE) -> Tensor Fill main diagonal tensor least 2-dimensions. dims>2, dimensions input must equal length. function modifies input tensor -place, returns input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-16","dir":"Articles > Tensor","previous_headings":"fill_diagonal_","what":"Arguments:","title":"Tensor objects","text":"fill_value (Scalar): fill value wrap (bool): diagonal ‘wrapped’ N columns tall matrices.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-8","dir":"Articles > Tensor","previous_headings":"fill_diagonal_","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_zeros(3, 3) a$fill_diagonal_(5) b <- torch_zeros(7, 3) b$fill_diagonal_(5) c <- torch_zeros(7, 3) c$fill_diagonal_(5, wrap=TRUE)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flatten","dir":"Articles > Tensor","previous_headings":"","what":"flatten","title":"Tensor objects","text":"flatten(input, start_dim=0, end_dim=-1) -> Tensor see ?torch_flatten","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flip","dir":"Articles > Tensor","previous_headings":"","what":"flip","title":"Tensor objects","text":"flip(dims) -> Tensor See ?torch_flip","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fliplr","dir":"Articles > Tensor","previous_headings":"","what":"fliplr","title":"Tensor objects","text":"fliplr() -> Tensor See ?torch_fliplr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flipud","dir":"Articles > Tensor","previous_headings":"","what":"flipud","title":"Tensor objects","text":"flipud() -> Tensor See ?torch_flipud","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"float","dir":"Articles > Tensor","previous_headings":"","what":"float","title":"Tensor objects","text":"float(memory_format=torch_preserve_format) -> Tensor self$float() equivalent self$(torch_float32). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-17","dir":"Articles > Tensor","previous_headings":"float","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor","dir":"Articles > Tensor","previous_headings":"","what":"floor","title":"Tensor objects","text":"floor() -> Tensor See ?torch_floor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_","dir":"Articles > Tensor","previous_headings":"","what":"floor_","title":"Tensor objects","text":"floor_() -> Tensor -place version $floor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_divide","dir":"Articles > Tensor","previous_headings":"","what":"floor_divide","title":"Tensor objects","text":"floor_divide(value) -> Tensor See [torch_floor_divide()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_divide_","dir":"Articles > Tensor","previous_headings":"","what":"floor_divide_","title":"Tensor objects","text":"floor_divide_(value) -> Tensor -place version $floor_divide","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fmod","dir":"Articles > Tensor","previous_headings":"","what":"fmod","title":"Tensor objects","text":"fmod(divisor) -> Tensor See ?torch_fmod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fmod_","dir":"Articles > Tensor","previous_headings":"","what":"fmod_","title":"Tensor objects","text":"fmod_(divisor) -> Tensor -place version $fmod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"frac","dir":"Articles > Tensor","previous_headings":"","what":"frac","title":"Tensor objects","text":"frac() -> Tensor See ?torch_frac","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"frac_","dir":"Articles > Tensor","previous_headings":"","what":"frac_","title":"Tensor objects","text":"frac_() -> Tensor -place version $frac","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gather","dir":"Articles > Tensor","previous_headings":"","what":"gather","title":"Tensor objects","text":"gather(dim, index) -> Tensor See ?torch_gather","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ge","dir":"Articles > Tensor","previous_headings":"","what":"ge","title":"Tensor objects","text":"ge() -> Tensor See ?torch_ge","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ge_","dir":"Articles > Tensor","previous_headings":"","what":"ge_","title":"Tensor objects","text":"ge_() -> Tensor -place version $ge","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"geometric_","dir":"Articles > Tensor","previous_headings":"","what":"geometric_","title":"Tensor objects","text":"geometric_(p, *, generator=NULL) -> Tensor Fills self tensor elements drawn geometric distribution: \\[ f(X=k) = p^{k - 1} (1 - p) \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"geqrf","dir":"Articles > Tensor","previous_headings":"","what":"geqrf","title":"Tensor objects","text":"geqrf() -> (Tensor, Tensor) See ?torch_geqrf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ger","dir":"Articles > Tensor","previous_headings":"","what":"ger","title":"Tensor objects","text":"ger(vec2) -> Tensor See ?torch_ger","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"get_device","dir":"Articles > Tensor","previous_headings":"","what":"get_device","title":"Tensor objects","text":"get_device() -> Device ordinal (Integer) CUDA tensors, function returns device ordinal GPU tensor resides. CPU tensors, error thrown.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-9","dir":"Articles > Tensor","previous_headings":"get_device","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(3, 4, 5, device='cuda:0') x$get_device() x$cpu()$get_device()  # RuntimeError: get_device is not implemented for type torch_FloatTensor"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"grad","dir":"Articles > Tensor","previous_headings":"","what":"grad","title":"Tensor objects","text":"attribute NULL default becomes Tensor first time call backward computes gradients self. attribute contain gradients computed future calls [backward()] accumulate (add) gradients .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gt","dir":"Articles > Tensor","previous_headings":"","what":"gt","title":"Tensor objects","text":"gt() -> Tensor See ?torch_gt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gt_","dir":"Articles > Tensor","previous_headings":"","what":"gt_","title":"Tensor objects","text":"gt_() -> Tensor -place version $gt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"half","dir":"Articles > Tensor","previous_headings":"","what":"half","title":"Tensor objects","text":"half(memory_format=torch_preserve_format) -> Tensor self$half() equivalent self$(torch_float16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-18","dir":"Articles > Tensor","previous_headings":"half","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"hardshrink","dir":"Articles > Tensor","previous_headings":"","what":"hardshrink","title":"Tensor objects","text":"hardshrink(lambd=0.5) -> Tensor See [torch_nn.functional.hardshrink()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"has_names","dir":"Articles > Tensor","previous_headings":"","what":"has_names","title":"Tensor objects","text":"TRUE tensor’s dimensions named. Otherwise, FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"histc","dir":"Articles > Tensor","previous_headings":"","what":"histc","title":"Tensor objects","text":"histc(bins=100, min=0, max=0) -> Tensor See ?torch_histc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ifft","dir":"Articles > Tensor","previous_headings":"","what":"ifft","title":"Tensor objects","text":"ifft(signal_ndim, normalized=FALSE) -> Tensor See ?torch_ifft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"imag","dir":"Articles > Tensor","previous_headings":"","what":"imag","title":"Tensor objects","text":"Returns new tensor containing imaginary values self tensor. returned tensor self share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-3","dir":"Articles > Tensor","previous_headings":"imag","what":"Warning:","title":"Tensor objects","text":"[imag()] supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-10","dir":"Articles > Tensor","previous_headings":"imag","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(4, dtype=torch_cfloat()) x x$imag"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_add","dir":"Articles > Tensor","previous_headings":"","what":"index_add","title":"Tensor objects","text":"index_add(tensor1, dim, index, tensor2) -> Tensor --place version $index_add_. tensor1 corresponds self $index_add_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_add_","dir":"Articles > Tensor","previous_headings":"","what":"index_add_","title":"Tensor objects","text":"index_add_(dim, index, tensor) -> Tensor Accumulate elements tensor self tensor adding indices order given index. example, dim == 0 index[] == j,  th row tensor added j th row self. dim th dimension tensor must size length index (must vector), dimensions must match self, error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-3","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Note:","title":"Tensor objects","text":"circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch_backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-19","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices tensor select tensor (Tensor): tensor containing values add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-11","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_ones(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1L, 4L, 3L)) x$index_add_(1, index, t)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_copy","dir":"Articles > Tensor","previous_headings":"","what":"index_copy","title":"Tensor objects","text":"index_copy(tensor1, dim, index, tensor2) -> Tensor --place version $index_copy_. tensor1 corresponds self $index_copy_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_copy_","dir":"Articles > Tensor","previous_headings":"","what":"index_copy_","title":"Tensor objects","text":"index_copy_(dim, index, tensor) -> Tensor Copies elements tensor self tensor selecting indices order given index. example, dim == 0 index[] == j,  th row tensor copied j th row self. dim th dimension tensor must size length index (must vector), dimensions must match self, error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-20","dir":"Articles > Tensor","previous_headings":"index_copy_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices tensor select tensor (Tensor): tensor containing values copy","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-12","dir":"Articles > Tensor","previous_headings":"index_copy_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_zeros(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 5, 3)) x$index_copy_(1, index, t)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_fill","dir":"Articles > Tensor","previous_headings":"","what":"index_fill","title":"Tensor objects","text":"index_fill(tensor1, dim, index, value) -> Tensor --place version $index_fill_. tensor1 corresponds self $index_fill_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_fill_","dir":"Articles > Tensor","previous_headings":"","what":"index_fill_","title":"Tensor objects","text":"index_fill_(dim, index, val) -> Tensor Fills elements self tensor value val selecting indices order given index.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-21","dir":"Articles > Tensor","previous_headings":"index_fill_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices self tensor fill val (float): value fill ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-13","dir":"Articles > Tensor","previous_headings":"index_fill_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 3), dtype = torch_long()) x$index_fill_(1, index, -1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_put","dir":"Articles > Tensor","previous_headings":"","what":"index_put","title":"Tensor objects","text":"index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor -place version $index_put_. tensor1 corresponds self $index_put_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_put_","dir":"Articles > Tensor","previous_headings":"","what":"index_put_","title":"Tensor objects","text":"index_put_(indices, value, accumulate=FALSE) -> Tensor Puts values tensor value tensor self using indices specified indices (tuple Tensors). expression tensor.index_put_(indices, value) equivalent tensor[indices] = value. Returns self. accumulate TRUE, elements value added self. accumulate FALSE, behavior undefined indices contain duplicate elements.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-22","dir":"Articles > Tensor","previous_headings":"index_put_","what":"Arguments:","title":"Tensor objects","text":"indices (tuple LongTensor): tensors used index self. value (Tensor): tensor dtype self. accumulate (bool): whether accumulate self","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_select","dir":"Articles > Tensor","previous_headings":"","what":"index_select","title":"Tensor objects","text":"index_select(dim, index) -> Tensor See [torch_index_select()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"indices","dir":"Articles > Tensor","previous_headings":"","what":"indices","title":"Tensor objects","text":"indices() -> Tensor self sparse COO tensor (.e., torch_sparse_coo layout), returns view contained indices tensor. Otherwise, throws error. See also Tensor.values.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-4","dir":"Articles > Tensor","previous_headings":"indices","what":"Note:","title":"Tensor objects","text":"method can called coalesced sparse tensor. See Tensor.coalesce details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"int","dir":"Articles > Tensor","previous_headings":"","what":"int","title":"Tensor objects","text":"int(memory_format=torch_preserve_format) -> Tensor self$int() equivalent self$(torch_int32). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-23","dir":"Articles > Tensor","previous_headings":"int","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"int_repr","dir":"Articles > Tensor","previous_headings":"","what":"int_repr","title":"Tensor objects","text":"int_repr() -> Tensor Given quantized Tensor, self$int_repr() returns CPU Tensor uint8_t data type stores underlying uint8_t values given Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"inverse","dir":"Articles > Tensor","previous_headings":"","what":"inverse","title":"Tensor objects","text":"inverse() -> Tensor See ?torch_inverse","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"irfft","dir":"Articles > Tensor","previous_headings":"","what":"irfft","title":"Tensor objects","text":"irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor See ?torch_irfft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_complex","dir":"Articles > Tensor","previous_headings":"","what":"is_complex","title":"Tensor objects","text":"is_complex() -> bool Returns TRUE data type self complex data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_contiguous","dir":"Articles > Tensor","previous_headings":"","what":"is_contiguous","title":"Tensor objects","text":"is_contiguous(memory_format=torch_contiguous_format) -> bool Returns TRUE self tensor contiguous memory order specified memory format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-24","dir":"Articles > Tensor","previous_headings":"is_contiguous","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): Specifies memory allocation order. Default: torch_contiguous_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_cuda","dir":"Articles > Tensor","previous_headings":"","what":"is_cuda","title":"Tensor objects","text":"TRUE Tensor stored GPU, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_floating_point","dir":"Articles > Tensor","previous_headings":"","what":"is_floating_point","title":"Tensor objects","text":"is_floating_point() -> bool Returns TRUE data type self floating point data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_leaf","dir":"Articles > Tensor","previous_headings":"","what":"is_leaf","title":"Tensor objects","text":"Tensors requires_grad FALSE leaf Tensors convention. Tensors requires_grad TRUE, leaf Tensors created user. means result operation grad_fn NULL. leaf Tensors grad populated call [backward()]. get grad populated non-leaf Tensors, can use [retain_grad()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-14","dir":"Articles > Tensor","previous_headings":"is_leaf","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_rand(10, requires_grad=TRUE) a$is_leaf  # b <- torch_rand(10, requires_grad=TRUE)$cuda() # b$is_leaf() # FALSE # b was created by the operation that cast a cpu Tensor into a cuda Tensor  c <- torch_rand(10, requires_grad=TRUE) + 2 c$is_leaf # c was created by the addition operation  # d <- torch_rand(10)$cuda() # d$is_leaf() # TRUE # d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)  # e <- torch_rand(10)$cuda()$requires_grad_() # e$is_leaf() # TRUE # e requires gradients and has no operations creating it  # f <- torch_rand(10, requires_grad=TRUE, device=\"cuda\") # f$is_leaf # TRUE # f requires grad, has no operation creating it"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_meta","dir":"Articles > Tensor","previous_headings":"","what":"is_meta","title":"Tensor objects","text":"TRUE Tensor meta tensor, FALSE otherwise. Meta tensors like normal tensors, carry data.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_pinned","dir":"Articles > Tensor","previous_headings":"","what":"is_pinned","title":"Tensor objects","text":"Returns true tensor resides pinned memory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_quantized","dir":"Articles > Tensor","previous_headings":"","what":"is_quantized","title":"Tensor objects","text":"TRUE Tensor quantized, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_set_to","dir":"Articles > Tensor","previous_headings":"","what":"is_set_to","title":"Tensor objects","text":"is_set_to(tensor) -> bool Returns TRUE object refers THTensor object Torch C API given tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_shared","dir":"Articles > Tensor","previous_headings":"","what":"is_shared","title":"Tensor objects","text":"Checks tensor shared memory. always TRUE CUDA tensors.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_signed","dir":"Articles > Tensor","previous_headings":"","what":"is_signed","title":"Tensor objects","text":"is_signed() -> bool Returns TRUE data type self signed data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isclose","dir":"Articles > Tensor","previous_headings":"","what":"isclose","title":"Tensor objects","text":"isclose(, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor See ?torch_isclose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isfinite","dir":"Articles > Tensor","previous_headings":"","what":"isfinite","title":"Tensor objects","text":"isfinite() -> Tensor See ?torch_isfinite","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isinf","dir":"Articles > Tensor","previous_headings":"","what":"isinf","title":"Tensor objects","text":"isinf() -> Tensor See ?torch_isinf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isnan","dir":"Articles > Tensor","previous_headings":"","what":"isnan","title":"Tensor objects","text":"isnan() -> Tensor See ?torch_isnan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"istft","dir":"Articles > Tensor","previous_headings":"","what":"istft","title":"Tensor objects","text":"See ?torch_istft ## item item() -> number Returns value tensor standard Python number. works tensors one element. cases, see $tolist. operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-15","dir":"Articles > Tensor","previous_headings":"istft","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(1.0) x$item()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"kthvalue","dir":"Articles > Tensor","previous_headings":"","what":"kthvalue","title":"Tensor objects","text":"kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_kthvalue","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"le","dir":"Articles > Tensor","previous_headings":"","what":"le","title":"Tensor objects","text":"le() -> Tensor See ?torch_le","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"le_","dir":"Articles > Tensor","previous_headings":"","what":"le_","title":"Tensor objects","text":"le_() -> Tensor -place version $le","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lerp","dir":"Articles > Tensor","previous_headings":"","what":"lerp","title":"Tensor objects","text":"lerp(end, weight) -> Tensor See ?torch_lerp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lerp_","dir":"Articles > Tensor","previous_headings":"","what":"lerp_","title":"Tensor objects","text":"lerp_(end, weight) -> Tensor -place version $lerp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lgamma","dir":"Articles > Tensor","previous_headings":"","what":"lgamma","title":"Tensor objects","text":"lgamma() -> Tensor See ?torch_lgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lgamma_","dir":"Articles > Tensor","previous_headings":"","what":"lgamma_","title":"Tensor objects","text":"lgamma_() -> Tensor -place version $lgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log","dir":"Articles > Tensor","previous_headings":"","what":"log","title":"Tensor objects","text":"log() -> Tensor See ?torch_log","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log10","dir":"Articles > Tensor","previous_headings":"","what":"log10","title":"Tensor objects","text":"log10() -> Tensor See [torch_log10()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log10_","dir":"Articles > Tensor","previous_headings":"","what":"log10_","title":"Tensor objects","text":"log10_() -> Tensor -place version $log10","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log1p","dir":"Articles > Tensor","previous_headings":"","what":"log1p","title":"Tensor objects","text":"log1p() -> Tensor See [torch_log1p()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log1p_","dir":"Articles > Tensor","previous_headings":"","what":"log1p_","title":"Tensor objects","text":"log1p_() -> Tensor -place version $log1p","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log2","dir":"Articles > Tensor","previous_headings":"","what":"log2","title":"Tensor objects","text":"log2() -> Tensor See [torch_log2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log2_","dir":"Articles > Tensor","previous_headings":"","what":"log2_","title":"Tensor objects","text":"log2_() -> Tensor -place version $log2","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log_","dir":"Articles > Tensor","previous_headings":"","what":"log_","title":"Tensor objects","text":"log_() -> Tensor -place version $log","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log_normal_","dir":"Articles > Tensor","previous_headings":"","what":"log_normal_","title":"Tensor objects","text":"log_normal_(mean=1, std=2, *, generator=NULL) Fills self tensor numbers samples log-normal distribution parameterized given mean \\mu standard deviation \\sigma. Note mean std mean standard deviation underlying normal distribution, returned distribution: \\[ f(x) = \\dfrac{1}{x \\sigma \\sqrt{2\\pi}}\\ e^{-\\frac{(\\ln x - \\mu)^2}{2\\sigma^2}} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logaddexp","dir":"Articles > Tensor","previous_headings":"","what":"logaddexp","title":"Tensor objects","text":"logaddexp() -> Tensor See ?torch_logaddexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logaddexp2","dir":"Articles > Tensor","previous_headings":"","what":"logaddexp2","title":"Tensor objects","text":"logaddexp2() -> Tensor See [torch_logaddexp2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logcumsumexp","dir":"Articles > Tensor","previous_headings":"","what":"logcumsumexp","title":"Tensor objects","text":"logcumsumexp(dim) -> Tensor See ?torch_logcumsumexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logdet","dir":"Articles > Tensor","previous_headings":"","what":"logdet","title":"Tensor objects","text":"logdet() -> Tensor See ?torch_logdet","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_and","dir":"Articles > Tensor","previous_headings":"","what":"logical_and","title":"Tensor objects","text":"logical_and() -> Tensor See [torch_logical_and()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_and_","dir":"Articles > Tensor","previous_headings":"","what":"logical_and_","title":"Tensor objects","text":"logical_and_() -> Tensor -place version $logical_and","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_not","dir":"Articles > Tensor","previous_headings":"","what":"logical_not","title":"Tensor objects","text":"logical_not() -> Tensor See [torch_logical_not()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_not_","dir":"Articles > Tensor","previous_headings":"","what":"logical_not_","title":"Tensor objects","text":"logical_not_() -> Tensor -place version $logical_not","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_or","dir":"Articles > Tensor","previous_headings":"","what":"logical_or","title":"Tensor objects","text":"logical_or() -> Tensor See [torch_logical_or()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_or_","dir":"Articles > Tensor","previous_headings":"","what":"logical_or_","title":"Tensor objects","text":"logical_or_() -> Tensor -place version $logical_or","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_xor","dir":"Articles > Tensor","previous_headings":"","what":"logical_xor","title":"Tensor objects","text":"logical_xor() -> Tensor See [torch_logical_xor()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_xor_","dir":"Articles > Tensor","previous_headings":"","what":"logical_xor_","title":"Tensor objects","text":"logical_xor_() -> Tensor -place version $logical_xor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logsumexp","dir":"Articles > Tensor","previous_headings":"","what":"logsumexp","title":"Tensor objects","text":"logsumexp(dim, keepdim=FALSE) -> Tensor See ?torch_logsumexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"long","dir":"Articles > Tensor","previous_headings":"","what":"long","title":"Tensor objects","text":"long(memory_format=torch_preserve_format) -> Tensor self$long() equivalent self$(torch_int64). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-25","dir":"Articles > Tensor","previous_headings":"long","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lstsq","dir":"Articles > Tensor","previous_headings":"","what":"lstsq","title":"Tensor objects","text":"lstsq() -> (Tensor, Tensor) See ?torch_lstsq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lt","dir":"Articles > Tensor","previous_headings":"","what":"lt","title":"Tensor objects","text":"lt() -> Tensor See ?torch_lt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lt_","dir":"Articles > Tensor","previous_headings":"","what":"lt_","title":"Tensor objects","text":"lt_() -> Tensor -place version $lt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lu","dir":"Articles > Tensor","previous_headings":"","what":"lu","title":"Tensor objects","text":"See ?torch_lu ## lu_solve lu_solve(LU_data, LU_pivots) -> Tensor See [torch_lu_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"map_","dir":"Articles > Tensor","previous_headings":"","what":"map_","title":"Tensor objects","text":"map_(tensor, callable) Applies callable element self tensor given tensor stores results self tensor. self tensor given tensor must broadcastable. callable signature: callable(, b) -> number","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_fill","dir":"Articles > Tensor","previous_headings":"","what":"masked_fill","title":"Tensor objects","text":"masked_fill(mask, value) -> Tensor --place version $masked_fill_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_fill_","dir":"Articles > Tensor","previous_headings":"","what":"masked_fill_","title":"Tensor objects","text":"masked_fill_(mask, value) Fills elements self tensor value mask TRUE. shape mask must broadcastable <broadcasting-semantics> shape underlying tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-26","dir":"Articles > Tensor","previous_headings":"masked_fill_","what":"Arguments:","title":"Tensor objects","text":"mask (BoolTensor): boolean mask value (float): value fill ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_scatter","dir":"Articles > Tensor","previous_headings":"","what":"masked_scatter","title":"Tensor objects","text":"masked_scatter(mask, tensor) -> Tensor --place version $masked_scatter_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_scatter_","dir":"Articles > Tensor","previous_headings":"","what":"masked_scatter_","title":"Tensor objects","text":"masked_scatter_(mask, source) Copies elements source self tensor positions mask TRUE. shape mask must :ref:broadcastable <broadcasting-semantics> shape underlying tensor. source least many elements number ones mask","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-27","dir":"Articles > Tensor","previous_headings":"masked_scatter_","what":"Arguments:","title":"Tensor objects","text":"mask (BoolTensor): boolean mask source (Tensor): tensor copy ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-5","dir":"Articles > Tensor","previous_headings":"masked_scatter_","what":"Note:","title":"Tensor objects","text":"mask operates self tensor, given source tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_select","dir":"Articles > Tensor","previous_headings":"","what":"masked_select","title":"Tensor objects","text":"masked_select(mask) -> Tensor See [torch_masked_select()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"matmul","dir":"Articles > Tensor","previous_headings":"","what":"matmul","title":"Tensor objects","text":"matmul(tensor2) -> Tensor See ?torch_matmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"matrix_power","dir":"Articles > Tensor","previous_headings":"","what":"matrix_power","title":"Tensor objects","text":"matrix_power(n) -> Tensor See [torch_matrix_power()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"max","dir":"Articles > Tensor","previous_headings":"","what":"max","title":"Tensor objects","text":"max(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_max","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mean","dir":"Articles > Tensor","previous_headings":"","what":"mean","title":"Tensor objects","text":"mean(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_mean","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"median","dir":"Articles > Tensor","previous_headings":"","what":"median","title":"Tensor objects","text":"median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_median","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"min","dir":"Articles > Tensor","previous_headings":"","what":"min","title":"Tensor objects","text":"min(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_min","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mm","dir":"Articles > Tensor","previous_headings":"","what":"mm","title":"Tensor objects","text":"mm(mat2) -> Tensor See ?torch_mm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mode","dir":"Articles > Tensor","previous_headings":"","what":"mode","title":"Tensor objects","text":"mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_mode","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mul","dir":"Articles > Tensor","previous_headings":"","what":"mul","title":"Tensor objects","text":"mul(value) -> Tensor See ?torch_mul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mul_","dir":"Articles > Tensor","previous_headings":"","what":"mul_","title":"Tensor objects","text":"mul_(value) -place version $mul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"multinomial","dir":"Articles > Tensor","previous_headings":"","what":"multinomial","title":"Tensor objects","text":"multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor See ?torch_multinomial","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mv","dir":"Articles > Tensor","previous_headings":"","what":"mv","title":"Tensor objects","text":"mv(vec) -> Tensor See ?torch_mv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mvlgamma","dir":"Articles > Tensor","previous_headings":"","what":"mvlgamma","title":"Tensor objects","text":"mvlgamma(p) -> Tensor See ?torch_mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mvlgamma_","dir":"Articles > Tensor","previous_headings":"","what":"mvlgamma_","title":"Tensor objects","text":"mvlgamma_(p) -> Tensor -place version $mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"names","dir":"Articles > Tensor","previous_headings":"","what":"names","title":"Tensor objects","text":"Stores names tensor’s dimensions. names[idx] corresponds name tensor dimension idx. Names either string dimension named NULL dimension unnamed. Dimension names may contain characters underscore. Furthermore, dimension name must valid Python variable name (.e., start underscore). Tensors may two named dimensions name.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-4","dir":"Articles > Tensor","previous_headings":"names","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"narrow","dir":"Articles > Tensor","previous_headings":"","what":"narrow","title":"Tensor objects","text":"narrow(dimension, start, length) -> Tensor See ?torch_narrow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-16","dir":"Articles > Tensor","previous_headings":"narrow","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:9, ncol = 3)) x$narrow(1, 1, 3) x$narrow(1, 1, 2)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"narrow_copy","dir":"Articles > Tensor","previous_headings":"","what":"narrow_copy","title":"Tensor objects","text":"narrow_copy(dimension, start, length) -> Tensor Tensor.narrow except returning copy rather shared storage. primarily sparse tensors, shared-storage narrow method. Calling narrow_copy` withdimemsion > self\\(sparse_dim()`` return copy relevant dense dimension narrowed, ``self\\)shape`` updated accordingly.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ndim","dir":"Articles > Tensor","previous_headings":"","what":"ndim","title":"Tensor objects","text":"Alias $dim()","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ndimension","dir":"Articles > Tensor","previous_headings":"","what":"ndimension","title":"Tensor objects","text":"ndimension() -> int Alias $dim()","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ne","dir":"Articles > Tensor","previous_headings":"","what":"ne","title":"Tensor objects","text":"ne() -> Tensor See ?torch_ne","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ne_","dir":"Articles > Tensor","previous_headings":"","what":"ne_","title":"Tensor objects","text":"ne_() -> Tensor -place version $ne","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"neg","dir":"Articles > Tensor","previous_headings":"","what":"neg","title":"Tensor objects","text":"neg() -> Tensor See ?torch_neg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"neg_","dir":"Articles > Tensor","previous_headings":"","what":"neg_","title":"Tensor objects","text":"neg_() -> Tensor -place version $neg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"nelement","dir":"Articles > Tensor","previous_headings":"","what":"nelement","title":"Tensor objects","text":"nelement() -> int Alias $numel","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_empty","dir":"Articles > Tensor","previous_headings":"","what":"new_empty","title":"Tensor objects","text":"new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled uninitialized data. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-28","dir":"Articles > Tensor","previous_headings":"new_empty","what":"Arguments:","title":"Tensor objects","text":"dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-17","dir":"Articles > Tensor","previous_headings":"new_empty","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(5) tensor$new_empty(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_full","dir":"Articles > Tensor","previous_headings":"","what":"new_full","title":"Tensor objects","text":"new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled fill_value. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-29","dir":"Articles > Tensor","previous_headings":"new_full","what":"Arguments:","title":"Tensor objects","text":"fill_value (scalar): number fill output tensor . dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-18","dir":"Articles > Tensor","previous_headings":"new_full","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(c(2), dtype=torch_float64()) tensor$new_full(c(3, 4), 3.141592)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_ones","dir":"Articles > Tensor","previous_headings":"","what":"new_ones","title":"Tensor objects","text":"new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled 1. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-30","dir":"Articles > Tensor","previous_headings":"new_ones","what":"Arguments:","title":"Tensor objects","text":"size (int…): list, tuple, torch_Size integers defining shape output tensor. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-19","dir":"Articles > Tensor","previous_headings":"new_ones","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_tensor(c(2), dtype=torch_int32()) tensor$new_ones(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_tensor","dir":"Articles > Tensor","previous_headings":"","what":"new_tensor","title":"Tensor objects","text":"new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns new Tensor data tensor data. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-5","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Warning:","title":"Tensor objects","text":"new_tensor always copies data(). Tensordata` want avoid copy, use [\\(requires_grad_()] [\\)detach()]. numpy array want avoid copy, use [torch_from_numpy()]. data tensor x, [new_tensor()()] reads ‘data’ whatever passed, constructs leaf variable. Therefore tensor$new_tensor(x) equivalent x$clone()$detach() tensor$new_tensor(x, requires_grad=TRUE) equivalent x$clone()$detach()$requires_grad_(TRUE). equivalents using clone() detach() recommended.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-31","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Arguments:","title":"Tensor objects","text":"data (array_like): returned Tensor copies data. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-20","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(c(2), dtype=torch_int8) data <- matrix(1:4, ncol = 2) tensor$new_tensor(data)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_zeros","dir":"Articles > Tensor","previous_headings":"","what":"new_zeros","title":"Tensor objects","text":"new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled 0. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-32","dir":"Articles > Tensor","previous_headings":"new_zeros","what":"Arguments:","title":"Tensor objects","text":"size (int…): list, tuple, torch_Size integers defining shape output tensor. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-21","dir":"Articles > Tensor","previous_headings":"new_zeros","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_tensor(c(1), dtype=torch_float64()) tensor$new_zeros(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"nonzero","dir":"Articles > Tensor","previous_headings":"","what":"nonzero","title":"Tensor objects","text":"nonzero() -> LongTensor See ?torch_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"norm","dir":"Articles > Tensor","previous_headings":"","what":"norm","title":"Tensor objects","text":"See ?torch_norm ## normal_ normal_(mean=0, std=1, *, generator=NULL) -> Tensor Fills self tensor elements samples normal distribution parameterized mean std.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numel","dir":"Articles > Tensor","previous_headings":"","what":"numel","title":"Tensor objects","text":"numel() -> int See ?torch_numel","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numpy","dir":"Articles > Tensor","previous_headings":"","what":"numpy","title":"Tensor objects","text":"numpy() -> numpy.ndarray Returns self tensor NumPy :class:ndarray. tensor returned ndarray share underlying storage. Changes self tensor reflected :class:ndarray vice versa.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"orgqr","dir":"Articles > Tensor","previous_headings":"","what":"orgqr","title":"Tensor objects","text":"orgqr(input2) -> Tensor See ?torch_orgqr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ormqr","dir":"Articles > Tensor","previous_headings":"","what":"ormqr","title":"Tensor objects","text":"ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor See ?torch_ormqr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"permute","dir":"Articles > Tensor","previous_headings":"","what":"permute","title":"Tensor objects","text":"permute(*dims) -> Tensor Returns view original tensor dimensions permuted.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-33","dir":"Articles > Tensor","previous_headings":"permute","what":"Arguments:","title":"Tensor objects","text":"dims (int…): desired ordering dimensions","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-22","dir":"Articles > Tensor","previous_headings":"permute","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(2, 3, 5) x$size() x$permute(c(3, 1, 2))$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pin_memory","dir":"Articles > Tensor","previous_headings":"","what":"pin_memory","title":"Tensor objects","text":"pin_memory() -> Tensor Copies tensor pinned memory, ’s already pinned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pinverse","dir":"Articles > Tensor","previous_headings":"","what":"pinverse","title":"Tensor objects","text":"pinverse() -> Tensor See ?torch_pinverse","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"polygamma","dir":"Articles > Tensor","previous_headings":"","what":"polygamma","title":"Tensor objects","text":"polygamma(n) -> Tensor See ?torch_polygamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"polygamma_","dir":"Articles > Tensor","previous_headings":"","what":"polygamma_","title":"Tensor objects","text":"polygamma_(n) -> Tensor -place version $polygamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pow","dir":"Articles > Tensor","previous_headings":"","what":"pow","title":"Tensor objects","text":"pow(exponent) -> Tensor See ?torch_pow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pow_","dir":"Articles > Tensor","previous_headings":"","what":"pow_","title":"Tensor objects","text":"pow_(exponent) -> Tensor -place version $pow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"prod","dir":"Articles > Tensor","previous_headings":"","what":"prod","title":"Tensor objects","text":"prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor See ?torch_prod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"put_","dir":"Articles > Tensor","previous_headings":"","what":"put_","title":"Tensor objects","text":"put_(indices, tensor, accumulate=FALSE) -> Tensor Copies elements tensor positions specified indices. purpose indexing, self tensor treated 1-D tensor. accumulate TRUE, elements tensor added self. accumulate FALSE, behavior undefined indices contain duplicate elements.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-34","dir":"Articles > Tensor","previous_headings":"put_","what":"Arguments:","title":"Tensor objects","text":"indices (LongTensor): indices self tensor (Tensor): tensor containing values copy accumulate (bool): whether accumulate self","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-23","dir":"Articles > Tensor","previous_headings":"put_","what":"Examples:","title":"Tensor objects","text":"","code":"src <- torch_tensor(matrix(3:8, ncol = 3)) src$put_(torch_tensor(1:2), torch_tensor(9:10))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_axis","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_axis","title":"Tensor objects","text":"q_per_channel_axis() -> int Given Tensor quantized linear (affine) per-channel quantization, returns index dimension per-channel quantization applied.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_scales","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_scales","title":"Tensor objects","text":"q_per_channel_scales() -> Tensor Given Tensor quantized linear (affine) per-channel quantization, returns Tensor scales underlying quantizer. number elements matches corresponding dimensions (q_per_channel_axis) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_zero_points","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_zero_points","title":"Tensor objects","text":"q_per_channel_zero_points() -> Tensor Given Tensor quantized linear (affine) per-channel quantization, returns tensor zero_points underlying quantizer. number elements matches corresponding dimensions (q_per_channel_axis) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_scale","dir":"Articles > Tensor","previous_headings":"","what":"q_scale","title":"Tensor objects","text":"q_scale() -> float Given Tensor quantized linear(affine) quantization, returns scale underlying quantizer().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_zero_point","dir":"Articles > Tensor","previous_headings":"","what":"q_zero_point","title":"Tensor objects","text":"q_zero_point() -> int Given Tensor quantized linear(affine) quantization, returns zero_point underlying quantizer().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"qr","dir":"Articles > Tensor","previous_headings":"","what":"qr","title":"Tensor objects","text":"qr(=TRUE) -> (Tensor, Tensor) See ?torch_qr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"qscheme","dir":"Articles > Tensor","previous_headings":"","what":"qscheme","title":"Tensor objects","text":"qscheme() -> torch_qscheme Returns quantization scheme given QTensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rad2deg","dir":"Articles > Tensor","previous_headings":"","what":"rad2deg","title":"Tensor objects","text":"rad2deg() -> Tensor See [torch_rad2deg()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rad2deg_","dir":"Articles > Tensor","previous_headings":"","what":"rad2deg_","title":"Tensor objects","text":"rad2deg_() -> Tensor -place version $rad2deg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"random_","dir":"Articles > Tensor","previous_headings":"","what":"random_","title":"Tensor objects","text":"random_(=0, =NULL, *, generator=NULL) -> Tensor Fills self tensor numbers sampled discrete uniform distribution [, - 1]. specified, values usually bounded self tensor’s data type. However, floating point types, unspecified, range [0, 2^mantissa] ensure every value representable. example, torch_tensor(1, dtype=torch_double).random_() uniform [0, 2^53].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"real","dir":"Articles > Tensor","previous_headings":"","what":"real","title":"Tensor objects","text":"Returns new tensor containing real values self tensor. returned tensor self share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-6","dir":"Articles > Tensor","previous_headings":"real","what":"Warning:","title":"Tensor objects","text":"[real()] supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-24","dir":"Articles > Tensor","previous_headings":"real","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(4, dtype=torch_cfloat()) x x$real"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reciprocal","dir":"Articles > Tensor","previous_headings":"","what":"reciprocal","title":"Tensor objects","text":"reciprocal() -> Tensor See ?torch_reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reciprocal_","dir":"Articles > Tensor","previous_headings":"","what":"reciprocal_","title":"Tensor objects","text":"reciprocal_() -> Tensor -place version $reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"record_stream","dir":"Articles > Tensor","previous_headings":"","what":"record_stream","title":"Tensor objects","text":"record_stream(stream) Ensures tensor memory reused another tensor current work queued stream complete.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-6","dir":"Articles > Tensor","previous_headings":"record_stream","what":"Note:","title":"Tensor objects","text":"caching allocator aware stream tensor allocated. Due awareness, already correctly manages life cycle tensors one stream. tensor used stream different stream origin, allocator might reuse memory unexpectedly. Calling method lets allocator know streams used tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"refine_names","dir":"Articles > Tensor","previous_headings":"","what":"refine_names","title":"Tensor objects","text":"Refines dimension names self according names. Refining special case renaming “lifts” unnamed dimensions. NULL dim can refined name; named dim can refined name. named tensors can coexist unnamed tensors, refining names gives nice way write named-tensor-aware code works named unnamed tensors. names may contain one Ellipsis (...). Ellipsis expanded greedily; expanded -place fill names length self$dim() using names corresponding indices self$names.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-35","dir":"Articles > Tensor","previous_headings":"refine_names","what":"Arguments:","title":"Tensor objects","text":"names (iterable str): desired names output tensor. May contain one Ellipsis.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-25","dir":"Articles > Tensor","previous_headings":"refine_names","what":"Examples:","title":"Tensor objects","text":"","code":"imgs <- torch_randn(32, 3, 128, 128) named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W')) named_imgs$names"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"register_hook","dir":"Articles > Tensor","previous_headings":"","what":"register_hook","title":"Tensor objects","text":"Registers backward hook. hook called every time gradient respect Tensor computed. hook following signature:: hook(grad) -> Tensor NULL hook modify argument, can optionally return new gradient used place grad. function returns handle method handle$remove() removes hook module.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"example","dir":"Articles > Tensor","previous_headings":"register_hook","what":"Example","title":"Tensor objects","text":"","code":"v <- torch_tensor(c(0., 0., 0.), requires_grad=TRUE) h <- v$register_hook(function(grad) grad * 2)  # double the gradient v$backward(torch_tensor(c(1., 2., 3.))) v$grad h$remove()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"remainder","dir":"Articles > Tensor","previous_headings":"","what":"remainder","title":"Tensor objects","text":"remainder(divisor) -> Tensor See ?torch_remainder","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"remainder_","dir":"Articles > Tensor","previous_headings":"","what":"remainder_","title":"Tensor objects","text":"remainder_(divisor) -> Tensor -place version $remainder","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rename","dir":"Articles > Tensor","previous_headings":"","what":"rename","title":"Tensor objects","text":"Renames dimension names self. two main usages: self$rename(**rename_map) returns view tensor dims renamed specified mapping rename_map. self$rename(*names) returns view tensor, renaming dimensions positionally using names. Use self$rename(NULL) drop names tensor. One specify positional args names keyword args rename_map.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-26","dir":"Articles > Tensor","previous_headings":"rename","what":"Examples:","title":"Tensor objects","text":"","code":"imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W')) renamed_imgs <- imgs$rename(c(\"Batch\", \"Channels\", \"Height\", \"Width\"))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rename_","dir":"Articles > Tensor","previous_headings":"","what":"rename_","title":"Tensor objects","text":"-place version $rename.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"renorm","dir":"Articles > Tensor","previous_headings":"","what":"renorm","title":"Tensor objects","text":"renorm(p, dim, maxnorm) -> Tensor See ?torch_renorm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"renorm_","dir":"Articles > Tensor","previous_headings":"","what":"renorm_","title":"Tensor objects","text":"renorm_(p, dim, maxnorm) -> Tensor -place version $renorm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"repeat","dir":"Articles > Tensor","previous_headings":"","what":"repeat","title":"Tensor objects","text":"repeat(*sizes) -> Tensor Repeats tensor along specified dimensions. Unlike $expand, function copies tensor’s data.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-36","dir":"Articles > Tensor","previous_headings":"repeat","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): number times repeat tensor along dimension","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-27","dir":"Articles > Tensor","previous_headings":"repeat","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(c(1, 2, 3)) x$`repeat`(c(4, 2)) x$`repeat`(c(4, 2, 1))$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"repeat_interleave","dir":"Articles > Tensor","previous_headings":"","what":"repeat_interleave","title":"Tensor objects","text":"repeat_interleave(repeats, dim=NULL) -> Tensor See [torch_repeat_interleave()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"requires_grad","dir":"Articles > Tensor","previous_headings":"","what":"requires_grad","title":"Tensor objects","text":"TRUE gradients need computed Tensor, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-7","dir":"Articles > Tensor","previous_headings":"requires_grad","what":"Note:","title":"Tensor objects","text":"fact gradients need computed Tensor mean grad attribute populated, see is_leaf details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"requires_grad_","dir":"Articles > Tensor","previous_headings":"","what":"requires_grad_","title":"Tensor objects","text":"requires_grad_(requires_grad=TRUE) -> Tensor Change autograd record operations tensor: sets tensor’s requires_grad attribute -place. Returns tensor. [requires_grad_()]’s main use case tell autograd begin recording operations Tensor tensor. tensor requires_grad=FALSE (obtained DataLoader, required preprocessing initialization), tensor.requires_grad_() makes autograd begin record operations tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-37","dir":"Articles > Tensor","previous_headings":"requires_grad_","what":"Arguments:","title":"Tensor objects","text":"requires_grad (bool): autograd record operations tensor. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-28","dir":"Articles > Tensor","previous_headings":"requires_grad_","what":"Examples:","title":"Tensor objects","text":"","code":"# Let's say we want to preprocess some saved weights and use # the result as new weights. saved_weights <- c(0.1, 0.2, 0.3, 0.25) loaded_weights <- torch_tensor(saved_weights) weights <- preprocess(loaded_weights)  # some function weights  # Now, start to record operations done to weights weights$requires_grad_() out <- weights$pow(2)$sum() out$backward() weights$grad"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reshape","dir":"Articles > Tensor","previous_headings":"","what":"reshape","title":"Tensor objects","text":"reshape(*shape) -> Tensor Returns tensor data number elements self specified shape. method returns view shape compatible current shape. See $view possible return view. See ?torch_reshape","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-38","dir":"Articles > Tensor","previous_headings":"reshape","what":"Arguments:","title":"Tensor objects","text":"shape (tuple ints int…): desired shape","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reshape_as","dir":"Articles > Tensor","previous_headings":"","what":"reshape_as","title":"Tensor objects","text":"reshape_as() -> Tensor Returns tensor shape . self$reshape_as() equivalent self$reshape(.sizes()). method returns view .sizes() compatible current shape. See $view possible return view. Please see reshape information reshape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-39","dir":"Articles > Tensor","previous_headings":"reshape_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor shape .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"resize_","dir":"Articles > Tensor","previous_headings":"","what":"resize_","title":"Tensor objects","text":"resize_(*sizes, memory_format=torch_contiguous_format) -> Tensor Resizes self tensor specified size. number elements larger current storage size, underlying storage resized fit new number elements. number elements smaller, underlying storage changed. Existing elements preserved new memory uninitialized.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-7","dir":"Articles > Tensor","previous_headings":"resize_","what":"Warning:","title":"Tensor objects","text":"low-level method. storage reinterpreted C-contiguous, ignoring current strides (unless target size equals current size, case tensor left unchanged). purposes, instead want use $view(), checks contiguity, $reshape(), copies data needed. change size -place custom strides, see $set_().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-40","dir":"Articles > Tensor","previous_headings":"resize_","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): desired size memory_format (torch_memory_format, optional): desired memory format Tensor. Default: torch_contiguous_format. Note memory format self going unaffected self$size() matches sizes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-29","dir":"Articles > Tensor","previous_headings":"resize_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:6, ncol = 2)) x$resize_(c(2, 2))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"resize_as_","dir":"Articles > Tensor","previous_headings":"","what":"resize_as_","title":"Tensor objects","text":"resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor Resizes self tensor size specified tensor. equivalent self$resize_(tensor.size()).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-41","dir":"Articles > Tensor","previous_headings":"resize_as_","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format Tensor. Default: torch_contiguous_format. Note memory format self going unaffected self$size() matches tensor.size().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"retain_grad","dir":"Articles > Tensor","previous_headings":"","what":"retain_grad","title":"Tensor objects","text":"Enables $grad attribute non-leaf Tensors.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rfft","dir":"Articles > Tensor","previous_headings":"","what":"rfft","title":"Tensor objects","text":"rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor See ?torch_rfft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"roll","dir":"Articles > Tensor","previous_headings":"","what":"roll","title":"Tensor objects","text":"roll(shifts, dims) -> Tensor See ?torch_roll","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rot90","dir":"Articles > Tensor","previous_headings":"","what":"rot90","title":"Tensor objects","text":"rot90(k, dims) -> Tensor See [torch_rot90()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"round","dir":"Articles > Tensor","previous_headings":"","what":"round","title":"Tensor objects","text":"round() -> Tensor See ?torch_round","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"round_","dir":"Articles > Tensor","previous_headings":"","what":"round_","title":"Tensor objects","text":"round_() -> Tensor -place version $round","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rsqrt","dir":"Articles > Tensor","previous_headings":"","what":"rsqrt","title":"Tensor objects","text":"rsqrt() -> Tensor See ?torch_rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rsqrt_","dir":"Articles > Tensor","previous_headings":"","what":"rsqrt_","title":"Tensor objects","text":"rsqrt_() -> Tensor -place version $rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter","dir":"Articles > Tensor","previous_headings":"","what":"scatter","title":"Tensor objects","text":"scatter(dim, index, src) -> Tensor --place version $scatter_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_","dir":"Articles > Tensor","previous_headings":"","what":"scatter_","title":"Tensor objects","text":"scatter_(dim, index, src) -> Tensor Writes values tensor src self indices specified index tensor. value src, output index specified index src dimension != dim corresponding value index dimension = dim. 3-D tensor, self updated : reverse operation manner described $gather. self, index src (Tensor) number dimensions. also required index.size(d) <= src.size(d) dimensions d, index.size(d) <= self$size(d) dimensions d != dim. Moreover, $gather, values index must 0 self$size(dim) - 1 inclusive, values row along specified dimension dim must unique.","code":"self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0 self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1 self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-42","dir":"Articles > Tensor","previous_headings":"scatter_","what":"Arguments:","title":"Tensor objects","text":"dim (int): axis along index index (LongTensor): indices elements scatter, can either empty size src. empty, operation returns identity src (Tensor): source element(s) scatter, incase value specified value (float): source element(s) scatter, incase src specified","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-30","dir":"Articles > Tensor","previous_headings":"scatter_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_rand(2, 5) x torch_zeros(3, 5)$scatter_(         1,          torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x) )  z <- torch_zeros(2, 4)$scatter_(         2,          torch_tensor(matrix(3:4, ncol = 1)), 1.23 )"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_add","dir":"Articles > Tensor","previous_headings":"","what":"scatter_add","title":"Tensor objects","text":"scatter_add(dim, index, src) -> Tensor --place version $scatter_add_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_add_","dir":"Articles > Tensor","previous_headings":"","what":"scatter_add_","title":"Tensor objects","text":"scatter_add_(dim, index, src) -> Tensor Adds values tensor self indices specified index tensor similar fashion ~$scatter_. value src, added index self specified index src dimension != dim corresponding value index dimension = dim. 3-D tensor, self updated :: self, index src number dimensions. also required index.size(d) <= src.size(d) dimensions d, index.size(d) <= self$size(d) dimensions d != dim.","code":"self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0 self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1 self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-8","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Note:","title":"Tensor objects","text":"circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch_backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-43","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Arguments:","title":"Tensor objects","text":"dim (int): axis along index index (LongTensor): indices elements scatter add, can either empty size src. empty, operation returns identity. src (Tensor): source elements scatter add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-31","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_rand(2, 5) x torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"select","dir":"Articles > Tensor","previous_headings":"","what":"select","title":"Tensor objects","text":"select(dim, index) -> Tensor Slices self tensor along selected dimension given index. function returns view original tensor given dimension removed.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-44","dir":"Articles > Tensor","previous_headings":"select","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension slice index (int): index select ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-9","dir":"Articles > Tensor","previous_headings":"select","what":"Note:","title":"Tensor objects","text":"select equivalent slicing. example, tensor$select(0, index) equivalent tensor[index] tensor$select(2, index) equivalent tensor[:,:,index].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"set_","dir":"Articles > Tensor","previous_headings":"","what":"set_","title":"Tensor objects","text":"set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> Tensor Sets underlying storage, size, strides. source tensor, self tensor share storage size strides source. Changes elements one tensor reflected .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-45","dir":"Articles > Tensor","previous_headings":"set_","what":"Arguments:","title":"Tensor objects","text":"source (Tensor Storage): tensor storage use storage_offset (int, optional): offset storage size (torch_Size, optional): desired size. Defaults size source. stride (tuple, optional): desired stride. Defaults C-contiguous strides.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"share_memory_","dir":"Articles > Tensor","previous_headings":"","what":"share_memory_","title":"Tensor objects","text":"Moves underlying storage shared memory. -op underlying storage already shared memory CUDA tensors. Tensors shared memory resized.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"short","dir":"Articles > Tensor","previous_headings":"","what":"short","title":"Tensor objects","text":"short(memory_format=torch_preserve_format) -> Tensor self$short() equivalent self$(torch_int16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-46","dir":"Articles > Tensor","previous_headings":"short","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sigmoid","dir":"Articles > Tensor","previous_headings":"","what":"sigmoid","title":"Tensor objects","text":"sigmoid() -> Tensor See ?torch_sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sigmoid_","dir":"Articles > Tensor","previous_headings":"","what":"sigmoid_","title":"Tensor objects","text":"sigmoid_() -> Tensor -place version $sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sign","dir":"Articles > Tensor","previous_headings":"","what":"sign","title":"Tensor objects","text":"sign() -> Tensor See ?torch_sign","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sign_","dir":"Articles > Tensor","previous_headings":"","what":"sign_","title":"Tensor objects","text":"sign_() -> Tensor -place version $sign","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sin","dir":"Articles > Tensor","previous_headings":"","what":"sin","title":"Tensor objects","text":"sin() -> Tensor See ?torch_sin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sin_","dir":"Articles > Tensor","previous_headings":"","what":"sin_","title":"Tensor objects","text":"sin_() -> Tensor -place version $sin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sinh","dir":"Articles > Tensor","previous_headings":"","what":"sinh","title":"Tensor objects","text":"sinh() -> Tensor See ?torch_sinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sinh_","dir":"Articles > Tensor","previous_headings":"","what":"sinh_","title":"Tensor objects","text":"sinh_() -> Tensor -place version $sinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"size","dir":"Articles > Tensor","previous_headings":"","what":"size","title":"Tensor objects","text":"size() -> torch_Size Returns size self tensor. returned value subclass tuple.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-32","dir":"Articles > Tensor","previous_headings":"size","what":"Examples:","title":"Tensor objects","text":"","code":"torch_empty(3, 4, 5)$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"slogdet","dir":"Articles > Tensor","previous_headings":"","what":"slogdet","title":"Tensor objects","text":"slogdet() -> (Tensor, Tensor) See ?torch_slogdet","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sort","dir":"Articles > Tensor","previous_headings":"","what":"sort","title":"Tensor objects","text":"sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor) See ?torch_sort","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sparse_dim","dir":"Articles > Tensor","previous_headings":"","what":"sparse_dim","title":"Tensor objects","text":"sparse_dim() -> int self sparse COO tensor (.e., torch_sparse_coo layout), returns number sparse dimensions. Otherwise, throws error. See also Tensor.dense_dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sparse_mask","dir":"Articles > Tensor","previous_headings":"","what":"sparse_mask","title":"Tensor objects","text":"sparse_mask(input, mask) -> Tensor Returns new SparseTensor values Tensor input filtered indices mask values ignored. input mask must shape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-47","dir":"Articles > Tensor","previous_headings":"sparse_mask","what":"Arguments:","title":"Tensor objects","text":"input (Tensor): input Tensor mask (SparseTensor): SparseTensor filter input based indices","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"split","dir":"Articles > Tensor","previous_headings":"","what":"split","title":"Tensor objects","text":"See ?torch_split","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sqrt","dir":"Articles > Tensor","previous_headings":"","what":"sqrt","title":"Tensor objects","text":"sqrt() -> Tensor See ?torch_sqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sqrt_","dir":"Articles > Tensor","previous_headings":"","what":"sqrt_","title":"Tensor objects","text":"sqrt_() -> Tensor -place version $sqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"square","dir":"Articles > Tensor","previous_headings":"","what":"square","title":"Tensor objects","text":"square() -> Tensor See ?torch_square","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"square_","dir":"Articles > Tensor","previous_headings":"","what":"square_","title":"Tensor objects","text":"square_() -> Tensor -place version $square","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"squeeze","dir":"Articles > Tensor","previous_headings":"","what":"squeeze","title":"Tensor objects","text":"squeeze(dim=NULL) -> Tensor See ?torch_squeeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"squeeze_","dir":"Articles > Tensor","previous_headings":"","what":"squeeze_","title":"Tensor objects","text":"squeeze_(dim=NULL) -> Tensor -place version $squeeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"std","dir":"Articles > Tensor","previous_headings":"","what":"std","title":"Tensor objects","text":"std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor See ?torch_std","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"stft","dir":"Articles > Tensor","previous_headings":"","what":"stft","title":"Tensor objects","text":"See ?torch_stft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage","dir":"Articles > Tensor","previous_headings":"","what":"storage","title":"Tensor objects","text":"storage() -> torch_Storage Returns underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage_offset","dir":"Articles > Tensor","previous_headings":"","what":"storage_offset","title":"Tensor objects","text":"storage_offset() -> int Returns self tensor’s offset underlying storage terms number storage elements (bytes).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-33","dir":"Articles > Tensor","previous_headings":"storage_offset","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(c(1, 2, 3, 4, 5)) x$storage_offset() x[3:N]$storage_offset()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage_type","dir":"Articles > Tensor","previous_headings":"","what":"storage_type","title":"Tensor objects","text":"storage_type() -> type Returns type underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"stride","dir":"Articles > Tensor","previous_headings":"","what":"stride","title":"Tensor objects","text":"stride(dim) -> tuple int Returns stride self tensor. Stride jump necessary go one element next one specified dimension dim. tuple strides returned argument passed . Otherwise, integer value returned stride particular dimension dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-48","dir":"Articles > Tensor","previous_headings":"stride","what":"Arguments:","title":"Tensor objects","text":"dim (int, optional): desired dimension stride required","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-34","dir":"Articles > Tensor","previous_headings":"stride","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:10, nrow = 2)) x$stride() x$stride(1) x$stride(-1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sub","dir":"Articles > Tensor","previous_headings":"","what":"sub","title":"Tensor objects","text":"sub(, *, alpha=1) -> Tensor Subtracts scalar tensor self tensor. alpha specified, element scaled alpha used. tensor, shape must broadcastable <broadcasting-semantics> shape underlying tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sub_","dir":"Articles > Tensor","previous_headings":"","what":"sub_","title":"Tensor objects","text":"sub_(, *, alpha=1) -> Tensor -place version $sub","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sum","dir":"Articles > Tensor","previous_headings":"","what":"sum","title":"Tensor objects","text":"sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor See ?torch_sum","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sum_to_size","dir":"Articles > Tensor","previous_headings":"","what":"sum_to_size","title":"Tensor objects","text":"sum_to_size(*size) -> Tensor Sum tensor size. size must broadcastable tensor size.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-49","dir":"Articles > Tensor","previous_headings":"sum_to_size","what":"Arguments:","title":"Tensor objects","text":"size (int…): sequence integers defining shape output tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"svd","dir":"Articles > Tensor","previous_headings":"","what":"svd","title":"Tensor objects","text":"svd(=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) See ?torch_svd","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"t","dir":"Articles > Tensor","previous_headings":"","what":"t","title":"Tensor objects","text":"t() -> Tensor See ?torch_t","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"t_","dir":"Articles > Tensor","previous_headings":"","what":"t_","title":"Tensor objects","text":"t_() -> Tensor -place version $t","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"take","dir":"Articles > Tensor","previous_headings":"","what":"take","title":"Tensor objects","text":"take(indices) -> Tensor See ?torch_take","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tan","dir":"Articles > Tensor","previous_headings":"","what":"tan","title":"Tensor objects","text":"tan() -> Tensor See ?torch_tan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tan_","dir":"Articles > Tensor","previous_headings":"","what":"tan_","title":"Tensor objects","text":"tan_() -> Tensor -place version $tan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tanh","dir":"Articles > Tensor","previous_headings":"","what":"tanh","title":"Tensor objects","text":"tanh() -> Tensor See ?torch_tanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tanh_","dir":"Articles > Tensor","previous_headings":"","what":"tanh_","title":"Tensor objects","text":"tanh_() -> Tensor -place version $tanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to","dir":"Articles > Tensor","previous_headings":"","what":"to","title":"Tensor objects","text":"(*args, **kwargs) -> Tensor Performs Tensor dtype /device conversion. torch_dtype :class:torch_device inferred arguments self$(*args, **kwargs).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-10","dir":"Articles > Tensor","previous_headings":"to","what":"Note:","title":"Tensor objects","text":"self Tensor already correct torch_dtype :class:torch_device, self returned. Otherwise, returned tensor copy self desired torch_dtype :class:torch_device. ways call : (dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor Returns Tensor specified dtype","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-50","dir":"Articles > Tensor","previous_headings":"to","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format. (device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor Returns Tensor specified device (optional) dtype. dtype NULL inferred self$dtype. non_blocking, tries convert asynchronously respect host possible, e.g., converting CPU Tensor pinned memory CUDA Tensor. copy set, new Tensor created even Tensor already matches desired conversion.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-51","dir":"Articles > Tensor","previous_headings":"to","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format. function:: (, non_blocking=FALSE, copy=FALSE) -> Tensor Returns Tensor torch_dtype :class:torch_device Tensor . non_blocking, tries convert asynchronously respect host possible, e.g., converting CPU Tensor pinned memory CUDA Tensor. copy set, new Tensor created even Tensor already matches desired conversion.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-35","dir":"Articles > Tensor","previous_headings":"to","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_randn(2, 2)  # Initially dtype=float32, device=cpu tensor$to(dtype = torch_float64())  other <- torch_randn(1, dtype=torch_float64()) tensor$to(other = other, non_blocking=TRUE)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to_mkldnn","dir":"Articles > Tensor","previous_headings":"","what":"to_mkldnn","title":"Tensor objects","text":"to_mkldnn() -> Tensor Returns copy tensor torch_mkldnn layout.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to_sparse","dir":"Articles > Tensor","previous_headings":"","what":"to_sparse","title":"Tensor objects","text":"to_sparse(sparseDims) -> Tensor Returns sparse copy tensor. PyTorch supports sparse tensors coordinate format <sparse-docs>.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-52","dir":"Articles > Tensor","previous_headings":"to_sparse","what":"Arguments:","title":"Tensor objects","text":"sparseDims (int, optional): number sparse dimensions include new sparse tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tolist","dir":"Articles > Tensor","previous_headings":"","what":"tolist","title":"Tensor objects","text":"tolist() -> list number Returns tensor (nested) list. scalars, standard Python number returned, just like $item. Tensors automatically moved CPU first necessary. operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"topk","dir":"Articles > Tensor","previous_headings":"","what":"topk","title":"Tensor objects","text":"topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) See ?torch_topk","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trace","dir":"Articles > Tensor","previous_headings":"","what":"trace","title":"Tensor objects","text":"trace() -> Tensor See ?torch_trace","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"transpose","dir":"Articles > Tensor","previous_headings":"","what":"transpose","title":"Tensor objects","text":"transpose(dim0, dim1) -> Tensor See ?torch_transpose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"transpose_","dir":"Articles > Tensor","previous_headings":"","what":"transpose_","title":"Tensor objects","text":"transpose_(dim0, dim1) -> Tensor -place version $transpose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triangular_solve","dir":"Articles > Tensor","previous_headings":"","what":"triangular_solve","title":"Tensor objects","text":"triangular_solve(, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor) See [torch_triangular_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tril","dir":"Articles > Tensor","previous_headings":"","what":"tril","title":"Tensor objects","text":"tril(k=0) -> Tensor See ?torch_tril","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tril_","dir":"Articles > Tensor","previous_headings":"","what":"tril_","title":"Tensor objects","text":"tril_(k=0) -> Tensor -place version $tril","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triu","dir":"Articles > Tensor","previous_headings":"","what":"triu","title":"Tensor objects","text":"triu(k=0) -> Tensor See ?torch_triu","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triu_","dir":"Articles > Tensor","previous_headings":"","what":"triu_","title":"Tensor objects","text":"triu_(k=0) -> Tensor -place version $triu","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"true_divide","dir":"Articles > Tensor","previous_headings":"","what":"true_divide","title":"Tensor objects","text":"true_divide(value) -> Tensor See [torch_true_divide()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"true_divide_","dir":"Articles > Tensor","previous_headings":"","what":"true_divide_","title":"Tensor objects","text":"true_divide_(value) -> Tensor -place version $true_divide_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trunc","dir":"Articles > Tensor","previous_headings":"","what":"trunc","title":"Tensor objects","text":"trunc() -> Tensor See ?torch_trunc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trunc_","dir":"Articles > Tensor","previous_headings":"","what":"trunc_","title":"Tensor objects","text":"trunc_() -> Tensor -place version $trunc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"type","dir":"Articles > Tensor","previous_headings":"","what":"type","title":"Tensor objects","text":"type(dtype=NULL, non_blocking=FALSE, **kwargs) -> str Tensor Returns type dtype provided, else casts object specified type. already correct type, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-53","dir":"Articles > Tensor","previous_headings":"type","what":"Arguments:","title":"Tensor objects","text":"dtype (type string): desired type non_blocking (bool): TRUE, source pinned memory destination GPU vice versa, copy performed asynchronously respect host. Otherwise, argument effect. **kwargs: compatibility, may contain key async place non_blocking argument. async arg deprecated.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"type_as","dir":"Articles > Tensor","previous_headings":"","what":"type_as","title":"Tensor objects","text":"type_as(tensor) -> Tensor Returns tensor cast type given tensor. -op tensor already correct type. equivalent self$type(tensor.type())","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-54","dir":"Articles > Tensor","previous_headings":"type_as","what":"Arguments:","title":"Tensor objects","text":"tensor (Tensor): tensor desired type","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unbind","dir":"Articles > Tensor","previous_headings":"","what":"unbind","title":"Tensor objects","text":"unbind(dim=0) -> seq See ?torch_unbind","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unflatten","dir":"Articles > Tensor","previous_headings":"","what":"unflatten","title":"Tensor objects","text":"Unflattens named dimension dim, viewing shape specified namedshape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-55","dir":"Articles > Tensor","previous_headings":"unflatten","what":"Arguments:","title":"Tensor objects","text":"namedshape: (iterable (name, size) tuples).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unfold","dir":"Articles > Tensor","previous_headings":"","what":"unfold","title":"Tensor objects","text":"unfold(dimension, size, step) -> Tensor Returns view original tensor contains slices size size self tensor dimension dimension. Step two slices given step. sizedim size dimension dimension self, size dimension dimension returned tensor (sizedim - size) / step + 1. additional dimension size size appended returned tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-56","dir":"Articles > Tensor","previous_headings":"unfold","what":"Arguments:","title":"Tensor objects","text":"dimension (int): dimension unfolding happens size (int): size slice unfolded step (int): step slice","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"uniform_","dir":"Articles > Tensor","previous_headings":"","what":"uniform_","title":"Tensor objects","text":"uniform_(=0, =1) -> Tensor Fills self tensor numbers sampled continuous uniform distribution: \\[ P(x) = \\dfrac{1}{\\text{} - \\text{}} \\]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unique","dir":"Articles > Tensor","previous_headings":"","what":"unique","title":"Tensor objects","text":"Returns unique elements input tensor. See ?torch_unique","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unique_consecutive","dir":"Articles > Tensor","previous_headings":"","what":"unique_consecutive","title":"Tensor objects","text":"Eliminates first element every consecutive group equivalent elements. See [torch_unique_consecutive()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unsqueeze","dir":"Articles > Tensor","previous_headings":"","what":"unsqueeze","title":"Tensor objects","text":"unsqueeze(dim) -> Tensor See ?torch_unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unsqueeze_","dir":"Articles > Tensor","previous_headings":"","what":"unsqueeze_","title":"Tensor objects","text":"unsqueeze_(dim) -> Tensor -place version $unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"values","dir":"Articles > Tensor","previous_headings":"","what":"values","title":"Tensor objects","text":"values() -> Tensor self sparse COO tensor (.e., torch_sparse_coo layout), returns view contained values tensor. Otherwise, throws error.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-11","dir":"Articles > Tensor","previous_headings":"values","what":"Note:","title":"Tensor objects","text":"method can called coalesced sparse tensor. See Tensor$coalesce details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"var","dir":"Articles > Tensor","previous_headings":"","what":"var","title":"Tensor objects","text":"var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor See ?torch_var","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"view","dir":"Articles > Tensor","previous_headings":"","what":"view","title":"Tensor objects","text":"view(*shape) -> Tensor Returns new tensor data self tensor different shape. returned tensor shares data must number elements, may different size. tensor viewed, new view size must compatible original size stride, .e., new view dimension must either subspace original dimension, span across original dimensions d, d+1, \\dots, d+k satisfy following contiguity-like condition \\forall = d, \\dots, d+k-1, \\[ \\text{stride}[] = \\text{stride}[+1] \\times \\text{size}[+1] \\] Otherwise, possible view self tensor shape without copying (e.g., via contiguous). unclear whether view can performed, advisable use :meth:reshape, returns view shapes compatible, copies (equivalent calling contiguous) otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-57","dir":"Articles > Tensor","previous_headings":"view","what":"Arguments:","title":"Tensor objects","text":"shape (torch_Size int…): desired size","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"view_as","dir":"Articles > Tensor","previous_headings":"","what":"view_as","title":"Tensor objects","text":"view_as() -> Tensor View tensor size . self$view_as() equivalent self$view(.size()). Please see $view information view.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-58","dir":"Articles > Tensor","previous_headings":"view_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor size .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"where","dir":"Articles > Tensor","previous_headings":"","what":"where","title":"Tensor objects","text":"(condition, y) -> Tensor self$(condition, y) equivalent torch_where(condition, self, y). See ?torch_where","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"zero_","dir":"Articles > Tensor","previous_headings":"","what":"zero_","title":"Tensor objects","text":"zero_() -> Tensor Fills self tensor zeros.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"from-r-objects","dir":"Articles","previous_headings":"","what":"From R objects","title":"Creating tensors","text":"can create tensors R objects using torch_tensor function. torch_tensor function takes R vector, matrix array creates equivalent torch_tensor. can see examples : default, create tensors cpu device, converting R datatype corresponding torch dtype. Note currently, numeric boolean types supported. can always modify dtype device converting R object torch tensor. example: options available creating tensor : requires_grad: boolean indicating want autograd record operations automatic differentiation. pin_memory: – set, tensor returned allocated pinned memory. Works CPU tensors. options available functions can used create new tensors, including factory functions listed next section.","code":"torch_tensor(c(1,2,3)) #> torch_tensor #>  1 #>  2 #>  3 #> [ CPUFloatType{3} ] # conform to row-major indexing used in torch torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE)) #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #> [ CPULongType{2,5} ] torch_tensor(array(runif(12), dim = c(2, 2, 3))) #> torch_tensor #> (1,.,.) =  #>   0.6008  0.4978  0.8746 #>   0.0074  0.7329  0.0342 #>  #> (2,.,.) =  #>   0.1572  0.2898  0.1749 #>   0.4664  0.7725  0.3204 #> [ CPUFloatType{2,2,3} ] torch_tensor(1, dtype = torch_long()) #> torch_tensor #>  1 #> [ CPULongType{1} ] torch_tensor(1, device = \"cpu\", dtype = torch_float64()) #> torch_tensor #>  1 #> [ CPUDoubleType{1} ]"},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"using-creation-functions","dir":"Articles","previous_headings":"","what":"Using creation functions","title":"Creating tensors","text":"can also use torch_* functions listed create torch tensors using algorithm. example, torch_randn function create tensors using normal distribution mean 0 standard deviation 1. can use ... argument pass size dimensions. example, code create normally distributed tensor shape 5x3. Another example torch_ones, creates tensor filled ones. full list functions can used bulk-create tensors torch: torch_arange: Returns tensor sequence integers, torch_empty: Returns tensor uninitialized values, torch_eye: Returns identity matrix, torch_full: Returns tensor filled single value, torch_linspace: Returns tensor values linearly spaced interval, torch_logspace: Returns tensor values logarithmically spaced interval, torch_ones: Returns tensor filled ones, torch_rand: Returns tensor filled values drawn uniform distribution [0, 1). torch_randint: Returns tensor integers randomly drawn interval, torch_randn: Returns tensor filled values drawn unit normal distribution, torch_randperm: Returns tensor filled random permutation integers interval, torch_zeros: Returns tensor filled zeros.","code":"x <- torch_randn(5, 3) x #> torch_tensor #>  0.8893  0.0678  0.1857 #>  2.8248 -1.0283 -2.1778 #> -1.9115 -0.3700 -0.5116 #>  0.6165  0.7820  0.5383 #> -0.4962 -0.7342 -0.2521 #> [ CPUFloatType{5,3} ] x <- torch_ones(2, 4, dtype = torch_int64(), device = \"cpu\") x #> torch_tensor #>  1  1  1  1 #>  1  1  1  1 #> [ CPULongType{2,4} ]"},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"conversion","dir":"Articles","previous_headings":"","what":"Conversion","title":"Creating tensors","text":"tensor exists can convert dtypes move different device method. example: can also copy tensor GPU using:","code":"x <- torch_tensor(1) y <- x$to(dtype = torch_int32()) x #> torch_tensor #>  1 #> [ CPUFloatType{1} ] y #> torch_tensor #>  1 #> [ CPUIntType{1} ] x <- torch_tensor(1) y <- x$cuda())"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"tracing","dir":"Articles","previous_headings":"Creating TorchScript programs","what":"Tracing","title":"TorchScript","text":"TorchScript programs can created R using tracing. using tracing, code automatically converted subset Python recording actual operators tensors simply executing discarding surrounding R code. Currently tracing supported way create TorchScript programs R code. example, let’s use jit_trace function create TorchScript program. pass regular R function example inputs. jit_trace function executed R function example input recorded torch operations occurred execution create graph. graph call intermediate representation TorchScript programs, can inspected : traced function can now invoked regular R function: ’s also possible trace nn_modules() defined R, example: using jit_trace nn_module forward method traced. can use jit_trace_module function pass example inputs methods. Traced modules look like normal nn_modules(), can called way:","code":"fn <- function(x) {   torch_relu(x) }  traced_fn <- jit_trace(fn, torch_tensor(c(-1, 0, 1))) traced_fn$graph #> graph(%0 : Float(3, strides=[1], requires_grad=0, device=cpu)): #>   %1 : Float(3, strides=[1], requires_grad=0, device=cpu) = aten::relu(%0) #>   return (%1) traced_fn(torch_randn(3)) #> torch_tensor #>  0.0000 #>  0.7543 #>  0.0000 #> [ CPUFloatType{3} ] module <- nn_module(   initialize = function() {     self$linear1 <- nn_linear(10, 10)     self$linear2 <- nn_linear(10, 1)   },   forward = function(x) {     x %>%        self$linear1() %>%        nnf_relu() %>%        self$linear2()   } ) traced_module <- jit_trace(module(), torch_randn(10, 10)) traced_module(torch_randn(3, 10)) #> torch_tensor #> 0.01 * #>  5.6937 #>   3.3835 #>   4.8311 #> [ CPUFloatType{3,1} ][ grad_fn = <AddmmBackward0> ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"limitations-of-tracing","dir":"Articles","previous_headings":"Creating TorchScript programs > Tracing","what":"Limitations of tracing","title":"TorchScript","text":"Tracing record control flow like -statements loops. control flow constant across module, fine often inlines control flow decisions. sometimes control flow actually part model . instance, recurrent network loop (possibly dynamic) length input sequence. example: returned ScriptModule, operations different behaviors training eval modes always behave mode tracing, matter mode ScriptModule . example: Tracing proegrams can take tensors lists tensors input return tensors lists tensors. example:","code":"# fn does does an operation for each dimension of a tensor fn <- function(x) {   x %>%      torch_unbind(dim = 1) %>%      lapply(function(x) x$sum()) %>%      torch_stack(dim = 1) } # we trace using as an example a tensor with size (10, 5, 5) traced_fn <- jit_trace(fn, torch_randn(10, 5, 5)) # applying it with a tensor with different size returns an error. traced_fn(torch_randn(11, 5, 5)) #> Error in cpp_call_traced_fn(ptr, inputs): The following operation failed in the TorchScript interpreter. #> Traceback of TorchScript (most recent call last): #> RuntimeError: Expected 10 elements in a list but found 11 traced_dropout <- jit_trace(nn_dropout(), torch_ones(5,5)) traced_dropout(torch_ones(3,3)) #> torch_tensor #>  2  0  0 #>  2  0  0 #>  2  0  2 #> [ CPUFloatType{3,3} ] traced_dropout$eval() # even after setting to eval mode, dropout is applied traced_dropout(torch_ones(3,3)) #> torch_tensor #>  0  2  0 #>  2  0  2 #>  0  2  2 #> [ CPUFloatType{3,3} ] fn <- function(x, y) {   x + y } jit_trace(fn, torch_tensor(1), 1) #> Error in cpp_trace_function(tr_fn, list(...), .compilation_unit, strict, : Only tensors or (possibly nested) dict or tuples of tensors can be inputs to traced functions. Got float #> Exception raised from addInput at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/frontend/tracer.cpp:422 (most recent call first): #> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 81 (0x105089ca1 in libc10.dylib) #> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 98 (0x105088342 in libc10.dylib) #> frame #2: torch::jit::tracer::addInput(std::__1::shared_ptr<torch::jit::tracer::TracingState> const&, c10::IValue const&, c10::Type::SingletonOrSharedTypePtr<c10::Type> const&, torch::jit::Value*) + 6052 (0x1270ce564 in libtorch_cpu.dylib) #> frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr<torch::jit::tracer::TracingState> const&, c10::IValue const&, c10::Type::SingletonOrSharedTypePtr<c10::Type> const&, torch::jit::Value*) + 4445 (0x1270cdf1d in libtorch_cpu.dylib) #> frame #4: torch::jit::tracer::trace(std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue>>, std::__1::function<std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue>> (std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue>>)> const&, std::__1::function<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> (at::Tensor const&)>, bool, bool, torch::jit::Module*, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>> const&) + 826 (0x1270cb59a in libtorch_cpu.dylib) #> frame #5: _lantern_trace_fn + 597 (0x109b5fb35 in liblantern.dylib) #> frame #6: cpp_trace_function(Rcpp::Function_Impl<Rcpp::PreserveStorage>, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 566 (0x107d05176 in torchpkg.so) #> frame #7: _torch_cpp_trace_function + 719 (0x107b3facf in torchpkg.so) #> frame #8: R_doDotCall + 13245 (0x102395a3d in libR.dylib) #> frame #9: bcEval_loop + 146643 (0x1023fd6d3 in libR.dylib) #> frame #10: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #11: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #12: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #13: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #14: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> frame #15: do_eval + 1253 (0x1023d20e5 in libR.dylib) #> frame #16: bcEval_loop + 44476 (0x1023e47bc in libR.dylib) #> frame #17: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #18: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #19: forcePromise + 230 (0x1023cb5a6 in libR.dylib) #> frame #20: Rf_eval + 634 (0x1023caafa in libR.dylib) #> frame #21: do_withVisible + 57 (0x1023d2479 in libR.dylib) #> frame #22: do_internal + 362 (0x10244a0aa in libR.dylib) #> frame #23: bcEval_loop + 45103 (0x1023e4a2f in libR.dylib) #> frame #24: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #25: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #26: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #27: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #28: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> frame #29: do_begin + 429 (0x1023cffad in libR.dylib) #> frame #30: Rf_eval + 990 (0x1023cac5e in libR.dylib) #> frame #31: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #32: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #33: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> frame #34: do_docall + 615 (0x10235b827 in libR.dylib) #> frame #35: bcEval_loop + 44476 (0x1023e47bc in libR.dylib) #> frame #36: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #37: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #38: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #39: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #40: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> frame #41: do_docall + 615 (0x10235b827 in libR.dylib) #> frame #42: bcEval_loop + 44476 (0x1023e47bc in libR.dylib) #> frame #43: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #44: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #45: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #46: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #47: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> frame #48: forcePromise + 230 (0x1023cb5a6 in libR.dylib) #> frame #49: bcEval_loop + 19496 (0x1023de628 in libR.dylib) #> frame #50: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #51: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #52: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #53: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #54: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> frame #55: do_begin + 429 (0x1023cffad in libR.dylib) #> frame #56: Rf_eval + 990 (0x1023cac5e in libR.dylib) #> frame #57: forcePromise + 230 (0x1023cb5a6 in libR.dylib) #> frame #58: bcEval_loop + 19496 (0x1023de628 in libR.dylib) #> frame #59: bcEval + 628 (0x1023cb374 in libR.dylib) #> frame #60: Rf_eval + 506 (0x1023caa7a in libR.dylib) #> frame #61: R_execClosure + 761 (0x1023cd5b9 in libR.dylib) #> frame #62: applyClosure_core + 128 (0x1023cc6c0 in libR.dylib) #> frame #63: Rf_eval + 1189 (0x1023cad25 in libR.dylib) #> :"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"compiling-torchscript","dir":"Articles","previous_headings":"Creating TorchScript programs","what":"Compiling TorchScript","title":"TorchScript","text":"’s also possible create TorchScript programs compiling TorchScript code. TorchScript code looks lot like standard python code. example:","code":"tr <- jit_compile(\" def fn (x: Tensor):   return torch.relu(x)  \") tr$fn(torch_tensor(c(-1, 0, 1))) #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"serializing-and-loading","dir":"Articles","previous_headings":"","what":"Serializing and loading","title":"TorchScript","text":"TorchScript programs can serialized using jit_save function loaded back disk jit_load. example: Loaded programs can executed usual: Note can load TorchScript programs created libraries different torch R. Eg, TorchScript program can created PyTorch torch.jit.trace torch.jit.script, run R. R objects automatically converted TorchScript counterpart following Types table document. However, sometimes ’s necessary make type annotations jit_tuple() jit_scalar() disambiguate conversion.","code":"fn <- function(x) {   torch_relu(x) } tr_fn <- jit_trace(fn, torch_tensor(1)) jit_save(tr_fn, \"path.pt\") loaded <- jit_load(\"path.pt\") loaded(torch_tensor(c(-1, 0, 1))) #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"types","dir":"Articles","previous_headings":"","what":"Types","title":"TorchScript","text":"following table lists TorchScript types convert back R.","code":""},{"path":"https://torch.mlverse.org/docs/articles/using-autograd.html","id":"automatic-differentiation-with-autograd","dir":"Articles","previous_headings":"","what":"Automatic differentiation with autograd","title":"Using autograd","text":"Torch uses module called autograd record operations performed tensors, store done obtain respective gradients. actions stored functions, functions applied order gradient output (normally, loss) respect tensors calculated: starting output node propagating gradients back network. form reverse mode automatic differentiation. users, can see bit implementation. prerequisite “recording” happen, tensors created requires_grad = TRUE. E.g. clear, tensor respect gradients calculated – normally, tensor representing weight bias, input data 1. now perform operation tensor, assigning result y find y now non-empty grad_fn tells torch compute gradient y respect x: Actual computation gradients triggered calling backward() output tensor. executed, x now non-empty field grad stores gradient y respect x: longer chain computations, can peek torch builds graph backward operations. slightly complex example. call retain_grad() y z just demonstration purposes; default, intermediate gradients – course computed – aren’t stored, order save memory. Starting $grad_fn, can follow graph back leaf nodes: calling $backward(), tensors graph respective gradients created. Without calls retain_grad , z$grad y$grad empty: Thus acquainted autograd, ’re ready modify example.","code":"x <- torch_ones(2,2, requires_grad = TRUE) y <- x$mean() y$grad_fn #> MeanBackward0 y$backward() x$grad #> torch_tensor #>  0.2500  0.2500 #>  0.2500  0.2500 #> [ CPUFloatType{2,2} ] x1 <- torch_ones(2,2, requires_grad = TRUE) x2 <- torch_tensor(1.1, requires_grad = TRUE) y <- x1 * (x2 + 2) y$retain_grad() z <- y$pow(2) * 3 z$retain_grad() out <- z$mean() # how to compute the gradient for mean, the last operation executed out$grad_fn #> MeanBackward0 # how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3 out$grad_fn$next_functions #> [[1]] #> MulBackward1 # how to compute the gradient for pow in z = y.pow(2) * 3 out$grad_fn$next_functions[[1]]$next_functions #> [[1]] #> PowBackward0 # how to compute the gradient for the multiplication in y = x * (x + 2) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions #> [[1]] #> MulBackward0 # how to compute the gradient for the two branches of y = x * (x + 2), # where the left branch is a leaf node (AccumulateGrad for x1) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions #> [[1]] #> torch::autograd::AccumulateGrad #>  #> [[2]] #> AddBackward1 # here we arrive at the other leaf node (AccumulateGrad for x2) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions #> [[1]] #> torch::autograd::AccumulateGrad out$backward() z$grad #> torch_tensor #>  0.2500  0.2500 #>  0.2500  0.2500 #> [ CPUFloatType{2,2} ] y$grad #> torch_tensor #>  4.6500  4.6500 #>  4.6500  4.6500 #> [ CPUFloatType{2,2} ] x2$grad #> torch_tensor #>  18.6000 #> [ CPUFloatType{1} ] x1$grad #> torch_tensor #>  14.4150  14.4150 #>  14.4150  14.4150 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/articles/using-autograd.html","id":"the-simple-network-now-using-autograd","dir":"Articles","previous_headings":"","what":"The simple network, now using autograd","title":"Using autograd","text":"single new line calling loss$backward(), now number lines (manual backprop) gone: still manually compute forward pass, still manually update weights. last two chapters section, ’ll see parts logic can made modular reusable, well.","code":"### generate training data ----------------------------------------------------- # input dimensionality (number of input features) d_in <- 3 # output dimensionality (number of predicted features) d_out <- 1 # number of observations in training set n <- 100 # create random data x <- torch_randn(n, d_in) y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n) y <- y$unsqueeze(dim = 1) ### initialize weights --------------------------------------------------------- # dimensionality of hidden layer d_hidden <- 32 # weights connecting input to hidden layer w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE) # weights connecting hidden to output layer w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE) # hidden layer bias b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE) # output layer bias b2 <- torch_zeros(1, d_out,requires_grad = TRUE) ### network parameters --------------------------------------------------------- learning_rate <- 1e-4 ### training loop -------------------------------------------------------------- for (t in 1:200) {      ### -------- Forward pass --------      y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)     ### -------- compute loss --------      loss <- (y_pred - y)$pow(2)$mean()     if (t %% 10 == 0) cat(t, as_array(loss), \"\\n\")     ### -------- Backpropagation --------      # compute the gradient of loss with respect to all tensors with requires_grad = True.     loss$backward()       ### -------- Update weights --------           # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation     with_no_grad({              w1$sub_(learning_rate * w1$grad)       w2$sub_(learning_rate * w2$grad)       b1$sub_(learning_rate * b1$grad)       b2$sub_(learning_rate * b2$grad)              # Zero the gradients after every pass, because they'd accumulate otherwise       w1$grad$zero_()       w2$grad$zero_()       b1$grad$zero_()       b2$grad$zero_()          })      } #> 10 59.5981  #> 20 54.32401  #> 30 49.66991  #> 40 45.58003  #> 50 41.95185  #> 60 38.72104  #> 70 35.83778  #> 80 33.25837  #> 90 30.9521  #> 100 28.87896  #> 110 27.00857  #> 120 25.31741  #> 130 23.78777  #> 140 22.40333  #> 150 21.1463  #> 160 20.00305  #> 170 18.96302  #> 180 18.01377  #> 190 17.14516  #> 200 16.34873"},{"path":"https://torch.mlverse.org/docs/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Daniel Falbel. Author, maintainer, copyright holder. Javier Luraschi. Author. Dmitriy Selivanov. Contributor. Athos Damiani. Contributor. Christophe Regouby. Contributor. Krzysztof Joachimiak. Contributor. Hamada S. Badr. Contributor. Sebastian Fischer. Contributor. RStudio. Copyright holder.","code":""},{"path":"https://torch.mlverse.org/docs/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Falbel D, Luraschi J (2024). torch: Tensors Neural Networks 'GPU' Acceleration. R package version 0.13.0, https://github.com/mlverse/torch, https://torch.mlverse.org/docs.","code":"@Manual{,   title = {torch: Tensors and Neural Networks with 'GPU' Acceleration},   author = {Daniel Falbel and Javier Luraschi},   year = {2024},   note = {R package version 0.13.0, https://github.com/mlverse/torch},   url = {https://torch.mlverse.org/docs}, }"},{"path":[]},{"path":"https://torch.mlverse.org/docs/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tensors and Neural Networks with GPU Acceleration","text":"torch can installed CRAN : can also install development version : first package load additional software installed. See also full installation guide .","code":"install.packages(\"torch\") remotes::install_github(\"mlverse/torch\")"},{"path":"https://torch.mlverse.org/docs/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Tensors and Neural Networks with GPU Acceleration","text":"can create torch tensors R objects torch_tensor function convert back R objects as_array.","code":"library(torch) x <- array(runif(8), dim = c(2, 2, 2)) y <- torch_tensor(x, dtype = torch_float64()) y #> torch_tensor #> (1,.,.) =  #>   0.6192  0.5800 #>   0.2488  0.3681 #>  #> (2,.,.) =  #>   0.0042  0.9206 #>   0.4388  0.5664 #> [ CPUDoubleType{2,2,2} ] identical(x, as_array(y)) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/index.html","id":"simple-autograd-example","dir":"","previous_headings":"Examples","what":"Simple Autograd Example","title":"Tensors and Neural Networks with GPU Acceleration","text":"following snippet let torch, using autograd feature, calculate derivatives:","code":"x <- torch_tensor(1, requires_grad = TRUE) w <- torch_tensor(2, requires_grad = TRUE) b <- torch_tensor(3, requires_grad = TRUE) y <- w * x + b y$backward() x$grad #> torch_tensor #>  2 #> [ CPUFloatType{1} ] w$grad #> torch_tensor #>  1 #> [ CPUFloatType{1} ] b$grad #> torch_tensor #>  1 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Tensors and Neural Networks with GPU Acceleration","text":"matter current skills ’s possible contribute torch development. See contributing guide information.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":null,"dir":"Reference","previous_headings":"","what":"Class representing the context. — AutogradContext","title":"Class representing the context. — AutogradContext","text":"Class representing context. Class representing context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Class representing the context. — AutogradContext","text":"ptr (Dev related) pointer context c++ object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Class representing the context. — AutogradContext","text":"needs_input_grad boolean listing arguments forward whether require_grad. saved_variables list objects saved backward via save_for_backward.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Class representing the context. — AutogradContext","text":"AutogradContext$new() AutogradContext$save_for_backward() AutogradContext$mark_non_differentiable() AutogradContext$mark_dirty() AutogradContext$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Class representing the context. — AutogradContext","text":"(Dev related) Initializes context. user related.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$new(   ptr,   env,   argument_names = NULL,   argument_needs_grad = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"ptr pointer c++ object env environment encloses forward backward argument_names names forward arguments argument_needs_grad whether argument forward needs grad.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-save-for-backward-","dir":"Reference","previous_headings":"","what":"Method save_for_backward()","title":"Class representing the context. — AutogradContext","text":"Saves given objects future call backward(). called , inside forward() method. Later, saved objects can accessed saved_variables attribute. returning user, check made ensure weren’t used -place operation modified content. Arguments can also kind R object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$save_for_backward(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... kind R object saved backward pass. common pass named arguments.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-mark-non-differentiable-","dir":"Reference","previous_headings":"","what":"Method mark_non_differentiable()","title":"Class representing the context. — AutogradContext","text":"Marks outputs non-differentiable. called , inside forward() method, arguments outputs. mark outputs requiring gradients, increasing efficiency backward computation. still need accept gradient output backward(), ’s always going zero tensor shape shape corresponding output. used e.g. indices returned max Function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$mark_non_differentiable(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... non-differentiable outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-mark-dirty-","dir":"Reference","previous_headings":"","what":"Method mark_dirty()","title":"Class representing the context. — AutogradContext","text":"Marks given tensors modified -place operation. called , inside forward() method, arguments inputs. Every tensor ’s modified -place call forward() given function, ensure correctness checks. doesn’t matter whether function called modification.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$mark_dirty(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... tensors modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Class representing the context. — AutogradContext","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Abstract base class for constraints. — Constraint","title":"Abstract base class for constraints. — Constraint","text":"Abstract base class constraints. Abstract base class constraints.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Abstract base class for constraints. — Constraint","text":"constraint object represents region variable valid, e.g. within variable can optimized.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Abstract base class for constraints. — Constraint","text":"Constraint$check() Constraint$print() Constraint$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-check-","dir":"Reference","previous_headings":"","what":"Method check()","title":"Abstract base class for constraints. — Constraint","text":"Returns byte tensor sample_shape + batch_shape indicating whether event value satisfies constraint.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$check(value)"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract base class for constraints. — Constraint","text":"value event value checked.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Abstract base class for constraints. — Constraint","text":"Define print method constraints,","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$print()"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Abstract base class for constraints. — Constraint","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract base class for constraints. — Constraint","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic R6 class representing distributions — Distribution","title":"Generic R6 class representing distributions — Distribution","text":"Distribution abstract base class probability distributions. Note: Python, adding torch.Size objects works concatenation Try example: torch.Size((2, 1)) + torch.Size((1,))","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Generic R6 class representing distributions — Distribution","text":".validate_args whether validate arguments has_rsample whether rsample has_enumerate_support whether enumerate support","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape Returns shape parameters batched. event_shape Returns shape single sample (without batching). Returns dictionary argument names torch_Constraint objects satisfied argument distribution. Args tensors need appear dict. support Returns torch_Constraint object representing distribution's support. mean Returns mean distribution variance Returns variance distribution stddev Returns standard deviation distribution TODO: consider different message","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generic R6 class representing distributions — Distribution","text":"Distribution$new() Distribution$expand() Distribution$sample() Distribution$rsample() Distribution$log_prob() Distribution$cdf() Distribution$icdf() Distribution$enumerate_support() Distribution$entropy() Distribution$perplexity() Distribution$.extended_shape() Distribution$.validate_sample() Distribution$print() Distribution$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generic R6 class representing distributions — Distribution","text":"Initializes distribution class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$new(batch_shape = NULL, event_shape = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape shape parameters batched. event_shape shape single sample (without batching). validate_args whether validate arguments . Validation can time consuming might want disable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-expand-","dir":"Reference","previous_headings":"","what":"Method expand()","title":"Generic R6 class representing distributions — Distribution","text":"Returns new distribution instance (populates existing instance provided derived class) batch dimensions expanded batch_shape. method calls expand distribution’s parameters. , allocate new memory expanded distribution instance. Additionally, repeat args checking parameter broadcasting initialize, instance first created.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$expand(batch_shape, .instance = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape desired expanded size. .instance new instance provided subclasses need override expand.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-sample-","dir":"Reference","previous_headings":"","what":"Method sample()","title":"Generic R6 class representing distributions — Distribution","text":"Generates sample_shape shaped sample sample_shape shaped batch samples distribution parameters batched.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$sample(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape shape want sample.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-rsample-","dir":"Reference","previous_headings":"","what":"Method rsample()","title":"Generic R6 class representing distributions — Distribution","text":"Generates sample_shape shaped reparameterized sample sample_shape shaped batch reparameterized samples distribution parameters batched.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$rsample(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape shape want sample.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-log-prob-","dir":"Reference","previous_headings":"","what":"Method log_prob()","title":"Generic R6 class representing distributions — Distribution","text":"Returns log probability density/mass function evaluated value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$log_prob(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-cdf-","dir":"Reference","previous_headings":"","what":"Method cdf()","title":"Generic R6 class representing distributions — Distribution","text":"Returns cumulative density/mass function evaluated value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$cdf(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-icdf-","dir":"Reference","previous_headings":"","what":"Method icdf()","title":"Generic R6 class representing distributions — Distribution","text":"Returns inverse cumulative density/mass function evaluated value. @description Returns tensor containing values supported discrete distribution. result enumerate dimension 0, shape result (cardinality,) + batch_shape + event_shape (event_shape = ()univariate distributions). Note enumerates batched tensors lock-steplist(c(0, 0), c(1, 1), ...). expand=FALSE, enumeration happens along dim 0, remaining batch dimensions singleton dimensions, list(c(0), c(1), ...)`.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$icdf(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$enumerate_support(expand = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"expand (bool): whether expand support batch dims match distribution's batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor iterating dimension 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-entropy-","dir":"Reference","previous_headings":"","what":"Method entropy()","title":"Generic R6 class representing distributions — Distribution","text":"Returns entropy distribution, batched batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$entropy()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor shape batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-perplexity-","dir":"Reference","previous_headings":"","what":"Method perplexity()","title":"Generic R6 class representing distributions — Distribution","text":"Returns perplexity distribution, batched batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$perplexity()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor shape batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-extended-shape-","dir":"Reference","previous_headings":"","what":"Method .extended_shape()","title":"Generic R6 class representing distributions — Distribution","text":"Returns size sample returned distribution, given sample_shape. Note, batch event shapes distribution instance fixed time construction. empty, returned shape upcast (1,).","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$.extended_shape(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape (torch_Size): size sample drawn.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-validate-sample-","dir":"Reference","previous_headings":"","what":"Method .validate_sample()","title":"Generic R6 class representing distributions — Distribution","text":"Argument validation distribution methods log_prob, cdf icdf. rightmost dimensions value scored via methods must agree distribution's batch event shapes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-11","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$.validate_sample(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value (Tensor): tensor whose log probability computed log_prob method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Generic R6 class representing distributions — Distribution","text":"Prints distribution instance.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-12","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$print()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generic R6 class representing distributions — Distribution","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-13","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts to array — as_array","title":"Converts to array — as_array","text":"Converts array","code":""},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts to array — as_array","text":"","code":"as_array(x)"},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts to array — as_array","text":"x object converted array","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"graph differentiated using chain rule. tensors non-scalar (.e. data one element) require gradient, Jacobian-vector product computed, case function additionally requires specifying grad_tensors. sequence matching length, contains “vector” Jacobian-vector product, usually gradient differentiated function w.r.t. corresponding tensors (None acceptable value tensors don’t need gradient tensors).","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"","code":"autograd_backward(   tensors,   grad_tensors = NULL,   retain_graph = create_graph,   create_graph = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"tensors (list Tensor) – Tensors derivative computed. grad_tensors (list (Tensor NULL)) – “vector” Jacobian-vector product, usually gradients w.r.t. element corresponding tensors. NULLvalues can specified scalar Tensors ones don’t require grad. aNULL` value acceptable grad_tensors, argument optional. retain_graph (bool, optional) – FALSE, graph used compute grad freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional) – TRUE, graph derivative constructed, allowing compute higher order derivative products. Defaults FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"function accumulates gradients leaves - might need zero calling .","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(1, requires_grad = TRUE) y <- 2 * x  a <- torch_tensor(1, requires_grad = TRUE) b <- 3 * a  autograd_backward(list(y, b)) }"},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Records operation history and defines formulas for differentiating ops. — autograd_function","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"Every operation performed Tensor's creates new function object, performs computation, records happened. history retained form DAG functions, edges denoting data dependencies (input <- output). , backward called, graph processed topological ordering, calling backward() methods Function object, passing returned gradients next Function's.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"","code":"autograd_function(forward, backward)"},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"forward Performs operation. must accept context ctx first argument, followed number arguments (tensors types). context can used store tensors can retrieved backward pass. See AutogradContext information context methods. backward Defines formula differentiating operation. must accept context ctx first argument, followed many outputs ad forward() returned (list()). names arguments matter passed order returned forward(). function return named list, argument gradient w.r.t given output, element returned list gradient w.r.t. corresponding input. context can used retrieve tensors saved forward pass. also attribute ctx$needs_input_grad named list booleans representing whether input needs gradient. E.g., backward() ctx$needs_input_grad$input = TRUE input argument forward() needs gradient computated w.r.t. output. See AutogradContext information context methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"","code":"if (torch_is_installed()) {  exp2 <- autograd_function(   forward = function(ctx, i) {     result <- i$exp()     ctx$save_for_backward(result = result)     result   },   backward = function(ctx, grad_output) {     list(i = grad_output * ctx$saved_variable$result)   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"grad_outputs list length matching output containing “vector” Jacobian-vector product, usually pre-computed gradients w.r.t. outputs. output doesn’t require_grad, gradient can None).","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"","code":"autograd_grad(   outputs,   inputs,   grad_outputs = NULL,   retain_graph = create_graph,   create_graph = FALSE,   allow_unused = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"outputs (sequence Tensor) – outputs differentiated function. inputs (sequence Tensor) – Inputs w.r.t. gradient returned (accumulated .grad). grad_outputs (sequence Tensor) – “vector” Jacobian-vector product. Usually gradients w.r.t. output. None values can specified scalar Tensors ones don’t require grad. None value acceptable grad_tensors, argument optional. Default: None. retain_graph (bool, optional) – FALSE, graph used compute grad freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional) – TRUE, graph derivative constructed, allowing compute higher order derivative products. Default: FALSE`. allow_unused (bool, optional) – FALSE, specifying inputs used computing outputs (therefore grad always zero) error. Defaults FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"only_inputs TRUE, function return list gradients w.r.t specified inputs. ’s FALSE, gradient w.r.t. remaining leaves still computed, accumulated .grad attribute.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"","code":"if (torch_is_installed()) { w <- torch_tensor(0.5, requires_grad = TRUE) b <- torch_tensor(0.9, requires_grad = TRUE) x <- torch_tensor(runif(100)) y <- 2 * x + 1 loss <- (y - (w * x + b))^2 loss <- loss$mean()  o <- autograd_grad(loss, list(w, b)) o } #> [[1]] #> torch_tensor #> -0.9935 #> [ CPUFloatType{1} ] #>  #> [[2]] #> torch_tensor #> -1.6206 #> [ CPUFloatType{1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Set grad mode — autograd_set_grad_mode","title":"Set grad mode — autograd_set_grad_mode","text":"Sets disables gradient history.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set grad mode — autograd_set_grad_mode","text":"","code":"autograd_set_grad_mode(enabled)"},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set grad mode — autograd_set_grad_mode","text":"enabled bool wether enable disable gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"CuDNN is available — backends_cudnn_is_available","title":"CuDNN is available — backends_cudnn_is_available","text":"CuDNN available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CuDNN is available — backends_cudnn_is_available","text":"","code":"backends_cudnn_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_version.html","id":null,"dir":"Reference","previous_headings":"","what":"CuDNN version — backends_cudnn_version","title":"CuDNN version — backends_cudnn_version","text":"CuDNN version","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CuDNN version — backends_cudnn_version","text":"","code":"backends_cudnn_version()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MKL is available — backends_mkl_is_available","title":"MKL is available — backends_mkl_is_available","text":"MKL available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MKL is available — backends_mkl_is_available","text":"","code":"backends_mkl_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MKL is available — backends_mkl_is_available","text":"Returns whether LibTorch built MKL support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MKLDNN is available — backends_mkldnn_is_available","title":"MKLDNN is available — backends_mkldnn_is_available","text":"MKLDNN available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MKLDNN is available — backends_mkldnn_is_available","text":"","code":"backends_mkldnn_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MKLDNN is available — backends_mkldnn_is_available","text":"Returns whether LibTorch built MKL-DNN support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mps_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MPS is available — backends_mps_is_available","title":"MPS is available — backends_mps_is_available","text":"MPS available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mps_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MPS is available — backends_mps_is_available","text":"","code":"backends_mps_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mps_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MPS is available — backends_mps_is_available","text":"Returns whether LibTorch built MPS support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"OpenMP is available — backends_openmp_is_available","title":"OpenMP is available — backends_openmp_is_available","text":"OpenMP available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OpenMP is available — backends_openmp_is_available","text":"","code":"backends_openmp_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"OpenMP is available — backends_openmp_is_available","text":"Returns whether LibTorch built OpenMP support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","title":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","text":"Raises value_error: values numeric instance, torch.*Tensor instance, instance implementing torch_function TODO: add has_torch_function((v,)) See: https://github.com/pytorch/pytorch/blob/master/torch/distributions/utils.py","code":""},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","text":"","code":"broadcast_all(values)"},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","text":"values List : torch.*Tensor instances broadcasted per _broadcasting-semantics. numeric instances (scalars) upcast tensors size type first tensor passed values.  values scalars, upcasted scalar Tensors. values (list numeric, torch.*Tensor objects implementing torch_function)","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a (Potentially Unexported) Torch Function — call_torch_function","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"function allows calling function prefixed torch_, including unexported functions potentially valuable uses yet user-friendly R wrapper function. Therefore, function used extreme caution. Make sure understand function expects input. may helpful read torch source code help , well documentation corresponding function Pytorch C++ API. Generally development advanced use .","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"","code":"call_torch_function(name, ..., quiet = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"name Name function call string. start \"torch_\" ... list arguments pass function. Argument splicing !!! supported. quiet TRUE, suppress warnings valuable information dangers function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"return value calling function name arguments ...","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"","code":"if (torch_is_installed()) { ## many unexported functions do 'backward' calculations (e.g. derivatives) ## These could be used as a part of custom autograd functions for example. x <- torch_randn(10, requires_grad = TRUE) y <- torch_tanh(x) ## calculate backwards gradient using standard torch method y$backward(torch_ones_like(x)) x$grad ## we can get the same result by calling the unexported `torch_tanh_backward()` ## function. The first argument is 1 to setup the Jacobian-vector product. ## see https://pytorch.org/blog/overview-of-pytorch-autograd-engine/ for details. call_torch_function(\"torch_tanh_backward\", 1, y) all.equal(call_torch_function(\"torch_tanh_backward\", 1, y, quiet = TRUE), x$grad)  } #> Warning: Because this function allows access to unexported functions, please use with caution, and #>             only if you are sure know what you are doing. Unexported functions will expect inputs that #>             are more C++-like than R-like. For example, they will expect all indexes to be 0-based instead #>             of 1-based. In addition unexported functions may be subject to removal from the API without #>             warning. Set quiet = TRUE to silence this warning. #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone a torch module. — clone_module","title":"Clone a torch module. — clone_module","text":"Clones module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone a torch module. — clone_module","text":"","code":"clone_module(module, deep = FALSE, ..., replace_values = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone a torch module. — clone_module","text":"module (nn_module) module clone deep (logical(1)) Whether create deep clone. ... () Additional parameters, currently unused. replace_values (logical(1)) Whether replace parameters buffers cloned values.","code":""},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clone a torch module. — clone_module","text":"","code":"if (torch_is_installed()) { clone_module(nn_linear(1, 1), deep = TRUE) # is the same as nn_linear(1, 1)$clone(deep = TRUE) } #> An `nn_module` containing 2 parameters. #>  #> ── Parameters ────────────────────────────────────────────────────────────────── #> • weight: Float [1:1, 1:1] #> • bias: Float [1:1]"},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":null,"dir":"Reference","previous_headings":"","what":"Contrib sort vertices — contrib_sort_vertices","title":"Contrib sort vertices — contrib_sort_vertices","text":"Based implementation Rotated_IoU","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contrib sort vertices — contrib_sort_vertices","text":"","code":"contrib_sort_vertices(vertices, mask, num_valid)"},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contrib sort vertices — contrib_sort_vertices","text":"vertices Tensor vertices. mask tensors containing masks. num_valid integer tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Contrib sort vertices — contrib_sort_vertices","text":"tensors CUDA device function can used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Contrib sort vertices — contrib_sort_vertices","text":"function make part official torch API.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contrib sort vertices — contrib_sort_vertices","text":"","code":"if (torch_is_installed()) { if (cuda_is_available()) {   v <- torch_randn(8, 1024, 24, 2)$cuda()   mean <- torch_mean(v, dim = 2, keepdim = TRUE)   v <- v - mean   m <- (torch_rand(8, 1024, 24) > 0.8)$cuda()   nv <- torch_sum(m$to(dtype = torch_int()), dim = -1)$to(dtype = torch_int())$cuda()   result <- contrib_sort_vertices(v, m, nv) } }"},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a gradient scaler — cuda_amp_grad_scaler","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"gradient scaler instance used perform dynamic gradient scaling avoid gradient underflow training mixed precision.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"","code":"cuda_amp_grad_scaler(   init_scale = 2^16,   growth_factor = 2,   backoff_factor = 0.5,   growth_interval = 2000,   enabled = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"init_scale numeric value indicating initial scale factor. growth_factor numeric value indicating growth factor. backoff_factor numeric value indicating backoff factor. growth_interval numeric value indicating growth interval. enabled logical value indicating whether gradient scaler enabled.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"gradient scaler object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_current_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the index of a currently selected device. — cuda_current_device","title":"Returns the index of a currently selected device. — cuda_current_device","text":"Returns index currently selected device.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_current_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the index of a currently selected device. — cuda_current_device","text":"","code":"cuda_current_device()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_device_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the number of GPUs available. — cuda_device_count","title":"Returns the number of GPUs available. — cuda_device_count","text":"Returns number GPUs available.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_device_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the number of GPUs available. — cuda_device_count","text":"","code":"cuda_device_count()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_empty_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty cache — cuda_empty_cache","title":"Empty cache — cuda_empty_cache","text":"Releases unoccupied cached memory currently held caching allocator can used GPU application visible nvidia-smi.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_empty_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty cache — cuda_empty_cache","text":"","code":"cuda_empty_cache()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_empty_cache.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Empty cache — cuda_empty_cache","text":"cuda_empty_cache() doesn’t increase amount GPU memory available torch. However, may help reduce fragmentation GPU memory certain cases. See Memory management article details GPU memory management.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"Returns major minor CUDA capability device","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"","code":"cuda_get_device_capability(device = cuda_current_device())"},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"device Integer value CUDA device return capabilities .","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","title":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","text":"Returns bool indicating CUDA currently available.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","text":"","code":"cuda_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"return value function dictionary statistics, non-negative integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"","code":"cuda_memory_stats(device = cuda_current_device())  cuda_memory_summary(device = cuda_current_device())"},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"device Integer value CUDA device return capabilities .","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"core-statistics","dir":"Reference","previous_headings":"","what":"Core statistics","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"\"allocated.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number allocation requests received memory allocator. \"allocated_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount allocated memory. \"segment.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number reserved segments cudaMalloc(). \"reserved_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount reserved memory. \"active.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number active memory blocks. \"active_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount active memory. \"inactive_split.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number inactive, non-releasable memory blocks. \"inactive_split_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount inactive, non-releasable memory. core statistics, values broken follows. Pool type: : combined statistics across memory pools. large_pool: statistics large allocation pool (October 2019, size >= 1MB allocations). small_pool: statistics small allocation pool (October 2019, size < 1MB allocations). Metric type: current: current value metric. peak: maximum value metric. allocated: historical total increase metric. freed: historical total decrease metric.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"additional-metrics","dir":"Reference","previous_headings":"","what":"Additional metrics","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"\"num_alloc_retries\": number failed cudaMalloc calls result cache flush retry. \"num_ooms\": number --memory errors thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_runtime_version.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the CUDA runtime version — cuda_runtime_version","title":"Returns the CUDA runtime version — cuda_runtime_version","text":"Returns CUDA runtime version","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_runtime_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the CUDA runtime version — cuda_runtime_version","text":"","code":"cuda_runtime_version()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_synchronize.html","id":null,"dir":"Reference","previous_headings":"","what":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","title":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","text":"Waits kernels streams CUDA device complete.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_synchronize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","text":"","code":"cuda_synchronize(device = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/cuda_synchronize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","text":"device device synchronize. uses current device given cuda_current_device() device specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"Data loader. Combines dataset sampler, provides single- multi-process iterators dataset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"","code":"dataloader(   dataset,   batch_size = 1,   shuffle = FALSE,   sampler = NULL,   batch_sampler = NULL,   num_workers = 0,   collate_fn = NULL,   pin_memory = FALSE,   drop_last = FALSE,   timeout = -1,   worker_init_fn = NULL,   worker_globals = NULL,   worker_packages = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"dataset (Dataset): dataset load data. batch_size (int, optional): many samples per batch load (default: 1). shuffle (bool, optional): set TRUE data reshuffled every epoch (default: FALSE). sampler (Sampler, optional): defines strategy draw samples dataset. specified, shuffle must False. Custom samplers can created sampler(). batch_sampler (Sampler, optional): like sampler, returns batch indices time. Mutually exclusive batch_size, shuffle, sampler, drop_last. Custom samplers can created sampler(). num_workers (int, optional): many subprocesses use data loading. 0 means data loaded main process. (default: 0) collate_fn (callable, optional): merges list samples form mini-batch. pin_memory (bool, optional): TRUE, data loader copy tensors CUDA pinned memory returning .  data elements custom type, collate_fn returns batch custom type see example . drop_last (bool, optional): set TRUE drop last incomplete batch, dataset size divisible batch size. FALSE size dataset divisible batch size, last batch smaller. (default: FALSE) timeout (numeric, optional): positive, timeout value collecting batch workers. -1 means timeout. (default: -1) worker_init_fn (callable, optional): NULL, called worker subprocess worker id (int [1, num_workers]) input, seeding data loading. (default: NULL) worker_globals (list character vector, optional) used num_workers > 0. character vector, objects names copied global environment workers. named list, list copied attached worker global environment. Notice objects copied worker initialization. worker_packages (character vector, optional) used num_workers > 0 optional character vector naming packages loaded worker.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"parallel-data-loading","dir":"Reference","previous_headings":"","what":"Parallel data loading","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"using num_workers > 0 data loading happen parallel worker. Note batches taken parallel observations. worker initialization  process happens following order: num_workers R sessions initialized. worker perform following actions: torch library loaded. random seed set using set.seed() using torch_manual_seed. packages passed worker_packages argument loaded. objects passed trough worker_globals parameters copied global environment. worker_init function ran id argument. dataset fetcher copied worker.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an iterator from a DataLoader — dataloader_make_iter","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"Creates iterator DataLoader","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"","code":"dataloader_make_iter(dataloader)"},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"dataloader dataloader object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the next element of a dataloader iterator — dataloader_next","title":"Get the next element of a dataloader iterator — dataloader_next","text":"Get next element dataloader iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the next element of a dataloader iterator — dataloader_next","text":"","code":"dataloader_next(iter, completed = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the next element of a dataloader iterator — dataloader_next","text":"iter DataLoader iter created dataloader_make_iter. completed returned value iterator exhausted.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create an function that generates R6 instances of class dataset — dataset","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"datasets represent map keys data samples subclass class. subclasses overwrite .getitem() method, supports fetching data sample given key. Subclasses also optionally overwrite .length(), expected return size dataset (e.g. number samples) used many sampler implementations default options dataloader().","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"","code":"dataset(   name = NULL,   inherit = Dataset,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"name name dataset. also used class . inherit can optionally inherit dataset creating new dataset. ... public methods dataset class private passed R6::R6Class(). active passed R6::R6Class(). parent_env environment use parent newly-created objects.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"output function f class dataset_generator. Calling f() creates new instance R6 class dataset. R6 class stored enclosing environment f can also accessed fs attribute Dataset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"dataloader()  default constructs index sampler yields integral indices.  make work map-style dataset non-integral indices/keys, custom sampler must provided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"get-a-batch-of-observations","dir":"Reference","previous_headings":"","what":"Get a batch of observations","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"default datasets iterated returning observation/item individually. Often possible optimized implementation take batch observations (eg, subsetting tensor multiple indexes faster subsetting index), case can implement .getbatch method used instead .getitem getting batch observations within dataloader. .getbatch must work batches size larger equal 1 care must taken drop batch dimension queried length 1 batch index - instance using drop=FALSE. .getitem() expected include batch dimension added datalaoder. see vignette(\"loading-data\").","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Dataset Subset — dataset_subset","title":"Dataset Subset — dataset_subset","text":"Subset dataset specified indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dataset Subset — dataset_subset","text":"","code":"dataset_subset(dataset, indices)"},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dataset Subset — dataset_subset","text":"dataset (Dataset): whole Dataset indices (sequence): Indices whole set selected subset","code":""},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets and sets the default floating point dtype. — torch_set_default_dtype","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"Gets sets default floating point dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"","code":"torch_set_default_dtype(d)  torch_get_default_dtype()"},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"d default floating point dtype set. Initially set torch_float().","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"Creates Bernoulli distribution parameterized probs logits (). Samples binary (0 1). take value 1 probability p 0 probability 1 - p.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"","code":"distr_bernoulli(probs = NULL, logits = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"probs (numeric torch_tensor): probability sampling 1 logits (numeric torch_tensor): log-odds sampling 1 validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"","code":"if (torch_is_installed()) { m <- distr_bernoulli(0.3) m$sample() # 30% chance 1; 70% chance 0 } #> torch_tensor #>  1 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"Creates categorical distribution parameterized either probs logits ().","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"","code":"distr_categorical(probs = NULL, logits = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"probs (Tensor): event probabilities logits (Tensor): event log probabilities (unnormalized) validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"equivalent distribution torch_multinomial() samples . Samples integers \\(\\{0, \\ldots, K-1\\}\\) K probs$size(-1). probs 1-dimensional length-K, element relative probability sampling class index. probs N-dimensional, first N-1 dimensions treated batch relative probability vectors. probs argument must non-negative, finite non-zero sum, normalized sum 1 along last dimension. attr:probs return normalized value. logits argument interpreted unnormalized log probabilities can therefore real number. likewise normalized resulting probabilities sum 1 along last dimension. attr:logits return normalized value. See also: torch_multinomial()","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"","code":"if (torch_is_installed()) { m <- distr_categorical(torch_tensor(c(0.25, 0.25, 0.25, 0.25))) m$sample() # equal probability of 1,2,3,4 } #> torch_tensor #> 4 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"Creates Chi2 distribution parameterized shape parameter df. exactly equivalent distr_gamma(alpha=0.5*df, beta=0.5)","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"","code":"distr_chi2(df, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"df (float torch_tensor): shape parameter distribution validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"","code":"if (torch_is_installed()) { m <- distr_chi2(torch_tensor(1.0)) m$sample() # Chi2 distributed with shape df=1 torch_tensor(0.1046) } #> torch_tensor #>  0.1046 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"Creates Gamma distribution parameterized shape concentration rate.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"","code":"distr_gamma(concentration, rate, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"concentration (float Tensor): shape parameter distribution (often referred alpha) rate (float Tensor): rate = 1 / scale distribution (often referred beta) validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"","code":"if (torch_is_installed()) { m <- distr_gamma(torch_tensor(1.0), torch_tensor(1.0)) m$sample() # Gamma distributed with concentration=1 and rate=1 } #> torch_tensor #>  2.0188 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixture of components in the same family — distr_mixture_same_family","title":"Mixture of components in the same family — distr_mixture_same_family","text":"MixtureSameFamily distribution implements (batch ) mixture distribution component different parameterizations distribution type. parameterized Categorical selecting distribution\" (k component) component distribution, .e., Distribution rightmost batch shape (equal [k]) indexes (batch ) component.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixture of components in the same family — distr_mixture_same_family","text":"","code":"distr_mixture_same_family(   mixture_distribution,   component_distribution,   validate_args = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixture of components in the same family — distr_mixture_same_family","text":"mixture_distribution torch_distributions.Categorical-like instance. Manages probability selecting component. number categories must match rightmost batch dimension component_distribution. Must either scalar batch_shape batch_shape matching component_distribution.batch_shape[:-1] component_distribution torch_distributions.Distribution-like instance. Right-batch dimension indexes component. validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixture of components in the same family — distr_mixture_same_family","text":"","code":"if (torch_is_installed()) { # Construct Gaussian Mixture Model in 1D consisting of 5 equally # weighted normal distributions mix <- distr_categorical(torch_ones(5)) comp <- distr_normal(torch_randn(5), torch_rand(5)) gmm <- distr_mixture_same_family(mix, comp) }"},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian distribution — distr_multivariate_normal","title":"Gaussian distribution — distr_multivariate_normal","text":"Creates multivariate normal (also called Gaussian) distribution parameterized mean vector covariance matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian distribution — distr_multivariate_normal","text":"","code":"distr_multivariate_normal(   loc,   covariance_matrix = NULL,   precision_matrix = NULL,   scale_tril = NULL,   validate_args = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian distribution — distr_multivariate_normal","text":"loc (Tensor): mean distribution covariance_matrix (Tensor): positive-definite covariance matrix precision_matrix (Tensor): positive-definite precision matrix scale_tril (Tensor): lower-triangular factor covariance, positive-valued diagonal validate_args Bool wether validate arguments .","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Gaussian distribution — distr_multivariate_normal","text":"multivariate normal distribution can parameterized either terms positive definite covariance matrix \\(\\mathbf{\\Sigma}\\) positive definite precision matrix \\(\\mathbf{\\Sigma}^{-1}\\) lower-triangular matrix \\(\\mathbf{L}\\) positive-valued diagonal entries, \\(\\mathbf{\\Sigma} = \\mathbf{L}\\mathbf{L}^\\top\\). triangular matrix can obtained via e.g. Cholesky decomposition covariance.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gaussian distribution — distr_multivariate_normal","text":"one covariance_matrix precision_matrix scale_tril can specified. Using scale_tril efficient: computations internally based scale_tril. covariance_matrix precision_matrix passed instead, used compute corresponding lower triangular matrices using Cholesky decomposition.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian distribution — distr_multivariate_normal","text":"","code":"if (torch_is_installed()) { m <- distr_multivariate_normal(torch_zeros(2), torch_eye(2)) m$sample() # normally distributed with mean=`[0,0]` and covariance_matrix=`I` } #> torch_tensor #>  0.2418 #>  1.1116 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"Creates normal (also called Gaussian) distribution parameterized loc scale.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"","code":"distr_normal(loc, scale, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"loc (float Tensor): mean distribution (often referred mu) scale (float Tensor): standard deviation distribution (often referred sigma) validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"Object torch_Normal class","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"","code":"if (torch_is_installed()) { m <- distr_normal(loc = 0, scale = 1) m$sample() # normally distributed with loc=0 and scale=1 } #> torch_tensor #>  1.3422 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"Samples nonnegative integers, pmf given $$ \\mbox{rate}^{k} \\frac{e^{-\\mbox{rate}}}{k!} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"","code":"distr_poisson(rate, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"rate (numeric, torch_tensor): rate parameter validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"","code":"if (torch_is_installed()) { m <- distr_poisson(torch_tensor(4)) m$sample() } #> torch_tensor #>  5 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Enumerate an iterator — enumerate.dataloader","title":"Enumerate an iterator — enumerate.dataloader","text":"Enumerate iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enumerate an iterator — enumerate.dataloader","text":"","code":"# S3 method for dataloader enumerate(x, max_len = 1e+06, ...)"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enumerate an iterator — enumerate.dataloader","text":"x generator enumerate. max_len maximum number iterations. ... passed specific methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":null,"dir":"Reference","previous_headings":"","what":"Enumerate an iterator — enumerate","title":"Enumerate an iterator — enumerate","text":"Enumerate iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enumerate an iterator — enumerate","text":"","code":"enumerate(x, ...)"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enumerate an iterator — enumerate","text":"x generator enumerate. ... passed specific methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Torch — install_torch","title":"Install Torch — install_torch","text":"Installs Torch dependencies.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Torch — install_torch","text":"","code":"install_torch(reinstall = FALSE, ..., .inform_restart = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Torch — install_torch","text":"reinstall Re-install Torch even already installed? ... Currently unused. .inform_restart TRUE running interactive() session, installation print message inform user session must restarted torch work correctly.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Install Torch — install_torch","text":"function mainly controlled environment variables can used override defaults: TORCH_HOME: installation path. default dependencies installed within package directory. Eg given system.file(package=\"torch\"). TORCH_URL: URL, path ZIP file directory containing LibTorch version. Files installed/copied TORCH_HOME directory. LANTERN_URL: TORCH_URL Lantern library. TORCH_INSTALL_DEBUG: Setting 1, shows debug log messages installation. PRECXX11ABI: Setting 1 trigger installation Pre-cxx11 ABI installation LibTorch. can useful environments older versions GLIBC like CentOS7 older Debian/Ubuntu versions. LANTERN_BASE_URL: base URL lantern files. allows passing directory lantern binaries located. filename constructed usual. TORCH_COMMIT_SHA: torch repository commit sha used querying lantern uploads. Set 'none' avoid looking build commit use latest build branch. CUDA: try automatically detect CUDA version installed system, might want manually set . can also disable CUDA installation setting 'cpu'. TORCH_R_VERSION: R torch version. unlikely need change , can useful R package installed, want install dependencies. TORCH_INSTALL environment variable can set 0 prevent auto-installing torch TORCH_LOAD set 0 avoid loading dependencies automatically. environment variables meant advanced use cases troubleshooting . timeout error occurs library archive download, length downloaded files differ reported length, increase timeout value help.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Torch from files — get_install_libs_url","title":"Install Torch from files — get_install_libs_url","text":"List Torch Lantern libraries URLs download local files order proceed  install_torch_from_file(). Installs Torch dependencies files.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Torch from files — get_install_libs_url","text":"","code":"get_install_libs_url(version = NA, type = NA)  install_torch_from_file(version = NA, type = NA, libtorch, liblantern, ...)"},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Torch from files — get_install_libs_url","text":"version used type used. function deprecated. libtorch installation archive file use Torch. Shall \"file://\" URL scheme. liblantern installation archive file use Lantern. Shall \"file://\" URL scheme. ... parameters passed \"install_torch()\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Install Torch from files — get_install_libs_url","text":"\"install_torch()\" initiated download possible, installation archive files present local filesystem, \"install_torch_from_file()\" can used workaround installation issue. \"libtorch\" archive containing torch modules, \"liblantern\" C interface libtorch used R package. highly dependent, checked \"get_install_libs_url()\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Install Torch from files — get_install_libs_url","text":"","code":"if (torch_is_installed()) { if (FALSE) { # on a linux CPU platform  get_install_libs_url() # then after making both files available into /tmp/ Sys.setenv(TORCH_URL=\"/tmp/libtorch-v1.13.1.zip\") Sys.setenv(LANTERN_URL=\"/tmp/lantern-0.9.1.9001+cpu+arm64-Darwin.zip\") torch::install_torch() } }"},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a dataloader — is_dataloader","title":"Checks if the object is a dataloader — is_dataloader","text":"Checks object dataloader","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a dataloader — is_dataloader","text":"","code":"is_dataloader(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a dataloader — is_dataloader","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a nn_buffer — is_nn_buffer","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"Checks object nn_buffer","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"","code":"is_nn_buffer(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is an nn_module — is_nn_module","title":"Checks if the object is an nn_module — is_nn_module","text":"Checks object nn_module","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is an nn_module — is_nn_module","text":"","code":"is_nn_module(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is an nn_module — is_nn_module","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if an object is a nn_parameter — is_nn_parameter","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"Checks object nn_parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"","code":"is_nn_parameter(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a torch optimizer — is_optimizer","title":"Checks if the object is a torch optimizer — is_optimizer","text":"Checks object torch optimizer","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a torch optimizer — is_optimizer","text":"","code":"is_optimizer(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a torch optimizer — is_optimizer","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if object is a device — is_torch_device","title":"Checks if object is a device — is_torch_device","text":"Checks object device","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if object is a device — is_torch_device","text":"","code":"is_torch_device(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if object is a device — is_torch_device","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if object is a torch data type — is_torch_dtype","title":"Check if object is a torch data type — is_torch_dtype","text":"Check object torch data type","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if object is a torch data type — is_torch_dtype","text":"","code":"is_torch_dtype(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if object is a torch data type — is_torch_dtype","text":"x object check.","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a torch layout. — is_torch_layout","title":"Check if an object is a torch layout. — is_torch_layout","text":"Check object torch layout.","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a torch layout. — is_torch_layout","text":"","code":"is_torch_layout(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a torch layout. — is_torch_layout","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a memory format — is_torch_memory_format","title":"Check if an object is a memory format — is_torch_memory_format","text":"Check object memory format","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a memory format — is_torch_memory_format","text":"","code":"is_torch_memory_format(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a memory format — is_torch_memory_format","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if an object is a QScheme — is_torch_qscheme","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"Checks object QScheme","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"","code":"is_torch_qscheme(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if a tensor is undefined — is_undefined_tensor","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"Checks tensor undefined","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"","code":"is_undefined_tensor(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"x tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an iterable dataset — iterable_dataset","title":"Creates an iterable dataset — iterable_dataset","text":"Creates iterable dataset","code":""},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an iterable dataset — iterable_dataset","text":"","code":"iterable_dataset(   name,   inherit = IterableDataset,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an iterable dataset — iterable_dataset","text":"name name dataset. also used class . inherit can optionally inherit dataset creating new dataset. ... public methods dataset class private passed R6::R6Class(). active passed R6::R6Class(). parent_env environment use parent newly-created objects.","code":""},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates an iterable dataset — iterable_dataset","text":"","code":"if (torch_is_installed()) { ids <- iterable_dataset(   name = \"hello\",   initialize = function(n = 5) {     self$n <- n     self$i <- 0   },   .iter = function() {     i <- 0     function() {       i <<- i + 1       if (i > self$n) {         coro::exhausted()       } else {         i       }     }   } ) coro::collect(ids()$.iter()) } #> [[1]] #> [1] 1 #>  #> [[2]] #> [1] 2 #>  #> [[3]] #> [1] 3 #>  #> [[4]] #> [1] 4 #>  #> [[5]] #> [1] 5 #>"},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compile TorchScript code into a graph — jit_compile","title":"Compile TorchScript code into a graph — jit_compile","text":"See TorchScript language reference documentation write TorchScript code.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compile TorchScript code into a graph — jit_compile","text":"","code":"jit_compile(source)"},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compile TorchScript code into a graph — jit_compile","text":"source valid TorchScript source code.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compile TorchScript code into a graph — jit_compile","text":"","code":"if (torch_is_installed()) { comp <- jit_compile(\" def fn (x):   return torch.abs(x)  def foo (x):   return torch.sum(x)  \")  comp$fn(torch_tensor(-1)) comp$foo(torch_randn(10)) } #> torch_tensor #> -2.62729 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a script_function or script_module previously saved with jit_save — jit_load","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"Loads script_function script_module previously saved jit_save","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"","code":"jit_load(path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"path path script_function script_module serialized jit_save(). ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable idiomatic access to JIT operators from R. — jit_ops","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"Call JIT operators directly R, keeping familiar argument types argument order. Note, however, : arguments required (defaults) axis numbering (well position numbers overall) starts 0 scalars wrapped jit_scalar()","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"","code":"jit_ops"},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"object class torch_ops length 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"","code":"if (torch_is_installed()) { t1 <- torch::torch_rand(4, 5) t2 <- torch::torch_ones(5, 4) # same as torch::torch_matmul(t1, t2) jit_ops$aten$matmul(t1, t2)  # same as torch_split(torch::torch_arange(0, 3), 2, 1) jit_ops$aten$split(torch::torch_arange(0, 3), torch::jit_scalar(2L), torch::jit_scalar(0L))  } #> [[1]] #> torch_tensor #>  0 #>  1 #> [ CPUFloatType{2} ] #>  #> [[2]] #> torch_tensor #>  2 #>  3 #> [ CPUFloatType{2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a script_function to a path — jit_save","title":"Saves a script_function to a path — jit_save","text":"Saves script_function path","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a script_function to a path — jit_save","text":"","code":"jit_save(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a script_function to a path — jit_save","text":"obj script_function save path path save serialized function. ... currently unused","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a script_function to a path — jit_save","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) }  input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input)  tmp <- tempfile(\"tst\", fileext = \"pt\") jit_save(tr_fn, tmp) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"Saves script_function script_module bytecode form, loaded mobile device","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"","code":"jit_save_for_mobile(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"obj script_function script_module save path path save serialized function. ... currently unused","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) }  input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input)  tmp <- tempfile(\"tst\", fileext = \"pt\") jit_save_for_mobile(tr_fn, tmp) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds the 'jit_scalar' class to the input — jit_scalar","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"Allows disambiguating length 1 vectors scalars passing jit.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"","code":"jit_scalar(x)"},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"x length 1 R vector.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace a function and return an executable script_function. — jit_trace","title":"Trace a function and return an executable script_function. — jit_trace","text":"Using jit_trace, can turn existing R function TorchScript script_function. must provide example inputs, run function, recording operations performed tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace a function and return an executable script_function. — jit_trace","text":"","code":"jit_trace(func, ..., strict = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace a function and return an executable script_function. — jit_trace","text":"func R function run example_inputs. func arguments return values must tensors (possibly nested) lists contain tensors. Can also nn_module(), case jit_trace_module() used trace module. ... example inputs passed function tracing. resulting trace can run inputs different types shapes assuming traced operations support types shapes. example_inputs may also single Tensor case automatically wrapped list. Note ... can named, order respected. strict run tracer strict mode (default: TRUE). turn want tracer record mutable container types (currently list/dict) sure container using problem constant structure get used control flow (, ) conditions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace a function and return an executable script_function. — jit_trace","text":"script_function func function script_module func nn_module().","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trace a function and return an executable script_function. — jit_trace","text":"resulting recording standalone function produces script_function. future also support tracing nn_modules.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Trace a function and return an executable script_function. — jit_trace","text":"Scripting yet supported R.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Trace a function and return an executable script_function. — jit_trace","text":"Tracing correctly records functions modules data dependent (e.g., conditionals data tensors) untracked external dependencies (e.g., perform input/output access global variables). Tracing records operations done given function run given tensors. Therefore, returned script_function always run traced graph input. important implications module expected run different sets operations, depending input /module state. example, Tracing record control-flow like -statements loops. control-flow constant across module, fine often inlines control-flow decisions. sometimes control-flow actually part model . instance, recurrent network loop (possibly dynamic) length input sequence. returned script_function, operations different behaviors training eval modes always behave mode tracing, matter mode script_function . cases like , tracing appropriate scripting better choice. trace models, may silently get incorrect results subsequent invocations model. tracer try emit warnings something may cause incorrect trace produced.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace a function and return an executable script_function. — jit_trace","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) } input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input) tr_fn(input) } #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace a module — jit_trace_module","title":"Trace a module — jit_trace_module","text":"Trace module return executable ScriptModule optimized using just--time compilation. module passed jit_trace(), forward method run traced. jit_trace_module(), can specify named list method names example inputs trace (see inputs) argument .","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace a module — jit_trace_module","text":"","code":"jit_trace_module(mod, ..., strict = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace a module — jit_trace_module","text":"mod torch nn_module()  containing methods whose names specified inputs. given methods compiled part single ScriptModule. ... named list containing sample inputs indexed method names mod. inputs passed methods whose names correspond inputs keys tracing. list('forward'=example_forward_input, 'method2'=example_method2_input). strict run tracer strict mode (default: TRUE). turn want tracer record mutable container types (currently list/dict) sure container using problem constant structure get used control flow (, ) conditions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trace a module — jit_trace_module","text":"See jit_trace information tracing.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace a module — jit_trace_module","text":"","code":"if (torch_is_installed()) { linear <- nn_linear(10, 1) tr_linear <- jit_trace_module(linear, forward = list(torch_randn(10, 10)))  x <- torch_randn(10, 10) torch_allclose(linear(x), tr_linear(x)) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds the 'jit_tuple' class to the input — jit_tuple","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"Allows specifying output input must considered jit tuple instead list dictionary tracing.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"","code":"jit_tuple(x)"},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"x list object converted tuple.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"Letting   , Cholesky decomposition complex Hermitian real symmetric positive-definite matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"","code":"linalg_cholesky(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian positive-definite matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"=LLHL∈Kn×n = LL^{H}\\mathrlap{\\qquad L \\\\mathbb{K}^{n \\times n}} =LLHL∈Kn×n  lower triangular matrix  conjugate transpose  complex, transpose  real-valued. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"","code":"if (torch_is_installed()) { a <- torch_eye(10) linalg_cholesky(a) } #> torch_tensor #>  1  0  0  0  0  0  0  0  0  0 #>  0  1  0  0  0  0  0  0  0  0 #>  0  0  1  0  0  0  0  0  0  0 #>  0  0  0  1  0  0  0  0  0  0 #>  0  0  0  0  1  0  0  0  0  0 #>  0  0  0  0  0  1  0  0  0  0 #>  0  0  0  0  0  0  1  0  0  0 #>  0  0  0  0  0  0  0  1  0  0 #>  0  0  0  0  0  0  0  0  1  0 #>  0  0  0  0  0  0  0  0  0  1 #> [ CPUFloatType{10,10} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"function skips (slow) error checking error message construction linalg_cholesky(), instead directly returning LAPACK error codes part named tuple (L, info). makes function faster way check matrix positive-definite, provides opportunity handle decomposition errors gracefully performantly linalg_cholesky() . Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. Hermitian positive-definite matrix, batch matrices one Hermitian positive-definite matrix, info stores positive integer corresponding matrix. positive integer indicates order leading minor positive-definite, decomposition completed. info filled zeros indicates decomposition successful. check_errors=TRUE info contains positive integers, RuntimeError thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"","code":"linalg_cholesky_ex(A, check_errors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"(Tensor): Hermitian n \\times n matrix batch matrices size (*, n, n) * one batch dimensions. check_errors (bool, optional): controls whether check content infos. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"CUDA device, function may synchronize device CPU. function \"experimental\" may change future PyTorch release.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"","code":"if (torch_is_installed()) { A <- torch_randn(2, 2) out <- linalg_cholesky_ex(A) out } #> $L #> torch_tensor #> -0.8449  0.0000 #>  1.3567  1.3276 #> [ CPUFloatType{2,2} ] #>  #> $info #> torch_tensor #> 1 #> [ CPUIntType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"Letting   , condition number  matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"","code":"linalg_cond(A, p = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions p (2, -2), shape (*, n, n) every matrix invertible p ('fro', 'nuc', inf, -inf, 1, -1). p (int, inf, -inf, 'fro', 'nuc', optional): type matrix norm use computations (see ). Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"real-valued tensor, even complex.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"κ()=∥∥p∥−1∥p\\kappa() = \\|\\|_p\\|^{-1}\\|_pκ()=∥∥p​∥−1∥p​ condition number measures numerical stability linear system AX = B respect matrix norm. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. p defines matrix norm computed. See table 'Details' find supported norms. p one ('fro', 'nuc', inf, -inf, 1, -1), function uses linalg_norm() linalg_inv(). , case, matrix (every matrix batch) square invertible. p (2, -2), function can computed terms singular values κ2()=σ1σnκ−2()=σnσ1\\kappa_2() = \\frac{\\sigma_1}{\\sigma_n}\\qquad \\kappa_{-2}() = \\frac{\\sigma_n}{\\sigma_1}κ2​()=σn​σ1​​κ−2​()=σ1​σn​​ cases, computed using linalg_svd(). norms, matrix (every matrix batch) may shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"inputs CUDA device, function synchronizes device CPU p one ('fro', 'nuc', inf, -inf, 1, -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(rbind(c(1., 0, -1), c(0, 1, 0), c(1, 0, 1))) linalg_cond(a) linalg_cond(a, \"fro\") } #> torch_tensor #> 3.16228 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square matrix. — linalg_det","title":"Computes the determinant of a square matrix. — linalg_det","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square matrix. — linalg_det","text":"","code":"linalg_det(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square matrix. — linalg_det","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the determinant of a square matrix. — linalg_det","text":"","code":"if (torch_is_installed()) { a <- torch_randn(3, 3) linalg_det(a)  a <- torch_randn(3, 3, 3) linalg_det(a) } #> torch_tensor #> -0.1705 #> -0.2190 #>  0.6716 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"Letting   , eigenvalue decomposition square matrix  (exists) defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"","code":"linalg_eig(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting diagonalizable matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"list (eigenvalues, eigenvectors) corresponds   . eigenvalues eigenvectors always complex-valued, even real. eigenvectors given columns eigenvectors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"=Vdiag⁡(Λ)V−1V∈Cn×n,Λ∈Cn = V \\operatorname{diag}(\\Lambda) V^{-1}\\mathrlap{\\qquad V \\\\mathbb{C}^{n \\times n}, \\Lambda \\\\mathbb{C}^n} =Vdiag(Λ)V−1V∈Cn×n,Λ∈Cn decomposition exists  diagonalizable_. case eigenvalues different. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"eigenvalues eigenvectors real matrix may complex.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"function assumes diagonalizable_ (example, eigenvalues different). diagonalizable, returned eigenvalues correct . eigenvectors matrix unique, continuous respect . Due lack uniqueness, different hardware software may compute different eigenvectors. non-uniqueness caused fact multiplying eigenvector non-zero number produces another set valid eigenvectors matrix. implmentation, returned eigenvectors normalized norm 1 largest real component. Gradients computed using V finite repeated eigenvalues. Furthermore, distance two eigenvalues close zero, gradient numerically unstable, depends eigenvalues  computation .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) wv <- linalg_eig(a) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"Letting   , eigenvalue decomposition complex Hermitian real symmetric matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"","code":"linalg_eigh(A, UPLO = \"L\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian matrices. UPLO ('L', 'U', optional): controls whether use upper lower triangular part computations. Default: 'L'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"list (eigenvalues, eigenvectors) corresponds   . eigenvalues always real-valued, even complex. also ordered ascending order. eigenvectors dtype contain eigenvectors columns.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"=Qdiag⁡(Λ)QHQ∈Kn×n,Λ∈Rn = Q \\operatorname{diag}(\\Lambda) Q^{H}\\mathrlap{\\qquad Q \\\\mathbb{K}^{n \\times n}, \\Lambda \\\\mathbb{R}^n} =Qdiag(Λ)QHQ∈Kn×n,Λ∈Rn  conjugate transpose  complex, transpose  real-valued.  orthogonal real case unitary complex case. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. assumed Hermitian (resp. symmetric), checked internally, instead: UPLO\\ = 'L' (default), lower triangular part matrix used computation. UPLO\\ = 'U', upper triangular part matrix used. eigenvalues returned ascending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"eigenvalues real symmetric complex Hermitian matrices always real.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"eigenvectors symmetric matrix unique, continuous respect . Due lack uniqueness, different hardware software may compute different eigenvectors. non-uniqueness caused fact multiplying eigenvector -1 real case  complex case produces another set valid eigenvectors matrix. non-uniqueness problem even worse matrix repeated eigenvalues. case, one may multiply associated eigenvectors spanning subspace rotation matrix resulting eigenvectors valid eigenvectors. Gradients computed using eigenvectors tensor finite unique eigenvalues. Furthermore, distance two eigvalues close zero, gradient numerically unstable, depends eigenvalues  computation .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) linalg_eigh(a) } #> [[1]] #> torch_tensor #>  0.6851 #>  2.1951 #> [ CPUFloatType{2} ] #>  #> [[2]] #> torch_tensor #>  0.5609 -0.8279 #> -0.8279 -0.5609 #> [ CPUFloatType{2,2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues of a square matrix. — linalg_eigvals","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"Letting   , eigenvalues square matrix  defined roots (counted multiplicity) polynomial p degree n given ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"","code":"linalg_eigvals(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"p(λ)=det⁡(−λIn)λ∈C p(\\lambda) = \\operatorname{det}(- \\lambda \\mathrm{}_n)\\mathrlap{\\qquad \\lambda \\\\mathbb{C}} p(λ)=det(−λIn​)λ∈C  n-dimensional identity matrix. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"eigenvalues real matrix may complex, roots real polynomial may complex. eigenvalues matrix always well-defined, even matrix diagonalizable.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) w <- linalg_eigvals(a) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"Letting   , eigenvalues complex Hermitian real symmetric  matrix  defined roots (counted multiplicity) polynomial p degree n given ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"","code":"linalg_eigvalsh(A, UPLO = \"L\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian matrices. UPLO ('L', 'U', optional): controls whether use upper lower triangular part computations. Default: 'L'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"real-valued tensor cointaining eigenvalues even complex. eigenvalues returned ascending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"p(λ)=det⁡(−λIn)λ∈R p(\\lambda) = \\operatorname{det}(- \\lambda \\mathrm{}_n)\\mathrlap{\\qquad \\lambda \\\\mathbb{R}} p(λ)=det(−λIn​)λ∈R  n-dimensional identity matrix. eigenvalues real symmetric complex Hermitian matrix always real. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. eigenvalues returned ascending order. assumed Hermitian (resp. symmetric), checked internally, instead: UPLO\\ = 'L' (default), lower triangular part matrix used computation. UPLO\\ = 'U', upper triangular part matrix used.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) linalg_eigvalsh(a) } #> torch_tensor #> -1.1600 #>  0.5052 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"Letting   , matrix  columns   vector  , function computes first  columns matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"","code":"linalg_householder_product(A, tau)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. tau (Tensor): tensor shape (*, k) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"H1H2...HkwithHi=Im−τiviviH H_1H_2 ... H_k \\qquad \\qquad H_i = \\mathrm{}_m - \\tau_i v_i v_i^{H} H1​H2​...Hk​withHi​=Im​−τi​vi​viH​  m-dimensional identity matrix  conjugate transpose  complex, transpose  real-valued. See Representation Orthogonal Unitary Matrices details. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"function uses values strictly main diagonal . values ignored.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"","code":"if (torch_is_installed()) { A <- torch_randn(2, 2) h_tau <- torch_geqrf(A) Q <- linalg_householder_product(h_tau[[1]], h_tau[[2]]) torch_allclose(Q, linalg_qr(A)[[1]]) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square matrix if it exists. — linalg_inv","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"Throws runtime_error matrix invertible.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"","code":"linalg_inv(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting invertible matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"Letting   , matrix , inverse matrix  (exists) defined −1A=AA−1=^{-1}= AA^{-1} = \\mathrm{}_n −1A=AA−1=​  n-dimensional identity matrix. inverse matrix exists  invertible. case, inverse unique. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. Consider using linalg_solve() possible multiplying matrix left inverse, linalg_solve(, B) == $inv() %*% B always prefered use linalg_solve() possible, faster numerically stable computing inverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"","code":"if (torch_is_installed()) { A <- torch_randn(4, 4) linalg_inv(A) } #> torch_tensor #> -0.2507 -0.4972 -0.1922 -0.9363 #> -0.2006 -1.1907  3.5489  0.3504 #> -1.1944 -1.1430  3.6438 -0.8388 #> -0.4556 -0.7984  0.8755 -0.3526 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"Returns namedtuple (inverse, info). inverse contains result inverting info stores LAPACK error codes. invertible matrix, batch matrices one invertible matrix, info stores positive integer corresponding matrix. positive integer indicates diagonal element LU decomposition input matrix exactly zero. info filled zeros indicates inversion successful. check_errors=TRUE info contains positive integers, RuntimeError thrown. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"","code":"linalg_inv_ex(A, check_errors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting square matrices. check_errors (bool, optional): controls whether check content info. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"CUDA device function may synchronize device CPU. function \"experimental\" may change future PyTorch release.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) out <- linalg_inv_ex(A) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"Letting   , least squares problem linear system   defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"","code":"linalg_lstsq(A, B, rcond = NULL, ..., driver = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"(Tensor): lhs tensor shape (*, m, n) * zero batch dimensions. B (Tensor): rhs tensor shape (*, m, k) * zero batch dimensions. rcond (float, optional): used determine effective rank . rcond = NULL, rcond set machine precision dtype times max(m, n). Default: NULL. ... currently unused. driver (str, optional): name LAPACK/MAGMA method used. NULL, 'gelsy' used CPU inputs 'gels' CUDA inputs. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"list (solution, residuals, rank, singular_values).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"min⁡X∈Kn×k∥AX−B∥F \\min_{X \\\\mathbb{K}^{n \\times k}} \\|AX - B\\|_F X∈Kn×kmin​∥AX−B∥F​  denotes Frobenius norm. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions. driver chooses LAPACK/MAGMA function used. CPU inputs valid values 'gels', 'gelsy', 'gelsd, 'gelss'. CUDA input, valid driver 'gels', assumes full-rank. choose best driver CPU consider: well-conditioned (condition number large), mind precision loss. general matrix: 'gelsy' (QR pivoting) (default) full-rank: 'gels' (QR) well-conditioned. 'gelsd' (tridiagonal reduction SVD) run memory issues: 'gelss' (full SVD). See also full description drivers rcond used determine effective rank matrices driver one ('gelsy', 'gelsd', 'gelss'). case,  singular values decreasing order,  rounded zero . rcond = NULL (default), rcond set machine precision dtype . function returns solution problem extra information list four tensors (solution, residuals, rank, singular_values). inputs , B shape (*, m, n), (*, m, k) respectively, cointains solution: least squares solution. shape (*, n, k). residuals: squared residuals solutions, , . shape equal batch dimensions . computed m > n every matrix full-rank, otherwise, empty tensor. batch matrices matrix batch full rank, empty tensor returned. behavior may change future PyTorch release. rank: tensor ranks matrices . shape equal batch dimensions . computed driver one ('gelsy', 'gelsd', 'gelss'), otherwise empty tensor. singular_values: tensor singular values matrices . shape (*, min(m, n)). computed driver one ('gelsd', 'gelss'), otherwise empty tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"function computes X = $pinverse() %*% B faster numerically stable way performing computations separately.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"default value rcond may change future PyTorch release. therefore recommended use fixed value avoid potential breaking changes.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"","code":"if (torch_is_installed()) { A <- torch_tensor(rbind(c(10, 2, 3), c(3, 10, 5), c(5, 6, 12)))$unsqueeze(1) # shape (1, 3, 3) B <- torch_stack(list(   rbind(c(2, 5, 1), c(3, 2, 1), c(5, 1, 9)),   rbind(c(4, 2, 9), c(2, 0, 3), c(2, 5, 3)) ), dim = 1) # shape (2, 3, 3) X <- linalg_lstsq(A, B)$solution # A is broadcasted to shape (2, 3, 3) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a matrix norm. — linalg_matrix_norm","title":"Computes a matrix norm. — linalg_matrix_norm","text":"complex valued, computes norm $abs() Support input float, double, cfloat cdouble dtypes. Also supports batches matrices: norm computed dimensions specified 2-tuple dim dimensions treated batch dimensions. output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a matrix norm. — linalg_matrix_norm","text":"","code":"linalg_matrix_norm(   A,   ord = \"fro\",   dim = c(-2, -1),   keepdim = FALSE,   dtype = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a matrix norm. — linalg_matrix_norm","text":"(Tensor): tensor two dimensions. default shape interpreted (*, m, n) * zero batch dimensions, behavior can controlled using dim. ord (int, inf, -inf, 'fro', 'nuc', optional): order norm. Default: 'fro' dim (int, Tupleint, optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a matrix norm. — linalg_matrix_norm","text":"ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a matrix norm. — linalg_matrix_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float())$reshape(c(3, 3)) linalg_matrix_norm(a) linalg_matrix_norm(a, ord = -1) b <- a$expand(c(2, -1, -1)) linalg_matrix_norm(b) linalg_matrix_norm(b, dim = c(1, 3)) } #> torch_tensor #>   3.1623 #>  10.0000 #>  17.2627 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"","code":"linalg_matrix_power(A, n)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"(Tensor): tensor shape (*, m, m) * zero batch dimensions. n (int): exponent.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"n=0, returns identity matrix (batch) shape . n negative, returns inverse matrix (invertible) raised power abs(n).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) linalg_matrix_power(A, 0) } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the numerical rank of a matrix. — linalg_matrix_rank","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"matrix rank computed number singular values (eigenvalues absolute value hermitian = TRUE) greater specified tol threshold.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"","code":"linalg_matrix_rank(   A,   ...,   atol = NULL,   rtol = NULL,   tol = NULL,   hermitian = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. ... currently used. atol absolute tolerance value. NULL ’s considered zero. rtol relative tolerance value. See value takes NULL. tol (float, Tensor, optional): tolerance value. See value takes NULL. Default: NULL. hermitian (bool, optional): indicates whether Hermitian complex symmetric real. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. hermitian = TRUE, assumed Hermitian complex symmetric real, checked internally. Instead, just lower triangular part matrix used computations. tol specified matrix dimensions (m, n), tolerance set tol=σ1max⁡(m,n)ε tol = \\sigma_1 \\max(m, n) \\varepsilon tol=σ1​max(m,n)ε  largest singular value (eigenvalue absolute value hermitian = TRUE),  epsilon value dtype (see torch_finfo()). batch matrices, tol computed way every element batch.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"","code":"if (torch_is_installed()) { a <- torch_eye(10) linalg_matrix_rank(a) } #> torch_tensor #> 10 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Efficiently multiplies two or more matrices — linalg_multi_dot","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"Efficiently multiplies two matrices reordering multiplications fewest arithmetic operations performed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"","code":"linalg_multi_dot(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"tensors (Sequence[Tensor]): two tensors multiply. first last tensors may 1D 2D. Every tensor must 2D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"Supports inputs float, double, cfloat cdouble dtypes. function support batched inputs. Every tensor tensors must 2D, except first last may 1D. first tensor 1D vector shape (n,) treated row vector shape (1, n), similarly last tensor 1D vector shape (n,) treated column vector shape (n, 1). first last tensors matrices, output matrix. However, either 1D vector, output 1D vector.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"function implemented chaining torch_mm() calls computing optimal matrix multiplication order. cost multiplying two matrices shapes (, b) (b, c) * b * c. Given matrices , B, C shapes (10, 100), (100, 5), (5, 50) respectively, can calculate cost different multiplication orders follows: cost⁡((AB)C)=10×100×5+10×5×50=7500 cost⁡((BC))=10×100×50+100×5×50=75000 \\begin{align*} \\operatorname{cost}((AB)C) &= 10 \\times 100 \\times 5 + 10 \\times 5 \\times 50 = 7500 \\ \\operatorname{cost}((BC)) &= 10 \\times 100 \\times 50 + 100 \\times 5 \\times 50 = 75000 \\end{align*} cost((AB)C)​=10×100×5+10×5×50=7500 cost((BC))​=10×100×50+100×5×50=75000​ case, multiplying B first followed C 10 times faster.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"","code":"if (torch_is_installed()) {  linalg_multi_dot(list(torch_tensor(c(1, 2)), torch_tensor(c(2, 3)))) } #> torch_tensor #> 8 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a vector or matrix norm. — linalg_norm","title":"Computes a vector or matrix norm. — linalg_norm","text":"complex valued, computes norm $abs() Supports input float, double, cfloat cdouble dtypes. Whether function computes vector matrix norm determined follows:","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a vector or matrix norm. — linalg_norm","text":"","code":"linalg_norm(A, ord = NULL, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a vector or matrix norm. — linalg_norm","text":"(Tensor): tensor shape (*, n) (*, m, n) * zero batch dimensions ord (int, float, inf, -inf, 'fro', 'nuc', optional): order norm. Default: NULL dim (int, Tupleint, optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a vector or matrix norm. — linalg_norm","text":"dim int, vector norm computed. dim 2-tuple, matrix norm computed. dim=NULL ord=NULL, flattened 1D 2-norm resulting vector computed. dim=NULL ord!=NULL, must 1D 2D. ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a vector or matrix norm. — linalg_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float()) - 4 a b <- a$reshape(c(3, 3)) b  linalg_norm(a) linalg_norm(b) } #> torch_tensor #> 7.74597 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"pseudoinverse may defined algebraically_ computationally convenient understand SVD_ Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"","code":"linalg_pinv(A, rcond = NULL, hermitian = FALSE, atol = NULL, rtol = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. rcond (float Tensor, optional): tolerance value determine singular value zero torch_Tensor, shape must broadcastable singular values returned linalg_svd(). Alias rtol. Default: 0. hermitian (bool, optional): indicates whether Hermitian complex symmetric real. Default: FALSE. atol absolute tolerance value. NULL ’s considered zero. rtol relative tolerance value. See value takes NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"hermitian= TRUE, assumed Hermitian complex symmetric real, checked internally. Instead, just lower triangular part matrix used computations. singular values (norm eigenvalues hermitian= TRUE) specified rcond threshold treated zero discarded computation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"function uses linalg_svd() hermitian= FALSE linalg_eigh() hermitian= TRUE. CUDA inputs, function synchronizes device CPU. Consider using linalg_lstsq() possible multiplying matrix left pseudoinverse, linalg_lstsq(, B)$solution == $pinv() %*% B always prefered use linalg_lstsq() possible, faster numerically stable computing pseudoinverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 5) linalg_pinv(A) } #> torch_tensor #>  0.6886  0.2109  0.0239 #>  0.0302  0.1808  0.1159 #>  0.3785  0.2301 -0.3357 #> -0.4896 -0.6582  0.2559 #> -0.1375 -0.4979  0.0205 #> [ CPUFloatType{5,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a matrix. — linalg_qr","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"Letting   , full QR decomposition matrix  defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"","code":"linalg_qr(A, mode = \"reduced\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. mode (str, optional): one 'reduced', 'complete', 'r'. Controls shape returned tensors. Default: 'reduced'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"list (Q, R).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"=QRQ∈Km×m,R∈Km×n = QR\\mathrlap{\\qquad Q \\\\mathbb{K}^{m \\times m}, R \\\\mathbb{K}^{m \\times n}} =QRQ∈Km×m,R∈Km×n  orthogonal real case unitary complex case,  upper triangular. m > n (tall matrix), R upper triangular, last m - n rows zero. case, can drop last m - n columns Q form reduced QR decomposition: =QRQ∈Km×n,R∈Kn×n = QR\\mathrlap{\\qquad Q \\\\mathbb{K}^{m \\times n}, R \\\\mathbb{K}^{n \\times n}} =QRQ∈Km×n,R∈Kn×n reduced QR decomposition agrees full QR decomposition n >= m (wide matrix). Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. parameter mode chooses full reduced QR decomposition. shape (*, m, n), denoting k = min(m, n) mode = 'reduced' (default): Returns (Q, R) shapes (*, m, k), (*, k, n) respectively. mode = 'complete': Returns (Q, R) shapes (*, m, m), (*, m, n) respectively. mode = 'r': Computes reduced R. Returns (Q, R) Q empty R shape (*, k, n).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(rbind(c(12., -51, 4), c(6, 167, -68), c(-4, 24, -41))) qr <- linalg_qr(a)  torch_mm(qr[[1]], qr[[2]])$round() torch_mm(qr[[1]]$t(), qr[[1]])$round() } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"complex , returns angle natural logarithm modulus determinant, , logarithmic polar decomposition determinant. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"","code":"linalg_slogdet(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"list (sign, logabsdet). logabsdet always real-valued, even complex. sign dtype .","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"determinant can recovered sign * exp(logabsdet). matrix determinant zero, returns (0, -Inf).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"","code":"if (torch_is_installed()) { a <- torch_randn(3, 3) linalg_slogdet(a) } #> [[1]] #> torch_tensor #> -1 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 0.643368 #> [ CPUFloatType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"Letting   , function computes solution  linear system associated , defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"","code":"linalg_solve(A, B)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions. B (Tensor): right-hand side tensor shape (*, n)  (*, n, k) (n,) (n, k) according rules described ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"$$   AX = B $$ system linear equations one solution  invertible_. function assumes  invertible. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions. Letting * zero batch dimensions, shape (*, n, n) B shape (*, n) (batch vectors) shape (*, n, k) (batch matrices \"multiple right-hand sides\"), function returns X shape (*, n) (*, n, k) respectively. Otherwise, shape (*, n, n)  B shape (n,)  (n, k), B broadcasted shape (*, n) (*, n, k) respectively. function returns solution resulting batch systems linear equations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"function computes X = $inverse() @ B faster numerically stable way performing computations separately.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) b <- torch_randn(3) x <- linalg_solve(A, b) torch_allclose(torch_matmul(A, x), b) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular solve — linalg_solve_triangular","title":"Triangular solve — linalg_solve_triangular","text":"Triangular solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular solve — linalg_solve_triangular","text":"","code":"linalg_solve_triangular(A, B, ..., upper, left = TRUE, unitriangular = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular solve — linalg_solve_triangular","text":"tensor shape (*, n, n) (*, k, k) left=TRUE) * zero batch dimensions. B right-hand side tensor shape (*, n, k) ... Currently ignored. upper whether upper lower triangular matrix. left wheter solve system AX=B XA=B unitriangular TRUE, diagonal elements assumed equal 1.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"Letting   , full SVD matrix , k = min(m,n), defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"","code":"linalg_svd(A, full_matrices = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. full_matrices (bool, optional): controls whether compute full reduced SVD, consequently, shape returned tensors U V. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"list (U, S, V) corresponds , ,  . S always real-valued, even complex. also ordered descending order. U V dtype . left / right singular vectors given columns U rows V respectively.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"=Udiag⁡(S)VHU∈Km×m,S∈Rk,V∈Kn×n = U \\operatorname{diag}(S) V^{H} \\mathrlap{\\qquad U \\\\mathbb{K}^{m \\times m}, S \\\\mathbb{R}^k, V \\\\mathbb{K}^{n \\times n}} =Udiag(S)VHU∈Km×m,S∈Rk,V∈Kn×n ,  conjugate transpose  complex, transpose  real-valued. matrices  ,  (thus ) orthogonal real case, unitary complex case. m > n (resp. m < n) can drop last m - n (resp. n - m) columns U (resp. V) form reduced SVD: =Udiag⁡(S)VHU∈Km×k,S∈Rk,V∈Kk×n = U \\operatorname{diag}(S) V^{H} \\mathrlap{\\qquad U \\\\mathbb{K}^{m \\times k}, S \\\\mathbb{R}^k, V \\\\mathbb{K}^{k \\times n}} =Udiag(S)VHU∈Km×k,S∈Rk,V∈Kk×n . case,   also orthonormal columns. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. returned decomposition named tuple (U, S, V) corresponds , ,  . singular values returned descending order. parameter full_matrices chooses full (default) reduced SVD.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"full_matrices=TRUE, gradients respect U[..., :, min(m, n):] Vh[..., min(m, n):, :] ignored, vectors can arbitrary bases corresponding subspaces.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"warnings","dir":"Reference","previous_headings":"","what":"Warnings","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"returned tensors U V unique, continuous respect . Due lack uniqueness, different hardware software may compute different singular vectors. non-uniqueness caused fact multiplying pair singular vectors  -1 real case  complex case produces another two valid singular vectors matrix. non-uniqueness problem even worse matrix repeated singular values. case, one may multiply associated singular vectors U V spanning subspace rotation matrix resulting vectors span subspace. Gradients computed using U V finite zero singular value repeated singular values. Furthermore, distance two singular values close zero, gradient numerically unstable, depends singular values  computation . gradient also numerically unstable small singular values, also depends computaiton .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(5, 3) linalg_svd(a, full_matrices = FALSE) } #> [[1]] #> torch_tensor #> -0.0445  0.3368 -0.7746 #> -0.8789 -0.1809  0.1178 #> -0.0135  0.8168  0.1631 #> -0.4666  0.3359 -0.0405 #>  0.0876  0.2717  0.5983 #> [ CPUFloatType{5,3} ] #>  #> [[2]] #> torch_tensor #>  2.7608 #>  1.9651 #>  0.9611 #> [ CPUFloatType{3} ] #>  #> [[3]] #> torch_tensor #> -0.0630 -0.9964  0.0561 #>  0.9145 -0.0351  0.4031 #>  0.3997 -0.0767 -0.9134 #> [ CPUFloatType{3,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular values of a matrix. — linalg_svdvals","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. singular values returned descending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"","code":"linalg_svdvals(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"real-valued tensor, even complex.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"","code":"if (torch_is_installed()) { A <- torch_randn(5, 3) S <- linalg_svdvals(A) S } #> torch_tensor #>  3.1020 #>  1.3424 #>  0.9048 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"m product first ind dimensions n product rest dimensions, function expects m n equal. case, computes tensor X tensordot(, X, ind) identity matrix dimension m.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"","code":"linalg_tensorinv(A, ind = 3L)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"(Tensor): tensor invert. ind (int): index compute inverse torch_tensordot(). Default: 3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"Supports input float, double, cfloat cdouble dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"Consider using linalg_tensorsolve() possible multiplying tensor left tensor inverse linalg_tensorsolve(, B) == torch_tensordot(linalg_tensorinv(), B)) always prefered use linalg_tensorsolve() possible, faster numerically stable computing pseudoinverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"","code":"if (torch_is_installed()) { A <- torch_eye(4 * 6)$reshape(c(4, 6, 8, 3)) Ainv <- linalg_tensorinv(A, ind = 3) Ainv$shape B <- torch_randn(4, 6) torch_allclose(torch_tensordot(Ainv, B), linalg_tensorsolve(A, B))  A <- torch_randn(4, 4) Atensorinv <- linalg_tensorinv(A, 2) Ainv <- linalg_inv(A) torch_allclose(Atensorinv, Ainv) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"m product first B\\ .ndim  dimensions n product rest dimensions, function expects m n equal. returned tensor x satisfies tensordot(, x, dims=x$ndim) == B.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"","code":"linalg_tensorsolve(A, B, dims = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"(Tensor): tensor solve . B (Tensor): solution dims (Tupleint, optional): dimensions moved. NULL, dimensions moved. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"dims specified, reshaped = movedim(, dims, seq(len(dims) - $ndim + 1, 0)) Supports inputs float, double, cfloat cdouble dtypes.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"","code":"if (torch_is_installed()) { A <- torch_eye(2 * 3 * 4)$reshape(c(2 * 3, 4, 2, 3, 4)) B <- torch_randn(2 * 3, 4) X <- linalg_tensorsolve(A, B) X$shape torch_allclose(torch_tensordot(A, X, dims = X$ndim), B)  A <- torch_randn(6, 4, 4, 3, 2) B <- torch_randn(4, 3, 2) X <- linalg_tensorsolve(A, B, dims = c(1, 3)) A <- A$permute(c(2, 4, 5, 1, 3)) torch_allclose(torch_tensordot(A, X, dims = X$ndim), B, atol = 1e-6) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a vector norm. — linalg_vector_norm","title":"Computes a vector norm. — linalg_vector_norm","text":"complex valued, computes norm $abs() Supports input float, double, cfloat cdouble dtypes. function necessarily treat multidimensonal batch vectors, instead:","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a vector norm. — linalg_vector_norm","text":"","code":"linalg_vector_norm(A, ord = 2, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a vector norm. — linalg_vector_norm","text":"(Tensor): tensor, flattened default, behavior can controlled using dim. ord (int, float, inf, -inf, 'fro', 'nuc', optional): order norm. Default: 2 dim (int, Tupleint, optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a vector norm. — linalg_vector_norm","text":"dim=NULL, flattened norm computed. dim int tuple, norm computed dimensions dimensions treated batch dimensions. behavior consistency linalg_norm(). ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a vector norm. — linalg_vector_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float()) - 4 a b <- a$reshape(c(3, 3)) b  linalg_vector_norm(a, ord = 3.5) linalg_vector_norm(b, ord = 3.5) } #> torch_tensor #> 5.43449 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":null,"dir":"Reference","previous_headings":"","what":"Load a state dict file — load_state_dict","title":"Load a state dict file — load_state_dict","text":"function used load models saved python. work correctly need use torch.save flag: _use_new_zipfile_serialization=True also remove nn.Parameter classes tensors dict.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load a state dict file — load_state_dict","text":"","code":"load_state_dict(path, ..., legacy_stream = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load a state dict file — load_state_dict","text":"path state dict file ... additional arguments currently used. legacy_stream TRUE state dict loaded using legacy way handling streams.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load a state dict file — load_state_dict","text":"named list tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load a state dict file — load_state_dict","text":"might change development pytorch's C++ api.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":null,"dir":"Reference","previous_headings":"","what":"Autocast context manager — local_autocast","title":"Autocast context manager — local_autocast","text":"Allow regions code run mixed precision. regions, ops run op-specific dtype chosen autocast improve performance maintaining accuracy.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autocast context manager — local_autocast","text":"","code":"local_autocast(   device_type,   dtype = NULL,   enabled = TRUE,   cache_enabled = NULL,   ...,   .env = parent.frame() )  with_autocast(   code,   ...,   device_type,   dtype = NULL,   enabled = TRUE,   cache_enabled = NULL )  set_autocast(device_type, dtype = NULL, enabled = TRUE, cache_enabled = NULL)  unset_autocast(context)"},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autocast context manager — local_autocast","text":"device_type character string indicating whether use 'cuda' 'cpu' device dtype torch data type indicating whether use torch_float16() torch_bfloat16(). enabled logical value indicating whether autocasting enabled region. Default: TRUE cache_enabled logical value indicating whether weight cache inside autocast enabled. ... currently unused. .env environment use scoping. code code executed gradient recording. context Returned set_autocast passed unsetting .","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Autocast context manager — local_autocast","text":"entering autocast-enabled region, Tensors may type. call half() bfloat16() model(s) inputs using autocasting. autocast enabled forward pass(es) network, including loss computation(s).  Backward passes autocast recommended. Backward ops run type autocast used corresponding forward ops.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Autocast context manager — local_autocast","text":"with_autocast(): context automatic mixed precision. set_autocast(): Set autocast context. advanced users . unset_autocast(): Unset autocast context.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autocast context manager — local_autocast","text":"","code":"if (torch_is_installed()) { x <- torch_randn(5, 5, dtype = torch_float32()) y <- torch_randn(5, 5, dtype = torch_float32())  foo <- function(x, y) {   local_autocast(device = \"cpu\")   z <- torch_mm(x, y)   w <- torch_mm(z, x)   w }  out <- foo(x, y) }"},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Device contexts — local_device","title":"Device contexts — local_device","text":"Device contexts","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Device contexts — local_device","text":"","code":"local_device(device, ..., .env = parent.frame())  with_device(code, ..., device)"},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Device contexts — local_device","text":"device torch device used default creating new tensors. ... currently unused. .env environment use scoping. code code evaluated modified environment.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Device contexts — local_device","text":"with_device(): Modifies default device selected context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_cosine_annealing.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","title":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","text":"Set learning rate parameter group using cosine annealing schedule","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_cosine_annealing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","text":"","code":"lr_cosine_annealing(   optimizer,   T_max,   eta_min = 0,   last_epoch = -1,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_cosine_annealing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","text":"optimizer (Optimizer): Wrapped optimizer. T_max Maximum number iterations eta_min Minimum learning rate. Default: 0. last_epoch index last epoch verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"Sets learning rate parameter group initial lr times given function. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"","code":"lr_lambda(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"optimizer (Optimizer): Wrapped optimizer. lr_lambda (function list): function computes multiplicative factor given integer parameter epoch, list functions, one group optimizer.param_groups. last_epoch (int): index last epoch. Default: -1. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"","code":"if (torch_is_installed()) { # Assuming optimizer has two groups. lambda1 <- function(epoch) epoch %/% 30 lambda2 <- function(epoch) 0.95^epoch if (FALSE) { scheduler <- lr_lambda(optimizer, lr_lambda = list(lambda1, lambda2)) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"Multiply learning rate parameter group factor given specified function. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"","code":"lr_multiplicative(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"optimizer (Optimizer): Wrapped optimizer. lr_lambda (function list): function computes multiplicative factor given integer parameter epoch, list functions, one group optimizer.param_groups. last_epoch (int): index last epoch. Default: -1. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"","code":"if (torch_is_installed()) { if (FALSE) { lmbda <- function(epoch) 0.95 scheduler <- lr_multiplicative(optimizer, lr_lambda = lmbda) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":null,"dir":"Reference","previous_headings":"","what":"Once cycle learning rate — lr_one_cycle","title":"Once cycle learning rate — lr_one_cycle","text":"Sets learning rate parameter group according 1cycle learning rate policy. 1cycle policy anneals learning rate initial learning rate maximum learning rate maximum learning rate minimum learning rate much lower initial learning rate.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Once cycle learning rate — lr_one_cycle","text":"","code":"lr_one_cycle(   optimizer,   max_lr,   total_steps = NULL,   epochs = NULL,   steps_per_epoch = NULL,   pct_start = 0.3,   anneal_strategy = \"cos\",   cycle_momentum = TRUE,   base_momentum = 0.85,   max_momentum = 0.95,   div_factor = 25,   final_div_factor = 10000,   last_epoch = -1,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Once cycle learning rate — lr_one_cycle","text":"optimizer (Optimizer): Wrapped optimizer. max_lr (float list): Upper learning rate boundaries cycle parameter group. total_steps (int): total number steps cycle. Note value provided , must inferred providing value epochs steps_per_epoch. Default: NULL epochs (int): number epochs train . used along steps_per_epoch order infer total number steps cycle value total_steps provided. Default: NULL steps_per_epoch (int): number steps per epoch train . used along epochs order infer total number steps cycle value total_steps provided. Default: NULL pct_start (float): percentage cycle (number steps) spent increasing learning rate. Default: 0.3 anneal_strategy (str): {'cos', 'linear'} Specifies annealing strategy: \"cos\" cosine annealing, \"linear\" linear annealing. Default: 'cos' cycle_momentum (bool): TRUE, momentum cycled inversely learning rate 'base_momentum' 'max_momentum'. Default: TRUE base_momentum (float list): Lower momentum boundaries cycle parameter group. Note momentum cycled inversely learning rate; peak cycle, momentum 'base_momentum' learning rate 'max_lr'. Default: 0.85 max_momentum (float list): Upper momentum boundaries cycle parameter group. Functionally, defines cycle amplitude (max_momentum - base_momentum). Note momentum cycled inversely learning rate; start cycle, momentum 'max_momentum' learning rate 'base_lr' Default: 0.95 div_factor (float): Determines initial learning rate via initial_lr = max_lr/div_factor Default: 25 final_div_factor (float): Determines minimum learning rate via min_lr = initial_lr/final_div_factor Default: 1e4 last_epoch (int): index last batch. parameter used resuming training job. Since step() invoked batch instead epoch, number represents total number batches computed, total number epochs computed. last_epoch=-1, schedule started beginning. Default: -1 verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Once cycle learning rate — lr_one_cycle","text":"policy initially described paper Super-Convergence: Fast Training Neural Networks Using Large Learning Rates. 1cycle learning rate policy changes learning rate every batch. step called batch used training. scheduler chainable. Note also total number steps cycle can determined one two ways (listed order precedence): value total_steps explicitly provided. number epochs (epochs) number steps per epoch (steps_per_epoch) provided. case, number total steps inferred total_steps = epochs * steps_per_epoch must either provide value total_steps provide value epochs steps_per_epoch.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Once cycle learning rate — lr_one_cycle","text":"","code":"if (torch_is_installed()) { if (FALSE) { data_loader <- dataloader(...) optimizer <- optim_sgd(model$parameters, lr = 0.1, momentum = 0.9) scheduler <- lr_one_cycle(optimizer,   max_lr = 0.01, steps_per_epoch = length(data_loader),   epochs = 10 )  for (i in 1:epochs) {   coro::loop(for (batch in data_loader) {     train_batch(...)     scheduler$step()   }) } }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce learning rate on plateau — lr_reduce_on_plateau","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"Reduce learning rate metric stopped improving. Models often benefit reducing learning rate factor 2-10 learning stagnates. scheduler reads metrics quantity improvement seen 'patience' number epochs, learning rate reduced.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"","code":"lr_reduce_on_plateau(   optimizer,   mode = \"min\",   factor = 0.1,   patience = 10,   threshold = 1e-04,   threshold_mode = \"rel\",   cooldown = 0,   min_lr = 0,   eps = 1e-08,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"optimizer (Optimizer): Wrapped optimizer. mode (str): One min, max. min mode, lr reduced quantity monitored stopped decreasing; max mode reduced quantity monitored stopped increasing. Default: 'min'. factor (float): Factor learning rate reduced. new_lr <- lr * factor. Default: 0.1. patience (int): Number epochs improvement learning rate reduced. example, patience = 2, ignore first 2 epochs improvement, decrease LR 3rd epoch loss still improved . Default: 10. threshold (float):Threshold measuring new optimum, focus significant changes. Default: 1e-4. threshold_mode (str): One rel, abs. rel mode, dynamic_threshold <- best * ( 1 + threshold ) 'max' mode best * ( 1 - threshold ) min mode. abs mode, dynamic_threshold <- best + threshold max mode best - threshold min mode. Default: 'rel'. cooldown (int): Number epochs wait resuming normal operation lr reduced. Default: 0. min_lr (float list): scalar list scalars. lower bound learning rate param groups group respectively. Default: 0. eps (float): Minimal decay applied lr. difference new old lr smaller eps, update ignored. Default: 1e-8. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"","code":"if (torch_is_installed()) { if (FALSE) {  optimizer <- optim_sgd(model$parameters(), lr=0.1, momentum=0.9) scheduler <- lr_reduce_on_plateau(optimizer, 'min') for (epoch in 1:10) {  train(...)  val_loss <- validate(...)  # note that step should be called after validate  scheduler$step(val_loss) } } }"},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates learning rate schedulers — lr_scheduler","title":"Creates learning rate schedulers — lr_scheduler","text":"Creates learning rate schedulers","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates learning rate schedulers — lr_scheduler","text":"","code":"lr_scheduler(   classname = NULL,   inherit = LRScheduler,   ...,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates learning rate schedulers — lr_scheduler","text":"classname optional name learning rate scheduler inherit optional learning rate scheduler inherit ... named list methods. must implement get_lr() method take argument returns learning rates param_group optimizer. parent_env passed R6::R6Class().","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Step learning rate decay — lr_step","title":"Step learning rate decay — lr_step","text":"Decays learning rate parameter group gamma every step_size epochs. Notice decay can happen simultaneously changes learning rate outside scheduler. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Step learning rate decay — lr_step","text":"","code":"lr_step(optimizer, step_size, gamma = 0.1, last_epoch = -1)"},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Step learning rate decay — lr_step","text":"optimizer (Optimizer): Wrapped optimizer. step_size (int): Period learning rate decay. gamma (float): Multiplicative factor learning rate decay. Default: 0.1. last_epoch (int): index last epoch. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Step learning rate decay — lr_step","text":"","code":"if (torch_is_installed()) { if (FALSE) { # Assuming optimizer uses lr = 0.05 for all groups # lr = 0.05     if epoch < 30 # lr = 0.005    if 30 <= epoch < 60 # lr = 0.0005   if 60 <= epoch < 90 # ... scheduler <- lr_step(optimizer, step_size = 30, gamma = 0.1) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"output size H, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"","code":"nn_adaptive_avg_pool1d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"output_size target output size H","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"","code":"if (torch_is_installed()) { # target output size of 5 m <- nn_adaptive_avg_pool1d(5) input <- torch_randn(1, 64, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"output size H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"","code":"nn_adaptive_avg_pool2d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"output_size target output size image form H x W. Can tuple (H, W) single H square image H x H. H W can either int, NULL means size input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7 m <- nn_adaptive_avg_pool2d(c(5, 7)) input <- torch_randn(1, 64, 8, 9) output <- m(input) # target output size of 7x7 (square) m <- nn_adaptive_avg_pool2d(7) input <- torch_randn(1, 64, 10, 9) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"output size D x H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"","code":"nn_adaptive_avg_pool3d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"output_size target output size form D x H x W. Can tuple (D, H, W) single number D cube D x D x D. D, H W can either int, None means size input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7x9 m <- nn_adaptive_avg_pool3d(c(5, 7, 9)) input <- torch_randn(1, 64, 8, 9, 10) output <- m(input) # target output size of 7x7x7 (cube) m <- nn_adaptive_avg_pool3d(7) input <- torch_randn(1, 64, 10, 9, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Efficient softmax approximation described Efficient softmax approximation GPUs Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"","code":"nn_adaptive_log_softmax_with_loss(   in_features,   n_classes,   cutoffs,   div_value = 4,   head_bias = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"in_features (int): Number features input tensor n_classes (int): Number classes dataset cutoffs (Sequence): Cutoffs used assign targets buckets div_value (float, optional): value used exponent compute sizes clusters. Default: 4.0 head_bias (bool, optional): True, adds bias term 'head' adaptive softmax. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"NamedTuple output loss fields: output Tensor size N containing computed target log probabilities example loss Scalar representing computed negative log likelihood loss","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Adaptive softmax approximate strategy training models large output spaces. effective label distribution highly imbalanced, example natural language modelling, word frequency distribution approximately follows Zipf's law. Adaptive softmax partitions labels several clusters, according frequency. clusters may contain different number targets . Additionally, clusters containing less frequent labels assign lower dimensional embeddings labels, speeds computation. minibatch, clusters least one target present evaluated. idea clusters accessed frequently (like first one, containing frequent labels), also cheap compute -- , contain small number assigned labels. highly recommend taking look original paper details. cutoffs ordered Sequence integers sorted increasing order. controls number clusters partitioning targets clusters. example setting cutoffs = c(10, 100, 1000) means first 10 targets assigned 'head' adaptive softmax, targets 11, 12, ..., 100 assigned first cluster, targets 101, 102, ..., 1000 assigned second cluster, targets 1001, 1002, ..., n_classes - 1 assigned last, third cluster. div_value used compute size additional cluster, given \\(\\left\\lfloor\\frac{\\mbox{\\_features}}{\\mbox{div\\_value}^{idx}}\\right\\rfloor\\), \\(idx\\) cluster index (clusters less frequent words larger indices, indices starting \\(1\\)). head_bias set True, adds bias term 'head' adaptive softmax. See paper details. Set False official implementation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"module returns NamedTuple output loss fields. See documentation details. compute log-probabilities classes, log_prob method can used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Labels passed inputs module sorted according frequency. means frequent label represented index 0, least frequent label represented index n_classes - 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"input: \\((N, \\mbox{\\_features})\\) target: \\((N)\\) value satisfies \\(0 <= \\mbox{target[]} <= \\mbox{n\\_classes}\\) output1: \\((N)\\) output2: Scalar","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"output size H, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"","code":"nn_adaptive_max_pool1d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"output_size target output size H return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool1d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"","code":"if (torch_is_installed()) { # target output size of 5 m <- nn_adaptive_max_pool1d(5) input <- torch_randn(1, 64, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"output size H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"","code":"nn_adaptive_max_pool2d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"output_size target output size image form H x W. Can tuple (H, W) single H square image H x H. H W can either int, None means size input. return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool2d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7 m <- nn_adaptive_max_pool2d(c(5, 7)) input <- torch_randn(1, 64, 8, 9) output <- m(input) # target output size of 7x7 (square) m <- nn_adaptive_max_pool2d(7) input <- torch_randn(1, 64, 10, 9) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"output size D x H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"","code":"nn_adaptive_max_pool3d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"output_size target output size image form D x H x W. Can tuple (D, H, W) single D cube D x D x D. D, H W can either int, None means size input. return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool3d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7x9 m <- nn_adaptive_max_pool3d(c(5, 7, 9)) input <- torch_randn(1, 64, 8, 9, 10) output <- m(input) # target output size of 7x7x7 (cube) m <- nn_adaptive_max_pool3d(7) input <- torch_randn(1, 64, 10, 9, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"simplest case, output value layer input size \\((N, C, L)\\), output \\((N, C, L_{})\\) kernel_size \\(k\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"","code":"nn_avg_pool1d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"$$   \\mbox{}(N_i, C_j, l) = \\frac{1}{k} \\sum_{m=0}^{k-1} \\mbox{input}(N_i, C_j, \\mbox{stride} \\times l + m) $$ padding non-zero, input implicitly zero-padded sides padding number points. parameters kernel_size, stride, padding can int one-element tuple.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor \\frac{L_{} +       2 \\times \\mbox{padding} - \\mbox{kernel\\_size}}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"","code":"if (torch_is_installed()) {  # pool with window of size=3, stride=2 m <- nn_avg_pool1d(3, stride = 2) m(torch_randn(1, 1, 8)) } #> torch_tensor #> (1,.,.) =  #>   0.4693 -0.1130 -0.5694 #> [ CPUFloatType{1,1,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"simplest case, output value layer input size \\((N, C, H, W)\\), output \\((N, C, H_{}, W_{})\\) kernel_size \\((kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"","code":"nn_avg_pool2d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation divisor_override specified, used divisor, otherwise kernel_size used","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"$$   (N_i, C_j, h, w)  = \\frac{1}{kH * kW} \\sum_{m=0}^{kH-1} \\sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \\times h + m, stride[1] \\times w + n) $$ padding non-zero, input implicitly zero-padded sides padding number points. parameters kernel_size, stride, padding can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{}  + 2 \\times \\mbox{padding}[0] -       \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{}  + 2 \\times \\mbox{padding}[1] -       \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 m <- nn_avg_pool2d(3, stride = 2) # pool of non-square window m <- nn_avg_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"simplest case, output value layer input size \\((N, C, D, H, W)\\), output \\((N, C, D_{}, H_{}, W_{})\\) kernel_size \\((kD, kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"","code":"nn_avg_pool3d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added three sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation divisor_override specified, used divisor, otherwise kernel_size used","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"$$ \\begin{array}{ll} \\mbox{}(N_i, C_j, d, h, w) = & \\sum_{k=0}^{kD-1} \\sum_{m=0}^{kH-1} \\sum_{n=0}^{kW-1} \\\\ & \\frac{\\mbox{input}(N_i, C_j, \\mbox{stride}[0] \\times d + k, \\mbox{stride}[1] \\times h + m, \\mbox{stride}[2] \\times w + n)}{kD \\times kH \\times kW} \\end{array} $$ padding non-zero, input implicitly zero-padded three sides padding number points. parameters kernel_size, stride can either : single int -- case value used depth, height width dimension tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] -       \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] -       \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] -       \\mbox{kernel\\_size}[2]}{\\mbox{stride}[2]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 m <- nn_avg_pool3d(3, stride = 2) # pool of non-square window m <- nn_avg_pool3d(c(3, 2, 2), stride = c(2, 1, 2)) input <- torch_randn(20, 16, 50, 44, 31) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm1D module — nn_batch_norm1d","title":"BatchNorm1D module — nn_batch_norm1d","text":"Applies Batch Normalization 2D 3D input (mini-batch 1D inputs optional additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm1D module — nn_batch_norm1d","text":"","code":"nn_batch_norm1d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm1D module — nn_batch_norm1d","text":"num_features \\(C\\) expected input size \\((N, C, L)\\) \\(L\\) input size \\((N, L)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set NULL cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics always uses batch statistics training eval modes. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm1D module — nn_batch_norm1d","text":"$$ y = \\frac{x - \\mathrm{E}[x]}{\\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default :attr:momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm1D module — nn_batch_norm1d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, L) slices, common terminology call Temporal Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm1D module — nn_batch_norm1d","text":"Input: \\((N, C)\\) \\((N, C, L)\\) Output: \\((N, C)\\) \\((N, C, L)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm1D module — nn_batch_norm1d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm1d(100) # Without Learnable Parameters m <- nn_batch_norm1d(100, affine = FALSE) input <- torch_randn(20, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm2D — nn_batch_norm2d","title":"BatchNorm2D — nn_batch_norm2d","text":"Applies Batch Normalization 4D input (mini-batch 2D inputs additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm2D — nn_batch_norm2d","text":"","code":"nn_batch_norm2d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm2D — nn_batch_norm2d","text":"num_features \\(C\\) expected input size \\((N, C, H, W)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics uses batch statistics instead training eval modes running mean variance None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm2D — nn_batch_norm2d","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE). Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm2D — nn_batch_norm2d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, H, W) slices, common terminology call Spatial Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm2D — nn_batch_norm2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm2D — nn_batch_norm2d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm2d(100) # Without Learnable Parameters m <- nn_batch_norm2d(100, affine = FALSE) input <- torch_randn(20, 100, 35, 45) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm3D — nn_batch_norm3d","title":"BatchNorm3D — nn_batch_norm3d","text":"Applies Batch Normalization 5D input (mini-batch 3D inputs additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm3D — nn_batch_norm3d","text":"","code":"nn_batch_norm3d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm3D — nn_batch_norm3d","text":"num_features \\(C\\) expected input size \\((N, C, D, H, W)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics uses batch statistics instead training eval modes running mean variance None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm3D — nn_batch_norm3d","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased = FALSE). Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm3D — nn_batch_norm3d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics : \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, D, H, W) slices, common terminology call Volumetric Batch Normalization Spatio-temporal Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm3D — nn_batch_norm3d","text":"Input: \\((N, C, D, H, W)\\) Output: \\((N, C, D, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm3D — nn_batch_norm3d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm3d(100) # Without Learnable Parameters m <- nn_batch_norm3d(100, affine = FALSE) input <- torch_randn(20, 100, 35, 45, 55) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary cross entropy loss — nn_bce_loss","title":"Binary cross entropy loss — nn_bce_loss","text":"Creates criterion measures Binary Cross Entropy target output:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary cross entropy loss — nn_bce_loss","text":"","code":"nn_bce_loss(weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary cross entropy loss — nn_bce_loss","text":"weight (Tensor, optional): manual rescaling weight given loss batch element. given, Tensor size nbatch. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Binary cross entropy loss — nn_bce_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_n \\left[ y_n \\cdot \\log x_n + (1 - y_n) \\cdot \\log (1 - x_n) \\right] $$ \\(N\\) batch size. reduction 'none' (default 'mean'), $$   \\ell(x, y) = \\left\\{ \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} \\right. $$ used measuring error reconstruction example auto-encoder. Note targets \\(y\\) numbers 0 1. Notice \\(x_n\\) either 0 1, one log terms mathematically undefined loss equation. PyTorch chooses set \\(\\log (0) = -\\infty\\), since \\(\\lim_{x\\0} \\log (x) = -\\infty\\). However, infinite term loss equation desirable several reasons. one, either \\(y_n = 0\\) \\((1 - y_n) = 0\\), multiplying 0 infinity. Secondly, infinite loss value, also infinite term gradient, since \\(\\lim_{x\\0} \\frac{d}{dx} \\log (x) = \\infty\\). make BCELoss's backward method nonlinear respect \\(x_n\\), using things like linear regression straight-forward. solution BCELoss clamps log function outputs greater equal -100. way, can always finite loss value linear backward method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Binary cross entropy loss — nn_bce_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Binary cross entropy loss — nn_bce_loss","text":"","code":"if (torch_is_installed()) { m <- nn_sigmoid() loss <- nn_bce_loss() input <- torch_randn(3, requires_grad = TRUE) target <- torch_rand(3) output <- loss(m(input), target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"BCE with logits loss — nn_bce_with_logits_loss","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"loss combines Sigmoid layer BCELoss one single class. version numerically stable using plain Sigmoid followed BCELoss , combining operations one layer, take advantage log-sum-exp trick numerical stability.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"","code":"nn_bce_with_logits_loss(weight = NULL, reduction = \"mean\", pos_weight = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"weight (Tensor, optional): manual rescaling weight given loss batch element. given, Tensor size nbatch. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. pos_weight (Tensor, optional): weight positive examples. Must vector length equal number classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_n \\left[ y_n \\cdot \\log \\sigma(x_n)                    + (1 - y_n) \\cdot \\log (1 - \\sigma(x_n)) \\right], $$ \\(N\\) batch size. reduction 'none' (default 'mean'), $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ used measuring error reconstruction example auto-encoder. Note targets t[] numbers 0 1. possible trade recall precision adding weights positive examples. case multi-label classification loss can described : $$ \\ell_c(x, y) = L_c = \\{l_{1,c},\\dots,l_{N,c}\\}^\\top, \\quad l_{n,c} = - w_{n,c} \\left[ p_c y_{n,c} \\cdot \\log \\sigma(x_{n,c}) + (1 - y_{n,c}) \\cdot \\log (1 - \\sigma(x_{n,c})) \\right], $$ \\(c\\) class number (\\(c > 1\\) multi-label binary classification, \\(c = 1\\) single-label binary classification), \\(n\\) number sample batch \\(p_c\\) weight positive answer class \\(c\\). \\(p_c > 1\\) increases recall, \\(p_c < 1\\) increases precision. example, dataset contains 100 positive 300 negative examples single class, pos_weight class equal \\(\\frac{300}{100}=3\\). loss act dataset contains \\(3\\times 100=300\\) positive examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_bce_with_logits_loss() input <- torch_randn(3, requires_grad = TRUE) target <- torch_empty(3)$random_(1, 2) output <- loss(input, target) output$backward()  target <- torch_ones(10, 64, dtype = torch_float32()) # 64 classes, batch size = 10 output <- torch_full(c(10, 64), 1.5) # A prediction (logit) pos_weight <- torch_ones(64) # All weights are equal to 1 criterion <- nn_bce_with_logits_loss(pos_weight = pos_weight) criterion(output, target) # -log(sigmoid(1.5)) } #> torch_tensor #> 0.201413 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":null,"dir":"Reference","previous_headings":"","what":"Bilinear module — nn_bilinear","title":"Bilinear module — nn_bilinear","text":"Applies bilinear transformation incoming data \\(y = x_1^T x_2 + b\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bilinear module — nn_bilinear","text":"","code":"nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bilinear module — nn_bilinear","text":"in1_features size first input sample in2_features size second input sample out_features size output sample bias set FALSE, layer learn additive bias. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Bilinear module — nn_bilinear","text":"Input1: \\((N, *, H_{in1})\\) \\(H_{in1}=\\mbox{in1\\_features}\\) \\(*\\) means number additional dimensions. last dimension inputs . Input2: \\((N, *, H_{in2})\\) \\(H_{in2}=\\mbox{in2\\_features}\\). Output: \\((N, *, H_{})\\) \\(H_{}=\\mbox{\\_features}\\) last dimension shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Bilinear module — nn_bilinear","text":"weight: learnable weights module shape \\((\\mbox{\\_features}, \\mbox{in1\\_features}, \\mbox{in2\\_features})\\). values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\), \\(k = \\frac{1}{\\mbox{in1\\_features}}\\) bias: learnable bias module shape \\((\\mbox{\\_features})\\). bias TRUE, values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\), \\(k = \\frac{1}{\\mbox{in1\\_features}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bilinear module — nn_bilinear","text":"","code":"if (torch_is_installed()) { m <- nn_bilinear(20, 30, 50) input1 <- torch_randn(128, 20) input2 <- torch_randn(128, 30) output <- m(input1, input2) print(output$size()) } #> [1] 128  50"},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a nn_buffer — nn_buffer","title":"Creates a nn_buffer — nn_buffer","text":"Indicates tensor buffer nn_module","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a nn_buffer — nn_buffer","text":"","code":"nn_buffer(x, persistent = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a nn_buffer — nn_buffer","text":"x tensor converted nn_buffer persistent whether buffer persistent .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"CELU module — nn_celu","title":"CELU module — nn_celu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CELU module — nn_celu","text":"","code":"nn_celu(alpha = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CELU module — nn_celu","text":"alpha \\(\\alpha\\) value CELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CELU module — nn_celu","text":"$$   \\mbox{CELU}(x) = \\max(0,x) + \\min(0, \\alpha * (\\exp(x/\\alpha) - 1)) $$ details can found paper Continuously Differentiable Exponential Linear Units.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"CELU module — nn_celu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CELU module — nn_celu","text":"","code":"if (torch_is_installed()) { m <- nn_celu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparsemax activation — nn_contrib_sparsemax","title":"Sparsemax activation — nn_contrib_sparsemax","text":"Sparsemax activation module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparsemax activation — nn_contrib_sparsemax","text":"","code":"nn_contrib_sparsemax(dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparsemax activation — nn_contrib_sparsemax","text":"dim dimension apply sparsemax function. (-1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sparsemax activation — nn_contrib_sparsemax","text":"SparseMax activation described 'Softmax Sparsemax: Sparse Model Attention Multi-Label Classification' implementation based aced125/sparsemax","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1D module — nn_conv1d","title":"Conv1D module — nn_conv1d","text":"Applies 1D convolution input signal composed several input planes. simplest case, output value layer input size \\((N, C_{\\mbox{}}, L)\\) output \\((N, C_{\\mbox{}}, L_{\\mbox{}})\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1D module — nn_conv1d","text":"","code":"nn_conv1d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1D module — nn_conv1d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int, tuple str, optional) – Padding added sides input. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv1D module — nn_conv1d","text":"$$ \\mbox{}(N_i, C_{\\mbox{}_j}) = \\mbox{bias}(C_{\\mbox{}_j}) +   \\sum_{k = 0}^{C_{} - 1} \\mbox{weight}(C_{\\mbox{}_j}, k) \\star \\mbox{input}(N_i, k) $$ \\(\\star\\) valid cross-correlation operator, \\(N\\) batch size, \\(C\\) denotes number channels, \\(L\\) length signal sequence. stride controls stride cross-correlation, single number one-element tuple. padding controls amount implicit zero-paddings sides padding number points. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv1D module — nn_conv1d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size \\((N, C_{}, L_{})\\), depthwise convolution depthwise multiplier K, can constructed arguments \\((C_{\\mbox{}}=C_{}, C_{\\mbox{}}=C_{} \\times K, ..., \\mbox{groups}=C_{})\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv1D module — nn_conv1d","text":"Input: \\((N, C_{}, L_{})\\) Output: \\((N, C_{}, L_{})\\) $$   L_{} = \\left\\lfloor\\frac{L_{} + 2 \\times \\mbox{padding} - \\mbox{dilation}     \\times (\\mbox{kernel\\_size} - 1) - 1}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv1D module — nn_conv1d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}}, \\mbox{kernel\\_size})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\) bias (Tensor): learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv1D module — nn_conv1d","text":"","code":"if (torch_is_installed()) { m <- nn_conv1d(16, 33, 3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2D module — nn_conv2d","title":"Conv2D module — nn_conv2d","text":"Applies 2D convolution input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2D module — nn_conv2d","text":"","code":"nn_conv2d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2D module — nn_conv2d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple string, optional): Zero-padding added sides input. controls amount padding applied input. can either string 'valid', '' tuple ints giving amount implicit padding applied sides. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv2D module — nn_conv2d","text":"simplest case, output value layer input size \\((N, C_{\\mbox{}}, H, W)\\) output \\((N, C_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}})\\) can precisely described : $$ \\mbox{}(N_i, C_{\\mbox{}_j}) = \\mbox{bias}(C_{\\mbox{}_j}) +   \\sum_{k = 0}^{C_{\\mbox{}} - 1} \\mbox{weight}(C_{\\mbox{}_j}, k) \\star \\mbox{input}(N_i, k) $$ \\(\\star\\) valid 2D cross-correlation operator, \\(N\\) batch size, \\(C\\) denotes number channels, \\(H\\) height input planes pixels, \\(W\\) width pixels. stride controls stride cross-correlation, single number tuple. padding controls amount implicit zero-paddings sides padding number points dimension. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size: \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\). parameters kernel_size, stride, padding, dilation can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv2D module — nn_conv2d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size :math:(N, C_{}, H_{}, W_{}), depthwise convolution depthwise multiplier K, can constructed arguments \\((\\_channels=C_{}, \\_channels=C_{} \\times K, ..., groups=C_{})\\). circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting backends_cudnn_deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv2D module — nn_conv2d","text":"Input: \\((N, C_{}, H_{}, W_{})\\) Output: \\((N, C_{}, H_{}, W_{})\\) $$   H_{} = \\left\\lfloor\\frac{H_{}  + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0]     \\times (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{}  + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1]     \\times (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv2D module — nn_conv2d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}}\\), \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\) bias (Tensor): learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv2D module — nn_conv2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride m <- nn_conv2d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2)) # non-square kernels and unequal stride and with padding and dilation m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2), dilation = c(3, 1)) input <- torch_randn(20, 16, 50, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3D module — nn_conv3d","title":"Conv3D module — nn_conv3d","text":"Applies 3D convolution input signal composed several input planes. simplest case, output value layer input size \\((N, C_{}, D, H, W)\\) output \\((N, C_{}, D_{}, H_{}, W_{})\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3D module — nn_conv3d","text":"","code":"nn_conv3d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3D module — nn_conv3d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int, tuple str, optional): padding added six sides input. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv3D module — nn_conv3d","text":"$$   (N_i, C_{out_j}) = bias(C_{out_j}) +   \\sum_{k = 0}^{C_{} - 1} weight(C_{out_j}, k) \\star input(N_i, k) $$ \\(\\star\\) valid 3D cross-correlation operator stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides padding number points dimension. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\). parameters kernel_size, stride, padding, dilation can either : single int -- case value used depth, height width dimension tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv3D module — nn_conv3d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size \\((N, C_{}, D_{}, H_{}, W_{})\\), depthwise convolution depthwise multiplier K, can constructed arguments \\((\\_channels=C_{}, \\_channels=C_{} \\times K, ..., groups=C_{})\\). circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE. Please see notes :doc:/notes/randomness background.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv3D module — nn_conv3d","text":"Input: \\((N, C_{}, D_{}, H_{}, W_{})\\) Output: \\((N, C_{}, D_{}, H_{}, W_{})\\) $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0]     \\times (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor  $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1]     \\times (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor  $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] - \\mbox{dilation}[2]     \\times (\\mbox{kernel\\_size}[2] - 1) - 1}{\\mbox{stride}[2]} + 1\\right\\rfloor  $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv3D module — nn_conv3d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]}, \\mbox{kernel\\_size[2]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels). bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv3D module — nn_conv3d","text":"","code":"if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv3d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(4, 2, 0)) input <- torch_randn(20, 16, 10, 50, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranspose1D — nn_conv_transpose1d","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Applies 1D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranspose1D — nn_conv_transpose1d","text":"","code":"nn_conv_transpose1d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranspose1D — nn_conv_transpose1d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides input. Default: 0 output_padding (int tuple, optional): Additional size added one side output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: TRUE dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranspose1D — nn_conv_transpose1d","text":"module can seen gradient Conv1d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set ~torch.nn.Conv1d ~torch.nn.ConvTranspose1d initialized parameters, inverses regard input output shapes. However, stride > 1, ~torch.nn.Conv1d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Input: \\((N, C_{}, L_{})\\) Output: \\((N, C_{}, L_{})\\) $$   L_{} = (L_{} - 1) \\times \\mbox{stride} - 2 \\times \\mbox{padding} + \\mbox{dilation} \\times (\\mbox{kernel\\_size} - 1) + \\mbox{output\\_padding} + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranspose1D — nn_conv_transpose1d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\) bias (Tensor):   learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranspose1D — nn_conv_transpose1d","text":"","code":"if (torch_is_installed()) { m <- nn_conv_transpose1d(32, 16, 2) input <- torch_randn(10, 32, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranpose2D module — nn_conv_transpose2d","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"","code":"nn_conv_transpose2d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides dimension input. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: True dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"module can seen gradient Conv2d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)). parameters kernel_size, stride, padding, output_padding can either : single int -- case value used height width dimensions tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation_, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set nn_conv2d nn_conv_transpose2d initialized parameters, inverses regard input output shapes. However, stride > 1, nn_conv2d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Input: \\((N, C_{}, H_{}, W_{})\\) Output: \\((N, C_{}, H_{}, W_{})\\) $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{dilation}[0] \\times (\\mbox{kernel\\_size}[0] - 1) + \\mbox{output\\_padding}[0] + 1 $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride}[1] - 2 \\times \\mbox{padding}[1] + \\mbox{dilation}[1] \\times (\\mbox{kernel\\_size}[1] - 1) + \\mbox{output\\_padding}[1] + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels) bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"","code":"if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv_transpose2d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2)) input <- torch_randn(20, 16, 50, 100) output <- m(input) # exact output size can be also specified as an argument input <- torch_randn(1, 16, 12, 12) downsample <- nn_conv2d(16, 16, 3, stride = 2, padding = 1) upsample <- nn_conv_transpose2d(16, 16, 3, stride = 2, padding = 1) h <- downsample(input) h$size() output <- upsample(h, output_size = input$size()) output$size() } #> [1]  1 16 12 12"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranpose3D module — nn_conv_transpose3d","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"","code":"nn_conv_transpose3d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides dimension input. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: True dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"transposed convolution operator multiplies input value element-wise learnable kernel, sums outputs input feature planes. module can seen gradient Conv3d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)). parameters kernel_size, stride, padding, output_padding can either : single int -- case value used depth, height width dimensions tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set ~torch.nn.Conv3d ~torch.nn.ConvTranspose3d initialized parameters, inverses regard input output shapes. However, stride > 1, ~torch.nn.Conv3d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Input: \\((N, C_{}, D_{}, H_{}, W_{})\\) Output: \\((N, C_{}, D_{}, H_{}, W_{})\\) $$   D_{} = (D_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{dilation}[0] \\times (\\mbox{kernel\\_size}[0] - 1) + \\mbox{output\\_padding}[0] + 1 $$ $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[1] - 2 \\times \\mbox{padding}[1] + \\mbox{dilation}[1] \\times (\\mbox{kernel\\_size}[1] - 1) + \\mbox{output\\_padding}[1] + 1 $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride}[2] - 2 \\times \\mbox{padding}[2] + \\mbox{dilation}[2] \\times (\\mbox{kernel\\_size}[2] - 1) + \\mbox{output\\_padding}[2] + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]}, \\mbox{kernel\\_size[2]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels) bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"","code":"if (torch_is_installed()) { if (FALSE) { # With square kernels and equal stride m <- nn_conv_transpose3d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(0, 4, 2)) input <- torch_randn(20, 16, 10, 50, 100) output <- m(input) } }"},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine embedding loss — nn_cosine_embedding_loss","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"Creates criterion measures loss given input tensors \\(x_1\\), \\(x_2\\) Tensor label \\(y\\) values 1 -1. used measuring whether two inputs similar dissimilar, using cosine distance, typically used learning nonlinear embeddings semi-supervised learning. loss function sample :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"","code":"nn_cosine_embedding_loss(margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"margin (float, optional): number \\(-1\\) \\(1\\), \\(0\\) \\(0.5\\) suggested. margin missing, default value \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"$$   \\mbox{loss}(x, y) =   \\begin{array}{ll} 1 - \\cos(x_1, x_2), & \\mbox{} y = 1 \\\\ \\max(0, \\cos(x_1, x_2) - \\mbox{margin}), & \\mbox{} y = -1 \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CrossEntropyLoss module — nn_cross_entropy_loss","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"criterion combines nn_log_softmax() nn_nll_loss() one single class. useful training classification problem C classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"","code":"nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C ignore_index (int, optional): Specifies target value ignored contribute input gradient. size_average TRUE, loss averaged non-ignored targets. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"provided, optional argument weight 1D Tensor assigning weight classes. particularly useful unbalanced training set. input expected contain raw, unnormalized scores class. input Tensor size either \\((minibatch, C)\\) \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional case (described later). criterion expects class index range \\([0, C-1]\\) target value 1D tensor size minibatch; ignore_index specified, criterion also accepts class index (index may necessarily class range). loss can described : $$   \\mbox{loss}(x, class) = -\\log\\left(\\frac{\\exp(x[class])}{\\sum_j \\exp(x[j])}\\right) = -x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right) $$ case weight argument specified: $$   \\mbox{loss}(x, class) = weight[class] \\left(-x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right)\\right) $$ losses averaged across observations minibatch. Can also used higher dimension inputs, 2D images, providing input size \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\), \\(K\\) number dimensions, target appropriate shape (see ).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"Input: \\((N, C)\\) C = number classes, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Target: \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Output: scalar. reduction 'none', size target: \\((N)\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_cross_entropy_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long()) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"The Connectionist Temporal Classification loss. — nn_ctc_loss","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"Calculates loss continuous (unsegmented) time series target sequence. CTCLoss sums probability possible alignments input target, producing loss value differentiable respect input node. alignment input target assumed \"many--one\", limits length target sequence must \\(\\leq\\) input length.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"","code":"nn_ctc_loss(blank = 0, reduction = \"mean\", zero_infinity = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"blank (int, optional): blank label. Default \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': output losses divided target lengths mean batch taken. Default: 'mean' zero_infinity (bool, optional): Whether zero infinite losses associated gradients. Default: FALSE Infinite losses mainly occur inputs short aligned targets.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"order use CuDNN, following must satisfied: targets must concatenated format, input_lengths must T.  \\(blank=0\\), target_lengths \\(\\leq 256\\), integer arguments must regular implementation uses (common PyTorch) torch_long dtype. dtype torch_int32. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"Log_probs: Tensor size \\((T, N, C)\\), \\(T = \\mbox{input length}\\), \\(N = \\mbox{batch size}\\), \\(C = \\mbox{number classes (including blank)}\\). logarithmized probabilities outputs (e.g. obtained [nnf)log_softmax()]). Targets: Tensor size \\((N, S)\\) \\((\\mbox{sum}(\\mbox{target\\_lengths}))\\), \\(N = \\mbox{batch size}\\) \\(S = \\mbox{max target length, shape } (N, S)\\). represent target sequences. element target sequence class index. target index blank (default=0). \\((N, S)\\) form, targets padded length longest sequence, stacked. \\((\\mbox{sum}(\\mbox{target\\_lengths}))\\) form, targets assumed un-padded concatenated within 1 dimension. Input_lengths: Tuple tensor size \\((N)\\), \\(N = \\mbox{batch size}\\). represent lengths inputs (must \\(\\leq T\\)). lengths specified sequence achieve masking assumption sequences padded equal lengths. Target_lengths: Tuple tensor size \\((N)\\), \\(N = \\mbox{batch size}\\). represent lengths targets. Lengths specified sequence achieve masking assumption sequences padded equal lengths. target shape \\((N,S)\\), target_lengths effectively stop index \\(s_n\\) target sequence, target_n = targets[n,0:s_n] target batch. Lengths must \\(\\leq S\\) targets given 1d tensor concatenation individual targets, target_lengths must add total length tensor. Output: scalar. reduction 'none', \\((N)\\), \\(N = \\mbox{batch size}\\). [nnf)log_softmax()]: R:nnf)log_softmax() [n,0:s_n]: R:n,0:s_n","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":". Graves et al.: Connectionist Temporal Classification: Labelling Unsegmented Sequence Data Recurrent Neural Networks: https://www.cs.toronto.edu/~graves/icml_2006.pdf","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"","code":"if (torch_is_installed()) { # Target are to be padded T <- 50 # Input sequence length C <- 20 # Number of classes (including blank) N <- 16 # Batch size S <- 30 # Target sequence length of longest target in batch (padding length) S_min <- 10 # Minimum target length, for demonstration purposes  # Initialize random batch of input vectors, for *size = (T,N,C) input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()  # Initialize random batch of targets (0 = blank, 1:C = classes) target <- torch_randint(low = 1, high = C, size = c(N, S), dtype = torch_long())  input_lengths <- torch_full(size = c(N), fill_value = TRUE, dtype = torch_long()) target_lengths <- torch_randint(low = S_min, high = S, size = c(N), dtype = torch_long()) ctc_loss <- nn_ctc_loss() loss <- ctc_loss(input, target, input_lengths, target_lengths) loss$backward()   # Target are to be un-padded T <- 50 # Input sequence length C <- 20 # Number of classes (including blank) N <- 16 # Batch size  # Initialize random batch of input vectors, for *size = (T,N,C) input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_() input_lengths <- torch_full(size = c(N), fill_value = TRUE, dtype = torch_long())  # Initialize random batch of targets (0 = blank, 1:C = classes) target_lengths <- torch_randint(low = 1, high = T, size = c(N), dtype = torch_long()) target <- torch_randint(   low = 1, high = C, size = as.integer(sum(target_lengths)),   dtype = torch_long() ) ctc_loss <- nn_ctc_loss() loss <- ctc_loss(input, target, input_lengths, target_lengths) loss$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout module — nn_dropout","title":"Dropout module — nn_dropout","text":"training, randomly zeroes elements input tensor probability p using samples Bernoulli distribution. channel zeroed independently every forward call.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout module — nn_dropout","text":"","code":"nn_dropout(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout module — nn_dropout","text":"p probability element zeroed. Default: 0.5 inplace set TRUE, operation -place. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout module — nn_dropout","text":"proven effective technique regularization preventing co-adaptation neurons described paper Improving neural networks preventing co-adaptation feature detectors. Furthermore, outputs scaled factor :math:\\frac{1}{1-p} training. means evaluation module simply computes identity function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout module — nn_dropout","text":"Input: \\((*)\\). Input can shape Output: \\((*)\\). Output shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout module — nn_dropout","text":"","code":"if (torch_is_installed()) { m <- nn_dropout(p = 0.2) input <- torch_randn(20, 16) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout2D module — nn_dropout2d","title":"Dropout2D module — nn_dropout2d","text":"Randomly zero entire channels (channel 2D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 2D tensor \\(\\mbox{input}[, j]\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout2D module — nn_dropout2d","text":"","code":"nn_dropout2d(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout2D module — nn_dropout2d","text":"p (float, optional): probability element zero-ed. inplace (bool, optional): set TRUE, operation -place","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout2D module — nn_dropout2d","text":"channel zeroed independently every forward call probability p using samples Bernoulli distribution. Usually input comes nn_conv2d modules. described paper Efficient Object Localization Using Convolutional Networks , adjacent pixels within feature maps strongly correlated (normally case early convolution layers) ..d. dropout regularize activations otherwise just result effective learning rate decrease. case, nn_dropout2d help promote independence feature maps used instead.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout2D module — nn_dropout2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout2D module — nn_dropout2d","text":"","code":"if (torch_is_installed()) { m <- nn_dropout2d(p = 0.2) input <- torch_randn(20, 16, 32, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout3D module — nn_dropout3d","title":"Dropout3D module — nn_dropout3d","text":"Randomly zero entire channels (channel 3D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 3D tensor \\(\\mbox{input}[, j]\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout3D module — nn_dropout3d","text":"","code":"nn_dropout3d(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout3D module — nn_dropout3d","text":"p (float, optional): probability element zeroed. inplace (bool, optional): set TRUE, operation -place","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout3D module — nn_dropout3d","text":"channel zeroed independently every forward call probability p using samples Bernoulli distribution. Usually input comes nn_conv2d modules. described paper Efficient Object Localization Using Convolutional Networks , adjacent pixels within feature maps strongly correlated (normally case early convolution layers) ..d. dropout regularize activations otherwise just result effective learning rate decrease. case, nn_dropout3d help promote independence feature maps used instead.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout3D module — nn_dropout3d","text":"Input: \\((N, C, D, H, W)\\) Output: \\((N, C, D, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout3D module — nn_dropout3d","text":"","code":"if (torch_is_installed()) { m <- nn_dropout3d(p = 0.2) input <- torch_randn(20, 16, 4, 32, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"ELU module — nn_elu","title":"ELU module — nn_elu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ELU module — nn_elu","text":"","code":"nn_elu(alpha = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ELU module — nn_elu","text":"alpha \\(\\alpha\\) value ELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ELU module — nn_elu","text":"$$   \\mbox{ELU}(x) = \\max(0,x) + \\min(0, \\alpha * (\\exp(x) - 1)) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ELU module — nn_elu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ELU module — nn_elu","text":"","code":"if (torch_is_installed()) { m <- nn_elu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding module — nn_embedding","title":"Embedding module — nn_embedding","text":"simple lookup table stores embeddings fixed dictionary size. module often used store word embeddings retrieve using indices. input module list indices, output corresponding word embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding module — nn_embedding","text":"","code":"nn_embedding(   num_embeddings,   embedding_dim,   padding_idx = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   sparse = FALSE,   .weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding module — nn_embedding","text":"num_embeddings (int): size dictionary embeddings embedding_dim (int): size embedding vector padding_idx (int, optional): given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. max_norm (float, optional): given, embedding vector norm larger max_norm renormalized norm max_norm. norm_type (float, optional): p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional): given, scale gradients inverse frequency words mini-batch. Default False. sparse (bool, optional): True, gradient w.r.t. weight matrix sparse tensor. .weight (Tensor) embeddings weights (case want set manually) See Notes details regarding sparse gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Embedding module — nn_embedding","text":"Keep mind limited number optimizers support sparse gradients: currently optim.SGD (CUDA CPU), optim.SparseAdam (CUDA CPU) optim.Adagrad (CPU) padding_idx set, embedding vector padding_idx initialized zeros. However, note vector can modified afterwards, e.g., using customized initialization method, thus changing vector used pad output. gradient vector nn_embedding always zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Embedding module — nn_embedding","text":"weight (Tensor): learnable weights module shape (num_embeddings, embedding_dim) initialized \\(\\mathcal{N}(0, 1)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Embedding module — nn_embedding","text":"Input: \\((*)\\), LongTensor arbitrary shape containing indices extract Output: \\((*, H)\\), * input shape \\(H=\\mbox{embedding\\_dim}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Embedding module — nn_embedding","text":"","code":"if (torch_is_installed()) { # an Embedding module containing 10 tensors of size 3 embedding <- nn_embedding(10, 3) # a batch of 2 samples of 4 indices each input <- torch_tensor(rbind(c(1, 2, 4, 5), c(4, 3, 2, 9)), dtype = torch_long()) embedding(input) # example with padding_idx embedding <- nn_embedding(10, 3, padding_idx = 1) input <- torch_tensor(matrix(c(1, 3, 1, 6), nrow = 1), dtype = torch_long()) embedding(input) } #> torch_tensor #> (1,.,.) =  #>   0.0000  0.0000  0.0000 #>   1.0913  0.6573  2.3777 #>   0.0000  0.0000  0.0000 #>  -0.3899 -0.0915 -1.3749 #> [ CPUFloatType{1,4,3} ][ grad_fn = <EmbeddingBackward0> ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding bag module — nn_embedding_bag","title":"Embedding bag module — nn_embedding_bag","text":"Computes sums, means maxes bags embeddings, without instantiating intermediate embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding bag module — nn_embedding_bag","text":"","code":"nn_embedding_bag(   num_embeddings,   embedding_dim,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   mode = \"mean\",   sparse = FALSE,   include_last_offset = FALSE,   padding_idx = NULL,   .weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding bag module — nn_embedding_bag","text":"num_embeddings (int): size dictionary embeddings embedding_dim (int): size embedding vector max_norm (float, optional): given, embedding vector norm larger max_norm renormalized norm max_norm. norm_type (float, optional): p p-norm compute max_norm option. Default 2 scale_grad_by_freq (boolean, optional): given, scale gradients inverse frequency words mini-batch. Default False. mode (string, optional): \"sum\", \"mean\" \"max\". Specifies way reduce bag. \"sum\" computes weighted sum, taking per_sample_weights  consideration. \"mean\" computes average values bag, \"max\" computes max value bag. sparse (bool, optional): True, gradient w.r.t. weight matrix sparse tensor. See Notes details regarding sparse gradients. include_last_offset (bool, optional): True, offsets one additional element, last element equivalent size indices. matches CSR format. padding_idx (int, optional):  given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. .weight (Tensor, optional) embeddings weights (case want set manually)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Embedding bag module — nn_embedding_bag","text":"weight (Tensor): learnable weights module shape (num_embeddings, embedding_dim) initialized \\(\\mathcal{N}(0, 1)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Embedding bag module — nn_embedding_bag","text":"","code":"if (torch_is_installed()) { # an EmbeddingBag module containing 10 tensors of size 3 embedding_sum <- nn_embedding_bag(10, 3, mode = 'sum') # a batch of 2 samples of 4 indices each input <- torch_tensor(c(1, 2, 4, 5, 4, 3, 2, 9), dtype = torch_long()) offsets <- torch_tensor(c(0, 4), dtype = torch_long()) embedding_sum(input, offsets) # example with padding_idx embedding_sum <- nn_embedding_bag(10, 3, mode = 'sum', padding_idx = 1) input <- torch_tensor(c(2, 2, 2, 2, 4, 3, 2, 9), dtype = torch_long()) offsets <- torch_tensor(c(0, 4), dtype = torch_long()) embedding_sum(input, offsets) # An EmbeddingBag can be loaded from an Embedding like so embedding <- nn_embedding(10, 3, padding_idx = 2) embedding_sum <- nn_embedding_bag$from_pretrained(embedding$weight,                                                  padding_idx = embedding$padding_idx,                                                  mode='sum') }"},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens a contiguous range of dims into a tensor. — nn_flatten","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"use nn_sequential.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"","code":"nn_flatten(start_dim = 2, end_dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"start_dim first dim flatten (default = 2). end_dim last dim flatten (default = -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"Input: (*, S_start,..., S_i, ..., S_end, *), S_i size dimension * means number dimensions including none. Output: (*, S_start*...*S_i*...S_end, *).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"","code":"if (torch_is_installed()) { input <- torch_randn(32, 1, 5, 5) m <- nn_flatten() m(input) } #> torch_tensor #> Columns 1 to 10-0.8015 -1.0448  0.4715 -0.0156  0.7085  0.6113 -0.5675 -0.7171 -0.2615  1.2159 #>  2.3857  1.2484 -0.5993  0.3149  1.0410 -0.0031 -0.1256 -1.4730 -0.4148  0.2949 #> -0.6647  2.6382 -1.9189  1.5107  0.1844 -0.4771 -0.1499  0.6239 -0.2575  0.1938 #>  2.0218 -0.1541  0.3580 -0.6831 -0.4747  1.5348  0.7168  0.3497 -0.4437  0.5037 #>  1.5272 -1.2138 -2.3443 -0.6669  0.8383  1.6540 -0.5365  0.3552  1.8786 -1.7464 #>  0.6394 -0.0746 -0.3072 -0.5264 -0.9450  1.0271  0.0991  0.0619  0.3204 -0.2456 #>  0.0250  1.0297 -1.4002  1.7605  1.5106 -2.4053 -0.9495 -0.1437  0.0332  1.5030 #> -0.5799  0.0074  1.2746  1.4900 -1.0154  0.3608 -0.8096 -0.9809  0.0256  2.0557 #>  0.3317  0.3536  0.8048  0.7115  0.3078  1.2502  1.4476 -0.4187 -1.0832 -0.8844 #> -1.0850 -0.6192  0.1627 -1.9400 -0.6659  1.2134  0.2179  0.5830 -0.6183  0.0321 #> -1.6692  0.7485 -0.8128  1.0693 -0.9317  0.9081 -0.7930 -0.7436 -0.6327  0.9231 #>  1.7045  0.1461  0.3835  1.6954 -0.1849 -0.2247  0.6355 -0.5568  0.3298  1.1491 #>  0.1119 -0.1293 -0.1382  1.6020 -0.7861  0.8450  1.0183 -0.0673  0.3050  0.9445 #> -1.8077 -0.1964 -0.1349  1.0182  0.7130 -0.4587 -2.3798  0.3252  1.3848 -0.7724 #>  0.2489  0.5840  0.1690 -0.9544  1.1001 -0.4978 -1.2585 -0.9969  0.0469  0.6774 #> -1.7650 -0.4240 -0.3131  2.0314  1.0105  1.0466  0.2259 -0.2248  1.6021  1.5515 #> -0.8189 -0.2861 -0.1885 -0.1888  1.2369 -0.8073  1.0088  0.8614  0.9339  0.4690 #> -0.8568  1.5507 -1.4375  1.7597  0.7363 -1.4567  2.2990  1.3478  1.4151  0.4128 #>  0.1707  0.9011 -1.6190  0.0704  0.8973 -1.6506  0.3608  0.0830  1.6077 -1.5901 #> -1.0287 -0.4341  0.0636 -0.4749  0.1226 -1.1832 -0.0746  0.9812 -1.4514 -0.0938 #>  0.0018  1.7226 -0.3270  1.1839 -0.4936 -0.8200  0.0203 -0.7399  0.3987  0.4709 #>  0.1172  0.9947  0.4600  1.3165 -1.3755  0.4174 -2.3616 -1.1633 -0.0096 -1.6175 #>  0.8800 -0.6760 -0.3397  1.4769 -0.1437  1.3507  0.3871 -0.4962 -1.1812  0.5646 #> -0.3333 -0.1028 -0.1121  0.2401 -0.9883  1.6939 -0.7209  1.0521 -0.3183 -0.0213 #> -2.6093 -1.1583 -0.8108  0.3671 -0.3561  1.3556  0.1852 -0.6264  1.0287  0.8352 #>  0.8922  1.2746  0.8763 -0.0620 -0.4186 -0.4048  0.1820 -2.4252 -0.5739 -0.7585 #>  1.6924 -0.4010 -0.0632 -0.6726  1.6817  0.9732 -0.9486 -1.2342 -1.1307  1.6650 #> -1.1404  1.6418  0.8507 -1.0957 -0.4790  1.2576  2.6430  1.4992  1.5525 -1.8223 #>  0.1169 -1.0268  1.0565 -0.2669 -0.2602  1.5338 -0.1799 -0.2940 -2.9724 -0.5963 #>  0.2384  1.6367  0.3955 -0.0629  1.3515 -0.0084  1.0178 -0.1979  0.3229  0.7691 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{32,25} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling Ben Graham","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"","code":"nn_fractional_max_pool2d(   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"kernel_size size window take max . Can single number k (square kernel k x k) tuple (kh, kw) output_size target output size image form oH x oW. Can tuple (oH, oW) single number oH square image oH x oH output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool2d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"max-pooling operation applied \\(kH \\times kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, and target output size 13x12 m <- nn_fractional_max_pool2d(3, output_size = c(13, 12)) # pool of square window and target output size being half of input image size m <- nn_fractional_max_pool2d(3, output_ratio = c(0.5, 0.5)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling Ben Graham","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"","code":"nn_fractional_max_pool3d(   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"kernel_size size window take max . Can single number k (square kernel k x k x k) tuple (kt x kh x kw) output_size target output size image form oT x oH x oW. Can tuple (oT, oH, oW) single number oH square image oH x oH x oH output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool3d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"max-pooling operation applied \\(kTxkHxkW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"","code":"if (torch_is_installed()) { # pool of cubic window of size=3, and target output size 13x12x11 m <- nn_fractional_max_pool3d(3, output_size = c(13, 12, 11)) # pool of cubic window and target output size being half of input size m <- nn_fractional_max_pool3d(3, output_ratio = c(0.5, 0.5, 0.5)) input <- torch_randn(20, 16, 50, 32, 16) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"GELU module — nn_gelu","title":"GELU module — nn_gelu","text":"Applies Gaussian Error Linear Units function: $$\\mbox{GELU}(x) = x * \\Phi(x)$$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GELU module — nn_gelu","text":"","code":"nn_gelu(approximate = \"none\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GELU module — nn_gelu","text":"approximate gelu approximation algorithm use: 'none' 'tanh'. Default: 'none'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GELU module — nn_gelu","text":"\\(\\Phi(x)\\) Cumulative Distribution Function Gaussian Distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"GELU module — nn_gelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GELU module — nn_gelu","text":"","code":"if (torch_is_installed()) { m <- nn_gelu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":null,"dir":"Reference","previous_headings":"","what":"GLU module — nn_glu","title":"GLU module — nn_glu","text":"Applies gated linear unit function \\({GLU}(, b)= \\otimes \\sigma(b)\\) \\(\\) first half input matrices \\(b\\) second half.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GLU module — nn_glu","text":"","code":"nn_glu(dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GLU module — nn_glu","text":"dim (int): dimension split input. Default: -1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"GLU module — nn_glu","text":"Input: \\((\\ast_1, N, \\ast_2)\\) * means, number additional dimensions Output: \\((\\ast_1, M, \\ast_2)\\) \\(M=N/2\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GLU module — nn_glu","text":"","code":"if (torch_is_installed()) { m <- nn_glu() input <- torch_randn(4, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization — nn_group_norm","title":"Group normalization — nn_group_norm","text":"Applies Group Normalization mini-batch inputs described paper Group Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization — nn_group_norm","text":"","code":"nn_group_norm(num_groups, num_channels, eps = 1e-05, affine = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization — nn_group_norm","text":"num_groups (int): number groups separate channels num_channels (int): number channels expected input eps value added denominator numerical stability. Default: 1e-5 affine boolean value set TRUE, module learnable per-channel affine parameters initialized ones (weights) zeros (biases). Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Group normalization — nn_group_norm","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ input channels separated num_groups groups, containing num_channels / num_groups channels. mean standard-deviation calculated separately group. \\(\\gamma\\) \\(\\beta\\) learnable per-channel affine transform parameter vectors size num_channels affine TRUE. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Group normalization — nn_group_norm","text":"layer uses statistics computed input data training evaluation modes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Group normalization — nn_group_norm","text":"Input: \\((N, C, *)\\) \\(C=\\mbox{num\\_channels}\\) Output: \\((N, C, *)\\)` (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group normalization — nn_group_norm","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(20, 6, 10, 10) # Separate 6 channels into 3 groups m <- nn_group_norm(3, 6) # Separate 6 channels into 6 groups (equivalent with [nn_instance_morm]) m <- nn_group_norm(6, 6) # Put all 6 channels into a single group (equivalent with [nn_layer_norm]) m <- nn_group_norm(1, 6) # Activating the module output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"element input sequence, layer computes following function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"","code":"nn_gru(   input_size,   hidden_size,   num_layers = 1,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two GRUs together form stacked GRU, second GRU taking outputs first GRU computing final results. Default: 1 bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs GRU layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional GRU. Default: FALSE ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"$$ \\begin{array}{ll} r_t = \\sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\\\ z_t = \\sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\\\ n_t = \\tanh(W_{} x_t + b_{} + r_t (W_{hn} h_{(t-1)}+ b_{hn})) \\\\ h_t = (1 - z_t) n_t + z_t h_{(t-1)} \\end{array} $$ \\(h_t\\) hidden state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0, \\(r_t\\), \\(z_t\\), \\(n_t\\) reset, update, new gates, respectively. \\(\\sigma\\) sigmoid function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"Inputs: input, h_0 input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() details. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. Defaults zero provided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"Outputs: output, h_n output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features h_t last layer GRU, t. PackedSequence given input, output also packed sequence. unpacked case, directions can separated using output$view(c(seq_len, batch, num_directions, hidden_size)), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len Like output, layers can separated using h_n$view(num_layers, num_directions, batch, hidden_size).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"weight_ih_l[k] : learnable input-hidden weights \\(\\mbox{k}^{th}\\) layer (W_ir|W_iz|W_in), shape (3*hidden_size x input_size) weight_hh_l[k] : learnable hidden-hidden weights \\(\\mbox{k}^{th}\\) layer (W_hr|W_hz|W_hn), shape (3*hidden_size x hidden_size) bias_ih_l[k] : learnable input-hidden bias \\(\\mbox{k}^{th}\\) layer (b_ir|b_iz|b_in), shape (3*hidden_size) bias_hh_l[k] : learnable hidden-hidden bias \\(\\mbox{k}^{th}\\) layer (b_hr|b_hz|b_hn), shape (3*hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"","code":"if (torch_is_installed()) {  rnn <- nn_gru(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) output <- rnn(input, h0) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardshwink module — nn_hardshrink","title":"Hardshwink module — nn_hardshrink","text":"Applies hard shrinkage function element-wise:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardshwink module — nn_hardshrink","text":"","code":"nn_hardshrink(lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardshwink module — nn_hardshrink","text":"lambd \\(\\lambda\\) value Hardshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardshwink module — nn_hardshrink","text":"$$   \\mbox{HardShrink}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x > \\lambda \\\\ x, & \\mbox{ } x < -\\lambda \\\\ 0, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardshwink module — nn_hardshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardshwink module — nn_hardshrink","text":"","code":"if (torch_is_installed()) { m <- nn_hardshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardsigmoid module — nn_hardsigmoid","title":"Hardsigmoid module — nn_hardsigmoid","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardsigmoid module — nn_hardsigmoid","text":"","code":"nn_hardsigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardsigmoid module — nn_hardsigmoid","text":"$$ \\mbox{Hardsigmoid}(x) = \\left\\{ \\begin{array}{ll}   0 & \\mbox{~} x \\le -3, \\\\   1 & \\mbox{~} x \\ge +3, \\\\   x / 6 + 1 / 2 & \\mbox{otherwise} \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardsigmoid module — nn_hardsigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardsigmoid module — nn_hardsigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_hardsigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardswish module — nn_hardswish","title":"Hardswish module — nn_hardswish","text":"Applies hardswish function, element-wise, described paper: Searching MobileNetV3","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardswish module — nn_hardswish","text":"","code":"nn_hardswish()"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardswish module — nn_hardswish","text":"$$ \\mbox{Hardswish}(x) = \\left\\{   \\begin{array}{ll}   0 & \\mbox{} x \\le -3, \\\\   x & \\mbox{} x \\ge +3, \\\\   x \\cdot (x + 3)/6 & \\mbox{otherwise}   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardswish module — nn_hardswish","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardswish module — nn_hardswish","text":"","code":"if (torch_is_installed()) { if (FALSE) { m <- nn_hardswish() input <- torch_randn(2) output <- m(input) }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardtanh module — nn_hardtanh","title":"Hardtanh module — nn_hardtanh","text":"Applies HardTanh function element-wise HardTanh defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardtanh module — nn_hardtanh","text":"","code":"nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardtanh module — nn_hardtanh","text":"min_val minimum value linear region range. Default: -1 max_val maximum value linear region range. Default: 1 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardtanh module — nn_hardtanh","text":"$$ \\mbox{HardTanh}(x) = \\left\\{ \\begin{array}{ll}   1 & \\mbox{ } x > 1 \\\\   -1 & \\mbox{ } x < -1 \\\\   x & \\mbox{ otherwise } \\\\ \\end{array} \\right. $$ range linear region :math:[-1, 1] can adjusted using min_val max_val.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardtanh module — nn_hardtanh","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardtanh module — nn_hardtanh","text":"","code":"if (torch_is_installed()) { m <- nn_hardtanh(-2, 2) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Hinge embedding loss — nn_hinge_embedding_loss","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"Measures loss given input tensor \\(x\\) labels tensor \\(y\\) (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"","code":"nn_hinge_embedding_loss(margin = 1, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"margin (float, optional): default value 1. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"usually used measuring whether two inputs similar dissimilar, e.g. using L1 pairwise distance \\(x\\), typically used learning nonlinear embeddings semi-supervised learning. loss function \\(n\\)-th sample mini-batch $$   l_n = \\begin{array}{ll} x_n, & \\mbox{}\\; y_n = 1,\\\\ \\max \\{0, \\Delta - x_n\\}, & \\mbox{}\\; y_n = -1, \\end{array} $$ total loss functions $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(L = \\{l_1,\\dots,l_N\\}^\\top\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"Input: \\((*)\\) \\(*\\) means, number dimensions. sum operation operates elements. Target: \\((*)\\), shape input Output: scalar. reduction 'none', shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity module — nn_identity","title":"Identity module — nn_identity","text":"placeholder identity operator argument-insensitive.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity module — nn_identity","text":"","code":"nn_identity(...)"},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity module — nn_identity","text":"... arguments (unused)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identity module — nn_identity","text":"","code":"if (torch_is_installed()) { m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE) input <- torch_randn(128, 20) output <- m(input) print(output$size()) } #> [1] 128  20"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate gain — nn_init_calculate_gain","title":"Calculate gain — nn_init_calculate_gain","text":"Return recommended gain value given nonlinearity function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate gain — nn_init_calculate_gain","text":"","code":"nn_init_calculate_gain(nonlinearity, param = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate gain — nn_init_calculate_gain","text":"nonlinearity non-linear function param optional parameter non-linear function","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":null,"dir":"Reference","previous_headings":"","what":"Constant initialization — nn_init_constant_","title":"Constant initialization — nn_init_constant_","text":"Fills input Tensor value val.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constant initialization — nn_init_constant_","text":"","code":"nn_init_constant_(tensor, val)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constant initialization — nn_init_constant_","text":"tensor n-dimensional Tensor val value fill tensor ","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Constant initialization — nn_init_constant_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_constant_(w, 0.3) } #> torch_tensor #>  0.3000  0.3000  0.3000  0.3000  0.3000 #>  0.3000  0.3000  0.3000  0.3000  0.3000 #>  0.3000  0.3000  0.3000  0.3000  0.3000 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":null,"dir":"Reference","previous_headings":"","what":"Dirac initialization — nn_init_dirac_","title":"Dirac initialization — nn_init_dirac_","text":"Fills {3, 4, 5}-dimensional input Tensor Dirac delta function. Preserves identity inputs Convolutional layers, many input channels preserved possible. case groups>1, group channels preserves identity.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dirac initialization — nn_init_dirac_","text":"","code":"nn_init_dirac_(tensor, groups = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dirac initialization — nn_init_dirac_","text":"tensor {3, 4, 5}-dimensional torch.Tensor groups (optional) number groups conv layer (default: 1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dirac initialization — nn_init_dirac_","text":"","code":"if (torch_is_installed()) { if (FALSE) { w <- torch_empty(3, 16, 5, 5) nn_init_dirac_(w) }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":null,"dir":"Reference","previous_headings":"","what":"Eye initialization — nn_init_eye_","title":"Eye initialization — nn_init_eye_","text":"Fills 2-dimensional input Tensor identity matrix. Preserves identity inputs Linear layers, many inputs preserved possible.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eye initialization — nn_init_eye_","text":"","code":"nn_init_eye_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eye initialization — nn_init_eye_","text":"tensor 2-dimensional torch tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eye initialization — nn_init_eye_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_eye_(w) } #> torch_tensor #>  1  0  0  0  0 #>  0  1  0  0  0 #>  0  0  1  0  0 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiming normal initialization — nn_init_kaiming_normal_","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"Fills input Tensor values according method described Delving deep rectifiers: Surpassing human-level performance ImageNet classification - , K. et al. (2015), using normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"","code":"nn_init_kaiming_normal_(   tensor,   a = 0,   mode = \"fan_in\",   nonlinearity = \"leaky_relu\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"tensor n-dimensional torch.Tensor negative slope rectifier used layer (used 'leaky_relu') mode either 'fan_in' (default) 'fan_out'. Choosing 'fan_in' preserves magnitude variance weights forward pass. Choosing 'fan_out' preserves magnitudes backwards pass. nonlinearity non-linear function. recommended use 'relu' 'leaky_relu' (default).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_kaiming_normal_(w, mode = \"fan_in\", nonlinearity = \"leaky_relu\") } #> torch_tensor #> -0.5483  0.6051 -0.1700  0.4698 -0.3578 #>  0.0111  0.0262  0.0239 -0.5470 -1.0493 #>  0.7398  0.0686  0.7970 -0.6537 -0.3586 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiming uniform initialization — nn_init_kaiming_uniform_","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"Fills input Tensor values according method described Delving deep rectifiers: Surpassing human-level performance ImageNet classification - , K. et al. (2015), using uniform distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"","code":"nn_init_kaiming_uniform_(   tensor,   a = 0,   mode = \"fan_in\",   nonlinearity = \"leaky_relu\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"tensor n-dimensional torch.Tensor negative slope rectifier used layer (used 'leaky_relu') mode either 'fan_in' (default) 'fan_out'. Choosing 'fan_in' preserves magnitude variance weights forward pass. Choosing 'fan_out' preserves magnitudes backwards pass. nonlinearity non-linear function. recommended use 'relu' 'leaky_relu' (default).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_kaiming_uniform_(w, mode = \"fan_in\", nonlinearity = \"leaky_relu\") } #> torch_tensor #>  0.6448 -0.9118 -0.6907 -0.7555  0.7679 #> -0.2540 -0.9912 -0.8890  0.7258  0.0094 #>  0.8867  0.6021 -0.3258 -0.0241  0.6353 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Normal initialization — nn_init_normal_","title":"Normal initialization — nn_init_normal_","text":"Fills input Tensor values drawn normal distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normal initialization — nn_init_normal_","text":"","code":"nn_init_normal_(tensor, mean = 0, std = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normal initialization — nn_init_normal_","text":"tensor n-dimensional Tensor mean mean normal distribution std standard deviation normal distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normal initialization — nn_init_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_normal_(w) } #> torch_tensor #>  1.1478  0.3302  1.4379  0.8988 -0.1674 #>  0.1804  1.2466 -1.3061 -0.3345  0.2397 #>  0.5363 -1.3692 -0.2766 -1.2271 -1.0775 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones initialization — nn_init_ones_","title":"Ones initialization — nn_init_ones_","text":"Fills input Tensor scalar value 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones initialization — nn_init_ones_","text":"","code":"nn_init_ones_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones initialization — nn_init_ones_","text":"tensor n-dimensional Tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones initialization — nn_init_ones_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_ones_(w) } #> torch_tensor #>  1  1  1  1  1 #>  1  1  1  1  1 #>  1  1  1  1  1 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal initialization — nn_init_orthogonal_","title":"Orthogonal initialization — nn_init_orthogonal_","text":"Fills input Tensor (semi) orthogonal matrix, described Exact solutions nonlinear dynamics learning deep linear neural networks - Saxe, . et al. (2013). input tensor must least 2 dimensions, tensors 2 dimensions trailing dimensions flattened.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal initialization — nn_init_orthogonal_","text":"","code":"nn_init_orthogonal_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal initialization — nn_init_orthogonal_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal initialization — nn_init_orthogonal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_orthogonal_(w) } #> torch_tensor #>  0.1160 -0.2048 -0.9706  0.0430  0.0257 #> -0.7007  0.1632 -0.0854  0.4108  0.5535 #>  0.0030  0.3251 -0.0535  0.7074 -0.6253 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse initialization — nn_init_sparse_","title":"Sparse initialization — nn_init_sparse_","text":"Fills 2D input Tensor sparse matrix, non-zero elements drawn normal distribution described Deep learning via Hessian-free optimization - Martens, J. (2010).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse initialization — nn_init_sparse_","text":"","code":"nn_init_sparse_(tensor, sparsity, std = 0.01)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse initialization — nn_init_sparse_","text":"tensor n-dimensional Tensor sparsity fraction elements column set zero std standard deviation normal distribution used generate non-zero values","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sparse initialization — nn_init_sparse_","text":"","code":"if (torch_is_installed()) { if (FALSE) { w <- torch_empty(3, 5) nn_init_sparse_(w, sparsity = 0.1) } }"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Truncated normal initialization — nn_init_trunc_normal_","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"Fills input Tensor values drawn truncated normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"","code":"nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"tensor n-dimensional Tensor mean mean normal distribution std standard deviation normal distribution minimum cutoff value b maximum cutoff value","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_trunc_normal_(w) } #> torch_tensor #>  1.6650 -0.5936 -0.4173 -0.3491 -0.2854 #> -0.7031 -0.3730  0.4309 -0.8737  0.2055 #>  0.7119  1.1223 -1.3891  1.1680 -0.0479 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniform initialization — nn_init_uniform_","title":"Uniform initialization — nn_init_uniform_","text":"Fills input Tensor values drawn uniform distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniform initialization — nn_init_uniform_","text":"","code":"nn_init_uniform_(tensor, a = 0, b = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uniform initialization — nn_init_uniform_","text":"tensor n-dimensional Tensor lower bound uniform distribution b upper bound uniform distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniform initialization — nn_init_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_uniform_(w) } #> torch_tensor #>  0.9775  0.7917  0.6477  0.2337  0.0261 #>  0.9267  0.4705  0.3090  0.4401  0.1671 #>  0.3938  0.8689  0.3312  0.1128  0.8623 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Xavier normal initialization — nn_init_xavier_normal_","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"Fills input Tensor values according method described Understanding difficulty training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"","code":"nn_init_xavier_normal_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_xavier_normal_(w) } #> torch_tensor #> -0.1958 -1.2098 -0.3898 -0.4418  0.3574 #> -0.2345  0.5271  0.5841 -0.1748  0.3071 #> -0.8960 -0.1640 -0.1738  0.7133  0.8885 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Xavier uniform initialization — nn_init_xavier_uniform_","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"Fills input Tensor values according method described Understanding difficulty training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using uniform distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"","code":"nn_init_xavier_uniform_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_xavier_uniform_(w) } #> torch_tensor #> -0.5071 -0.3531  0.1250  0.7562 -0.6302 #> -0.6892  0.6461  0.5584  0.7683 -0.3572 #> -0.5540 -0.8478 -0.5756  0.1296  0.1135 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros initialization — nn_init_zeros_","title":"Zeros initialization — nn_init_zeros_","text":"Fills input Tensor scalar value 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros initialization — nn_init_zeros_","text":"","code":"nn_init_zeros_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros initialization — nn_init_zeros_","text":"tensor n-dimensional tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros initialization — nn_init_zeros_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_zeros_(w) } #> torch_tensor #>  0  0  0  0  0 #>  0  0  0  0  0 #>  0  0  0  0  0 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Kullback-Leibler divergence loss — nn_kl_div_loss","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"Kullback-Leibler divergence loss measure Kullback-Leibler divergence useful distance measure continuous distributions often useful performing direct regression space (discretely sampled) continuous output distributions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"","code":"nn_kl_div_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'batchmean' | 'sum' | 'mean'. 'none': reduction applied. 'batchmean': sum output divided batchsize. 'sum': output summed. 'mean': output divided number elements output. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"nn_nll_loss(), input given expected contain log-probabilities restricted 2D Tensor. targets interpreted probabilities default, considered log-probabilities log_target set TRUE. criterion expects target Tensor size input Tensor. unreduced (.e. reduction set 'none') loss can described : $$   l(x,y) = L = \\{ l_1,\\dots,l_N \\}, \\quad l_n = y_n \\cdot \\left( \\log y_n - x_n \\right) $$ index \\(N\\) spans dimensions input \\(L\\) shape input. reduction 'none' (default 'mean'), : $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';} \\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ default reduction mode 'mean', losses averaged minibatch observations well dimensions. 'batchmean' mode gives correct KL divergence losses averaged batch dimension . 'mean' mode's behavior changed 'batchmean' next major release.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"reduction = 'mean' return true kl divergence value, please use reduction = 'batchmean' aligns KL math definition. next major release, 'mean' changed 'batchmean'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar default. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"L1 loss — nn_l1_loss","title":"L1 loss — nn_l1_loss","text":"Creates criterion measures mean absolute error (MAE) element input \\(x\\) target \\(y\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L1 loss — nn_l1_loss","text":"","code":"nn_l1_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L1 loss — nn_l1_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"L1 loss — nn_l1_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$ \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = \\left| x_n - y_n \\right|, $$ \\(N\\) batch size. reduction 'none' (default 'mean'), : $$ \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(x\\) \\(y\\) tensors arbitrary shapes total \\(n\\) elements . sum operation still operates elements, divides \\(n\\). division \\(n\\) can avoided one sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"L1 loss — nn_l1_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"L1 loss — nn_l1_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_l1_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randn(3, 5) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization — nn_layer_norm","title":"Layer normalization — nn_layer_norm","text":"Applies Layer Normalization mini-batch inputs described paper Layer Normalization","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization — nn_layer_norm","text":"","code":"nn_layer_norm(normalized_shape, eps = 1e-05, elementwise_affine = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization — nn_layer_norm","text":"normalized_shape (int list): input shape expected input size \\([* \\times \\mbox{normalized\\_shape}[0] \\times \\mbox{normalized\\_shape}[1] \\times \\ldots \\times \\mbox{normalized\\_shape}[-1]]\\) single integer used, treated singleton list, module normalize last dimension expected specific size. eps value added denominator numerical stability. Default: 1e-5 elementwise_affine boolean value set TRUE, module learnable per-element affine parameters initialized ones (weights) zeros (biases). Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Layer normalization — nn_layer_norm","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated separately last certain number dimensions shape specified normalized_shape. \\(\\gamma\\) \\(\\beta\\) learnable affine transform parameters normalized_shape elementwise_affine TRUE. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer normalization — nn_layer_norm","text":"Unlike Batch Normalization Instance Normalization, applies scalar scale bias entire channel/plane affine option, Layer Normalization applies per-element scale bias elementwise_affine. layer uses statistics computed input data training evaluation modes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Layer normalization — nn_layer_norm","text":"Input: \\((N, *)\\) Output: \\((N, *)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer normalization — nn_layer_norm","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(20, 5, 10, 10) # With Learnable Parameters m <- nn_layer_norm(input$size()[-1]) # Without Learnable Parameters m <- nn_layer_norm(input$size()[-1], elementwise_affine = FALSE) # Normalize over last two dimensions m <- nn_layer_norm(c(10, 10)) # Normalize over last dimension of size 10 m <- nn_layer_norm(10) # Activating the module output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"LeakyReLU module — nn_leaky_relu","title":"LeakyReLU module — nn_leaky_relu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LeakyReLU module — nn_leaky_relu","text":"","code":"nn_leaky_relu(negative_slope = 0.01, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LeakyReLU module — nn_leaky_relu","text":"negative_slope Controls angle negative slope. Default: 1e-2 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LeakyReLU module — nn_leaky_relu","text":"$$   \\mbox{LeakyReLU}(x) = \\max(0, x) + \\mbox{negative\\_slope} * \\min(0, x) $$ $$   \\mbox{LeakyRELU}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x \\geq 0 \\\\ \\mbox{negative\\_slope} \\times x, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LeakyReLU module — nn_leaky_relu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LeakyReLU module — nn_leaky_relu","text":"","code":"if (torch_is_installed()) { m <- nn_leaky_relu(0.1) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear module — nn_linear","title":"Linear module — nn_linear","text":"Applies linear transformation incoming data: y = xA^T + b","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear module — nn_linear","text":"","code":"nn_linear(in_features, out_features, bias = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear module — nn_linear","text":"in_features size input sample out_features size output sample bias set FALSE, layer learn additive bias. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Linear module — nn_linear","text":"Input: (N, *, H_in) * means number additional dimensions H_in = in_features. Output: (N, *, H_out) last dimension shape input :math:H_out = out_features.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Linear module — nn_linear","text":"weight: learnable weights module shape (out_features, in_features). values initialized \\(U(-\\sqrt{k}, \\sqrt{k})\\)s, \\(k = \\frac{1}{\\mbox{\\_features}}\\) bias: learnable bias module shape \\((\\mbox{\\_features})\\). bias TRUE, values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{\\_features}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear module — nn_linear","text":"","code":"if (torch_is_installed()) { m <- nn_linear(20, 30) input <- torch_randn(128, 20) output <- m(input) print(output$size()) } #> [1] 128  30"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"LogSigmoid module — nn_log_sigmoid","title":"LogSigmoid module — nn_log_sigmoid","text":"Applies element-wise function: $$   \\mbox{LogSigmoid}(x) = \\log\\left(\\frac{ 1 }{ 1 + \\exp(-x)}\\right)  $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LogSigmoid module — nn_log_sigmoid","text":"","code":"nn_log_sigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LogSigmoid module — nn_log_sigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LogSigmoid module — nn_log_sigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_log_sigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"LogSoftmax module — nn_log_softmax","title":"LogSoftmax module — nn_log_softmax","text":"Applies \\(\\log(\\mbox{Softmax}(x))\\) function n-dimensional input Tensor. LogSoftmax formulation can simplified :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LogSoftmax module — nn_log_softmax","text":"","code":"nn_log_softmax(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LogSoftmax module — nn_log_softmax","text":"dim (int): dimension along LogSoftmax computed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LogSoftmax module — nn_log_softmax","text":"Tensor dimension shape input values range [-inf, 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LogSoftmax module — nn_log_softmax","text":"$$   \\mbox{LogSoftmax}(x_{}) = \\log\\left(\\frac{\\exp(x_i) }{ \\sum_j \\exp(x_j)} \\right) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LogSoftmax module — nn_log_softmax","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LogSoftmax module — nn_log_softmax","text":"","code":"if (torch_is_installed()) { m <- nn_log_softmax(1) input <- torch_randn(2, 3) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"window, function computed :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"","code":"nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"$$   f(X) = \\sqrt[p]{\\sum_{x \\X} x^{p}} $$ p = \\(\\infty\\), one gets Max Pooling p = 1, one gets Sum Pooling (proportional Average Pooling)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"sum power p zero, gradient function defined. implementation set gradient zero case.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor\\frac{L_{} - \\mbox{kernel\\_size}}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"","code":"if (torch_is_installed()) { # power-2 pool of window of length 3, with stride 2. m <- nn_lp_pool1d(2, 3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"window, function computed :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"","code":"nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size size window stride stride window. Default value kernel_size ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"$$   f(X) = \\sqrt[p]{\\sum_{x \\X} x^{p}} $$ p = \\(\\infty\\), one gets Max Pooling p = 1, one gets Sum Pooling (proportional average pooling) parameters kernel_size, stride can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"sum power p zero, gradient function defined. implementation set gradient zero case.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{} - \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} - \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"","code":"if (torch_is_installed()) {  # power-2 pool of square window of size=3, stride=2 m <- nn_lp_pool2d(2, 3, stride = 2) # pool of non-square window of power 1.2 m <- nn_lp_pool2d(1.2, c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"element input sequence, layer computes following function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"","code":"nn_lstm(   input_size,   hidden_size,   num_layers = 1,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two LSTMs together form stacked LSTM, second LSTM taking outputs first LSTM computing final results. Default: 1 bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs LSTM layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional LSTM. Default: FALSE ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"$$ \\begin{array}{ll} \\\\ i_t = \\sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\\\ f_t = \\sigma(W_{} x_t + b_{} + W_{hf} h_{(t-1)} + b_{hf}) \\\\ g_t = \\tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{(t-1)} + b_{hg}) \\\\ o_t = \\sigma(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\\\ c_t = f_t c_{(t-1)} + i_t g_t \\\\ h_t = o_t \\tanh(c_t) \\\\ \\end{array} $$ \\(h_t\\) hidden state time t, \\(c_t\\) cell state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0, \\(i_t\\), \\(f_t\\), \\(g_t\\), \\(o_t\\) input, forget, cell, output gates, respectively. \\(\\sigma\\) sigmoid function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"Inputs: input, (h_0, c_0) input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() nn_utils_rnn_pack_sequence() details. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. c_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial cell state element batch. (h_0, c_0) provided, h_0 c_0 default zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"Outputs: output, (h_n, c_n) output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features (h_t) last layer LSTM, t. torch_nn.utils.rnn.PackedSequence given input, output also packed sequence. unpacked case, directions can separated using output$view(c(seq_len, batch, num_directions, hidden_size)), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len. Like output, layers can separated using h_n$view(c(num_layers, num_directions, batch, hidden_size)) similarly c_n. c_n (num_layers * num_directions, batch, hidden_size): tensor containing cell state t = seq_len","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"weight_ih_l[k] : learnable input-hidden weights \\(\\mbox{k}^{th}\\) layer (W_ii|W_if|W_ig|W_io), shape (4*hidden_size x input_size) weight_hh_l[k] : learnable hidden-hidden weights \\(\\mbox{k}^{th}\\) layer (W_hi|W_hf|W_hg|W_ho), shape (4*hidden_size x hidden_size) bias_ih_l[k] : learnable input-hidden bias \\(\\mbox{k}^{th}\\) layer (b_ii|b_if|b_ig|b_io), shape (4*hidden_size) bias_hh_l[k] : learnable hidden-hidden bias \\(\\mbox{k}^{th}\\) layer (b_hi|b_hf|b_hg|b_ho), shape (4*hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"","code":"if (torch_is_installed()) { rnn <- nn_lstm(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) c0 <- torch_randn(2, 3, 20) output <- rnn(input, list(h0, c0)) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Margin ranking loss — nn_margin_ranking_loss","title":"Margin ranking loss — nn_margin_ranking_loss","text":"Creates criterion measures loss given inputs \\(x1\\), \\(x2\\), two 1D mini-batch Tensors, label 1D mini-batch tensor \\(y\\) (containing 1 -1). \\(y = 1\\) assumed first input ranked higher (larger value) second input, vice-versa \\(y = -1\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margin ranking loss — nn_margin_ranking_loss","text":"","code":"nn_margin_ranking_loss(margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margin ranking loss — nn_margin_ranking_loss","text":"margin (float, optional): default value \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Margin ranking loss — nn_margin_ranking_loss","text":"loss function pair samples mini-batch : $$   \\mbox{loss}(x1, x2, y) = \\max(0, -y * (x1 - x2) + \\mbox{margin}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Margin ranking loss — nn_margin_ranking_loss","text":"Input1: \\((N)\\) N batch size. Input2: \\((N)\\), shape Input1. Target: \\((N)\\), shape inputs. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Margin ranking loss — nn_margin_ranking_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_margin_ranking_loss() input1 <- torch_randn(3, requires_grad = TRUE) input2 <- torch_randn(3, requires_grad = TRUE) target <- torch_randn(3)$sign() output <- loss(input1, input2, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxPool1D module — nn_max_pool1d","title":"MaxPool1D module — nn_max_pool1d","text":"Applies 1D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxPool1D module — nn_max_pool1d","text":"","code":"nn_max_pool1d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxPool1D module — nn_max_pool1d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful  nn_max_unpool1d() later. ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MaxPool1D module — nn_max_pool1d","text":"simplest case, output value layer input size \\((N, C, L)\\) output \\((N, C, L_{})\\) can precisely described : $$   (N_i, C_j, k) = \\max_{m=0, \\ldots, \\mbox{kernel\\_size} - 1} input(N_i, C_j, stride \\times k + m) $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link nice visualization dilation .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MaxPool1D module — nn_max_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor \\frac{L_{} + 2 \\times \\mbox{padding} - \\mbox{dilation}     \\times (\\mbox{kernel\\_size} - 1) - 1}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MaxPool1D module — nn_max_pool1d","text":"","code":"if (torch_is_installed()) { # pool of size=3, stride=2 m <- nn_max_pool1d(3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxPool2D module — nn_max_pool2d","title":"MaxPool2D module — nn_max_pool2d","text":"Applies 2D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxPool2D module — nn_max_pool2d","text":"","code":"nn_max_pool2d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxPool2D module — nn_max_pool2d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful nn_max_unpool2d() later. ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MaxPool2D module — nn_max_pool2d","text":"simplest case, output value layer input size \\((N, C, H, W)\\), output \\((N, C, H_{}, W_{})\\) kernel_size \\((kH, kW)\\) can precisely described : $$ \\begin{array}{ll} (N_i, C_j, h, w) ={} & \\max_{m=0, \\ldots, kH-1} \\max_{n=0, \\ldots, kW-1} \\\\ & \\mbox{input}(N_i, C_j, \\mbox{stride[0]} \\times h + m,                \\mbox{stride[1]} \\times w + n) \\end{array} $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link nice visualization dilation . parameters kernel_size, stride, padding, dilation can either : single int -- case value used height width dimension tuple two ints -- case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MaxPool2D module — nn_max_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 * \\mbox{padding[0]} - \\mbox{dilation[0]}     \\times (\\mbox{kernel\\_size[0]} - 1) - 1}{\\mbox{stride[0]}} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 * \\mbox{padding[1]} - \\mbox{dilation[1]}     \\times (\\mbox{kernel\\_size[1]} - 1) - 1}{\\mbox{stride[1]}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MaxPool2D module — nn_max_pool2d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_max_pool2d(3, stride = 2) # pool of non-square window m <- nn_max_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"simplest case, output value layer input size \\((N, C, D, H, W)\\), output \\((N, C, D_{}, H_{}, W_{})\\) kernel_size \\((kD, kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"","code":"nn_max_pool3d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added three sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful torch_nn.MaxUnpool3d later ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"$$ \\begin{array}{ll} \\mbox{}(N_i, C_j, d, h, w) = & \\max_{k=0, \\ldots, kD-1} \\max_{m=0, \\ldots, kH-1} \\max_{n=0, \\ldots, kW-1} \\\\  & \\mbox{input}(N_i, C_j, \\mbox{stride[0]} \\times d + k, \\mbox{stride[1]} \\times h + m, \\mbox{stride[2]} \\times w + n) \\end{array} $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link_ nice visualization dilation . parameters kernel_size, stride, padding, dilation can either : single int -- case value used depth, height width dimension tuple three ints -- case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0] \\times     (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1] \\times     (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] - \\mbox{dilation}[2] \\times     (\\mbox{kernel\\_size}[2] - 1) - 1}{\\mbox{stride}[2]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_max_pool3d(3, stride = 2) # pool of non-square window m <- nn_max_pool3d(c(3, 2, 2), stride = c(2, 1, 2)) input <- torch_randn(20, 16, 50, 44, 31) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"MaxPool1d fully invertible, since non-maximal values lost. MaxUnpool1d takes input output MaxPool1d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"","code":"nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"MaxPool1d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs Example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"input: input Tensor invert indices: indices given nn_max_pool1d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"Input: \\((N, C, H_{})\\) Output: \\((N, C, H_{})\\), $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{kernel\\_size}[0] $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"","code":"if (torch_is_installed()) { pool <- nn_max_pool1d(2, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool1d(2, stride = 2)  input <- torch_tensor(array(1:8 / 1, dim = c(1, 1, 8))) out <- pool(input) unpool(out[[1]], out[[2]])  # Example showcasing the use of output_size input <- torch_tensor(array(1:8 / 1, dim = c(1, 1, 8))) out <- pool(input) unpool(out[[1]], out[[2]], output_size = input$size()) unpool(out[[1]], out[[2]]) } #> torch_tensor #> (1,1,.,.) =  #>   0 #>   2 #>   0 #>   4 #>   0 #>   6 #>   0 #>   8 #> [ CPUFloatType{1,1,8,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"MaxPool2d fully invertible, since non-maximal values lost. MaxUnpool2d takes input output MaxPool2d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"","code":"nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"MaxPool2d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs Example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"input: input Tensor invert indices: indices given nn_max_pool2d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = (H_{} - 1) \\times \\mbox{stride[0]} - 2 \\times \\mbox{padding[0]} + \\mbox{kernel\\_size[0]} $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride[1]} - 2 \\times \\mbox{padding[1]} + \\mbox{kernel\\_size[1]} $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"","code":"if (torch_is_installed()) {  pool <- nn_max_pool2d(2, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool2d(2, stride = 2) input <- torch_randn(1, 1, 4, 4) out <- pool(input) unpool(out[[1]], out[[2]])  # specify a different output size than input size unpool(out[[1]], out[[2]], output_size = c(1, 1, 5, 5)) } #> torch_tensor #> (1,1,.,.) =  #>   0.0000  0.0000  0.6845  0.0000  0.6537 #>   0.0000  0.0000  0.0000  0.0000  0.0000 #>   0.0000  0.0000  0.0000  1.3880  0.0000 #>   1.7067  0.0000  0.0000  0.0000  0.0000 #>   0.0000  0.0000  0.0000  0.0000  0.0000 #> [ CPUFloatType{1,1,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"MaxPool3d fully invertible, since non-maximal values lost. MaxUnpool3d takes input output MaxPool3d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"","code":"nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"MaxPool3d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs section .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"input: input Tensor invert indices: indices given nn_max_pool3d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = (D_{} - 1) \\times \\mbox{stride[0]} - 2 \\times \\mbox{padding[0]} + \\mbox{kernel\\_size[0]} $$ $$   H_{} = (H_{} - 1) \\times \\mbox{stride[1]} - 2 \\times \\mbox{padding[1]} + \\mbox{kernel\\_size[1]} $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride[2]} - 2 \\times \\mbox{padding[2]} + \\mbox{kernel\\_size[2]} $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 pool <- nn_max_pool3d(3, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool3d(3, stride = 2) out <- pool(torch_randn(20, 16, 51, 33, 15)) unpooled_output <- unpool(out[[1]], out[[2]]) unpooled_output$size() } #> [1] 20 16 51 33 15"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for all neural network modules. — nn_module","title":"Base class for all neural network modules. — nn_module","text":"models also subclass class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for all neural network modules. — nn_module","text":"","code":"nn_module(   classname = NULL,   inherit = nn_Module,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for all neural network modules. — nn_module","text":"classname optional name module inherit optional module inherit ... methods implementation private passed R6::R6Class(). active passed R6::R6Class(). parent_env passed R6::R6Class().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Base class for all neural network modules. — nn_module","text":"Modules can also contain Modules, allowing nest tree structure. can assign submodules regular attributes. expected implement initialize forward create new nn_module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"initialize","dir":"Reference","previous_headings":"","what":"Initialize","title":"Base class for all neural network modules. — nn_module","text":"initialize function called whenever new instance nn_module created. use initialize functions define submodules parameters module. example:   initialize function can number parameters. objects assigned self$ available methods implement. Tensors wrapped nn_parameter() nn_buffer() submodules automatically tracked assigned self$. initialize function optional module defining weights, submodules buffers.","code":"initialize = function(input_size, output_size) {    self$conv1 <- nn_conv2d(input_size, output_size, 5)    self$conv2 <- nn_conv2d(output_size, output_size, 5) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"forward","dir":"Reference","previous_headings":"","what":"Forward","title":"Base class for all neural network modules. — nn_module","text":"forward method called whenever instance nn_module called. usually used implement computation module weights ad submodules defined initialize function. example:   forward function can use self$training attribute make different computations depending wether model training , example implementing dropout module.","code":"forward = function(input) {    input <- self$conv1(input)    input <- nnf_relu(input)    input <- self$conv2(input)    input <- nnf_relu(input)    input  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"cloning","dir":"Reference","previous_headings":"","what":"Cloning","title":"Base class for all neural network modules. — nn_module","text":"finalize cloning module, can define private finalize_deep_clone() method. method called cloned object deep-cloning module, modules, parameters buffers already cloned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for all neural network modules. — nn_module","text":"","code":"if (torch_is_installed()) { model <- nn_module(   initialize = function() {     self$conv1 <- nn_conv2d(1, 20, 5)     self$conv2 <- nn_conv2d(20, 20, 5)   },   forward = function(input) {     input <- self$conv1(input)     input <- nnf_relu(input)     input <- self$conv2(input)     input <- nnf_relu(input)     input   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":null,"dir":"Reference","previous_headings":"","what":"Container that allows named values — nn_module_dict","title":"Container that allows named values — nn_module_dict","text":"Container allows named values","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Container that allows named values — nn_module_dict","text":"","code":"nn_module_dict(dict)"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Container that allows named values — nn_module_dict","text":"dict named list submodules saved module.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Container that allows named values — nn_module_dict","text":"","code":"if (torch_is_installed()) { nn_module <- nn_module(   initialize = function() {     self$dict <- nn_module_dict(list(       l1 = nn_linear(10, 20),       l2 = nn_linear(20, 10)     ))   },   forward = function(x) {     x <- self$dict$l1(x)     self$dict$l2(x)   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Holds submodules in a list. — nn_module_list","title":"Holds submodules in a list. — nn_module_list","text":"nn_module_list can indexed like regular R list, modules contains properly registered, visible nn_module methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Holds submodules in a list. — nn_module_list","text":"","code":"nn_module_list(modules = list())"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Holds submodules in a list. — nn_module_list","text":"modules list modules add","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Holds submodules in a list. — nn_module_list","text":"","code":"if (torch_is_installed()) {  my_module <- nn_module(   initialize = function() {     self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))   },   forward = function(x) {     for (i in 1:length(self$linears)) {       x <- self$linears[[i]](x)     }     x   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"MSE loss — nn_mse_loss","title":"MSE loss — nn_mse_loss","text":"Creates criterion measures mean squared error (squared L2 norm) element input \\(x\\) target \\(y\\). unreduced (.e. reduction set 'none') loss can described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSE loss — nn_mse_loss","text":"","code":"nn_mse_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSE loss — nn_mse_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSE loss — nn_mse_loss","text":"$$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = \\left( x_n - y_n \\right)^2, $$ \\(N\\) batch size. reduction 'none' (default 'mean'), : $$   \\ell(x, y) =   \\begin{array}{ll} \\mbox{mean}(L), &  \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  &  \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(x\\) \\(y\\) tensors arbitrary shapes total \\(n\\) elements . mean operation still operates elements, divides \\(n\\). division \\(n\\) can avoided one sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MSE loss — nn_mse_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSE loss — nn_mse_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_mse_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randn(3, 5) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi margin loss — nn_multi_margin_loss","title":"Multi margin loss — nn_multi_margin_loss","text":"Creates criterion optimizes multi-class classification hinge loss (margin-based loss) input \\(x\\) (2D mini-batch Tensor) output \\(y\\) (1D tensor target class indices, \\(0 \\leq y \\leq \\mbox{x.size}(1)-1\\)):","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi margin loss — nn_multi_margin_loss","text":"","code":"nn_multi_margin_loss(p = 1, margin = 1, weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi margin loss — nn_multi_margin_loss","text":"p (int, optional): default value \\(1\\). \\(1\\) \\(2\\) supported values. margin (float, optional): default value \\(1\\). weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi margin loss — nn_multi_margin_loss","text":"mini-batch sample, loss terms 1D input \\(x\\) scalar output \\(y\\) : $$   \\mbox{loss}(x, y) = \\frac{\\sum_i \\max(0, \\mbox{margin} - x[y] + x[]))^p}{\\mbox{x.size}(0)} $$ \\(x \\\\left\\{0, \\; \\cdots , \\; \\mbox{x.size}(0) - 1\\right\\}\\) \\(\\neq y\\). Optionally, can give non-equal weighting classes passing 1D weight tensor constructor. loss function becomes: $$   \\mbox{loss}(x, y) = \\frac{\\sum_i \\max(0, w[y] * (\\mbox{margin} - x[y] + x[]))^p)}{\\mbox{x.size}(0)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"MultiHead attention — nn_multihead_attention","title":"MultiHead attention — nn_multihead_attention","text":"Allows model jointly attend information different representation subspaces. See reference: Attention Need","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MultiHead attention — nn_multihead_attention","text":"","code":"nn_multihead_attention(   embed_dim,   num_heads,   dropout = 0,   bias = TRUE,   add_bias_kv = FALSE,   add_zero_attn = FALSE,   kdim = NULL,   vdim = NULL,   batch_first = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MultiHead attention — nn_multihead_attention","text":"embed_dim total dimension model. num_heads parallel attention heads. Note embed_dim split across num_heads (.e. head dimension embed_dim %/% num_heads). dropout Dropout layer attn_output_weights. Default: 0.0. bias add bias module parameter. Default: True. add_bias_kv add bias key value sequences dim=0. add_zero_attn add new batch zeros key value sequences dim=1. kdim total number features key. Default: NULL vdim total number features value. Default: NULL. Note: kdim vdim NULL, set embed_dim query, key, value number features. batch_first TRUE input output tensors \\((N,   S, E)\\) instead \\((S, N, E)\\), N batch size, S sequence length, E embedding dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MultiHead attention — nn_multihead_attention","text":"$$ \\mbox{MultiHead}(Q, K, V) = \\mbox{Concat}(head_1,\\dots,head_h)W^O \\mbox{} head_i = \\mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MultiHead attention — nn_multihead_attention","text":"Inputs: query: \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. (see batch_first argument) key: \\((S, N, E)\\), S source sequence length, N batch size, E embedding dimension. (see batch_first argument) value: \\((S, N, E)\\) S source sequence length, N batch size, E embedding dimension. (see batch_first argument) key_padding_mask: \\((N, S)\\) N batch size, S source sequence length. ByteTensor provided, non-zero positions ignored position zero positions unchanged. BoolTensor provided, positions value True ignored position value False unchanged. attn_mask: 2D mask \\((L, S)\\) L target sequence length, S source sequence length. 3D mask \\((N*num_heads, L, S)\\) N batch size, L target sequence length, S source sequence length. attn_mask ensure position allowed attend unmasked positions. ByteTensor provided, non-zero positions allowed attend zero positions unchanged. BoolTensor provided, positions True allowed attend False values unchanged. FloatTensor provided, added attention weight. Outputs: attn_output: \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. (see  batch_first argument) attn_output_weights: avg_weights TRUE (default), output attention weights averaged attention heads, giving tensor shape \\((N, L, S)\\) N batch size, L target sequence length, S source sequence length. avg_weights FALSE, attention weight tensor output -, shape \\((N, H, L, S)\\), H number attention heads.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MultiHead attention — nn_multihead_attention","text":"","code":"if (torch_is_installed()) { if (FALSE) { multihead_attn <- nn_multihead_attention(embed_dim, num_heads) out <- multihead_attn(query, key, value) attn_output <- out[[1]] attn_output_weights <- out[[2]] }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel margin loss — nn_multilabel_margin_loss","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"Creates criterion optimizes multi-class multi-classification hinge loss (margin-based loss) input \\(x\\) (2D mini-batch Tensor) output \\(y\\) (2D Tensor target class indices). sample mini-batch:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"","code":"nn_multilabel_margin_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"$$   \\mbox{loss}(x, y) = \\sum_{ij}\\frac{\\max(0, 1 - (x[y[j]] - x[]))}{\\mbox{x.size}(0)} $$ \\(x \\\\left\\{0, \\; \\cdots , \\; \\mbox{x.size}(0) - 1\\right\\}\\), \\ \\(y \\\\left\\{0, \\; \\cdots , \\; \\mbox{y.size}(0) - 1\\right\\}\\), \\ \\(0 \\leq y[j] \\leq \\mbox{x.size}(0)-1\\), \\ \\(\\neq y[j]\\) \\(\\) \\(j\\). \\(y\\) \\(x\\) must size. criterion considers contiguous block non-negative targets starts front. allows different samples variable amounts target classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"Input: \\((C)\\) \\((N, C)\\) N batch size C number classes. Target: \\((C)\\) \\((N, C)\\), label targets padded -1 ensuring shape input. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_multilabel_margin_loss() x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1, 4)) # for target y, only consider labels 4 and 1, not after label -1 y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1, 4)) loss(x, y) } #> torch_tensor #> 0.85 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"Creates criterion optimizes multi-label one-versus-loss based max-entropy, input \\(x\\) target \\(y\\) size \\((N, C)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"","code":"nn_multilabel_soft_margin_loss(weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"sample minibatch: $$   loss(x, y) = - \\frac{1}{C} * \\sum_i y[] * \\log((1 + \\exp(-x[]))^{-1}) + (1-y[]) * \\log\\left(\\frac{\\exp(-x[])}{(1 + \\exp(-x[]))}\\right) $$ \\(\\\\left\\{0, \\; \\cdots , \\; \\mbox{x.nElement}() - 1\\right\\}\\), \\(y[] \\\\left\\{0, \\; 1\\right\\}\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"Input: \\((N, C)\\) N batch size C number classes. Target: \\((N, C)\\), label targets padded -1 ensuring shape input. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Nll loss — nn_nll_loss","title":"Nll loss — nn_nll_loss","text":"negative log likelihood loss. useful train classification problem C classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nll loss — nn_nll_loss","text":"","code":"nn_nll_loss(weight = NULL, ignore_index = -100, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nll loss — nn_nll_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. ignore_index (int, optional): Specifies target value ignored contribute input gradient. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': weighted mean output taken, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nll loss — nn_nll_loss","text":"provided, optional argument weight 1D Tensor assigning weight classes. particularly useful unbalanced training set. input given forward call expected contain log-probabilities class. input Tensor size either \\((minibatch, C)\\) \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional case (described later). Obtaining log-probabilities neural network easily achieved adding  LogSoftmax  layer last layer network. may use CrossEntropyLoss instead, prefer add extra layer. target loss expects class index range \\([0, C-1]\\) C = number classes; ignore_index specified, loss also accepts class index (index may necessarily class range). unreduced (.e. reduction set 'none') loss can described : $$ \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_{y_n} x_{n,y_n}, \\quad w_{c} = \\mbox{weight}[c] \\cdot \\mbox{1}\\{c \\= \\mbox{ignore\\_index}\\}, $$ \\(x\\) input, \\(y\\) target, \\(w\\) weight, \\(N\\) batch size. reduction 'none' (default 'mean'), $$ \\ell(x, y) = \\begin{array}{ll} \\sum_{n=1}^N \\frac{1}{\\sum_{n=1}^N w_{y_n}} l_n, &   \\mbox{reduction} = \\mbox{'mean';}\\\\ \\sum_{n=1}^N l_n,  &   \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ Can also used higher dimension inputs, 2D images, providing input size \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\), \\(K\\) number dimensions, target appropriate shape (see ). case images, computes NLL loss per-pixel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Nll loss — nn_nll_loss","text":"Input: \\((N, C)\\) C = number classes, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Target: \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Output: scalar. reduction 'none', size target: \\((N)\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nll loss — nn_nll_loss","text":"","code":"if (torch_is_installed()) { m <- nn_log_softmax(dim = 2) loss <- nn_nll_loss() # input is of size N x C = 3 x 5 input <- torch_randn(3, 5, requires_grad = TRUE) # each element in target has to have 0 <= value < C target <- torch_tensor(c(2, 1, 5), dtype = torch_long()) output <- loss(m(input), target) output$backward()  # 2D loss example (used, for example, with image inputs) N <- 5 C <- 4 loss <- nn_nll_loss() # input is of size N x C x height x width data <- torch_randn(N, 16, 10, 10) conv <- nn_conv2d(16, C, c(3, 3)) m <- nn_log_softmax(dim = 1) # each element in target has to have 0 <= value < C target <- torch_empty(N, 8, 8, dtype = torch_long())$random_(1, C) output <- loss(m(conv(data)), target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise distance — nn_pairwise_distance","title":"Pairwise distance — nn_pairwise_distance","text":"Computes batchwise pairwise distance vectors \\(v_1\\), \\(v_2\\) using p-norm:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise distance — nn_pairwise_distance","text":"","code":"nn_pairwise_distance(p = 2, eps = 1e-06, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise distance — nn_pairwise_distance","text":"p (real): norm degree. Default: 2 eps (float, optional): Small value avoid division zero. Default: 1e-6 keepdim (bool, optional): Determines whether keep vector dimension. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pairwise distance — nn_pairwise_distance","text":"$$  \\Vert x \\Vert _p = \\left( \\sum_{=1}^n  \\vert x_i \\vert ^ p \\right) ^ {1/p}. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Pairwise distance — nn_pairwise_distance","text":"Input1: \\((N, D)\\) D = vector dimension Input2: \\((N, D)\\), shape Input1 Output: \\((N)\\). keepdim TRUE, \\((N, 1)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise distance — nn_pairwise_distance","text":"","code":"if (torch_is_installed()) { pdist <- nn_pairwise_distance(p = 2) input1 <- torch_randn(100, 128) input2 <- torch_randn(100, 128) output <- pdist(input1, input2) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an nn_parameter — nn_parameter","title":"Creates an nn_parameter — nn_parameter","text":"Indicates nn_module x parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an nn_parameter — nn_parameter","text":"","code":"nn_parameter(x, requires_grad = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an nn_parameter — nn_parameter","text":"x tensor want indicate parameter requires_grad whether parameter  requires_grad = TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson NLL loss — nn_poisson_nll_loss","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"Negative log likelihood loss Poisson distribution target. loss can described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"","code":"nn_poisson_nll_loss(   log_input = TRUE,   full = FALSE,   eps = 1e-08,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"log_input (bool, optional): TRUE loss computed \\(\\exp(\\mbox{input}) - \\mbox{target}*\\mbox{input}\\), FALSE loss \\(\\mbox{input} - \\mbox{target}*\\log(\\mbox{input}+\\mbox{eps})\\). full (bool, optional): whether compute full loss, . e. add Stirling approximation term \\(\\mbox{target}*\\log(\\mbox{target}) - \\mbox{target} + 0.5 * \\log(2\\pi\\mbox{target})\\). eps (float, optional): Small value avoid evaluation \\(\\log(0)\\) log_input = FALSE. Default: 1e-8 reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"$$ \\mbox{target} \\sim \\mathrm{Poisson}(\\mbox{input}) \\mbox{loss}(\\mbox{input}, \\mbox{target}) = \\mbox{input} - \\mbox{target} * \\log(\\mbox{input}) + \\log(\\mbox{target!}) $$ last term can omitted approximated Stirling formula. approximation used target values 1. targets less equal 1 zeros added loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar default. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_poisson_nll_loss() log_input <- torch_randn(5, 2, requires_grad = TRUE) target <- torch_randn(5, 2) output <- loss(log_input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":null,"dir":"Reference","previous_headings":"","what":"PReLU module — nn_prelu","title":"PReLU module — nn_prelu","text":"Applies element-wise function: $$   \\mbox{PReLU}(x) = \\max(0,x) + * \\min(0,x) $$ $$   \\mbox{PReLU}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x \\geq 0 \\\\ ax, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PReLU module — nn_prelu","text":"","code":"nn_prelu(num_parameters = 1, init = 0.25)"},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PReLU module — nn_prelu","text":"num_parameters (int): number \\(\\) learn. Although takes int input, two values legitimate: 1, number channels input. Default: 1 init (float): initial value \\(\\). Default: 0.25","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PReLU module — nn_prelu","text":"\\(\\) learnable parameter. called without arguments, nn.prelu() uses single parameter \\(\\) across input channels. called nn_prelu(nChannels), separate \\(\\) used input channel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"PReLU module — nn_prelu","text":"weight decay used learning \\(\\) good performance. Channel dim 2nd dim input. input dims < 2, channel dim number channels = 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"PReLU module — nn_prelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"PReLU module — nn_prelu","text":"weight (Tensor): learnable weights shape (num_parameters).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PReLU module — nn_prelu","text":"","code":"if (torch_is_installed()) { m <- nn_prelu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune top layer(s) of a network — nn_prune_head","title":"Prune top layer(s) of a network — nn_prune_head","text":"Prune head_size last layers nn_module order replace head, order use pruned module sequential embedding module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune top layer(s) of a network — nn_prune_head","text":"","code":"nn_prune_head(x, head_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune top layer(s) of a network — nn_prune_head","text":"x nn_network prune head_size number nn_layers prune","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune top layer(s) of a network — nn_prune_head","text":"nn_sequential network top nn_layer removed","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prune top layer(s) of a network — nn_prune_head","text":"","code":"if (torch_is_installed()) { if (torch_is_installed()) { x <- nn_sequential(   nn_relu(),   nn_tanh(),   nn_relu6(),   nn_relu(),   nn_linear(2,10),   nn_batch_norm1d(10),   nn_tanh(),   nn_linear(10,3) ) prune <- nn_prune_head(x, 3) prune } } #> An `nn_module` containing 30 parameters. #>  #> ── Modules ───────────────────────────────────────────────────────────────────── #> • 0: <nn_relu> #0 parameters #> • 1: <nn_tanh> #0 parameters #> • 2: <nn_relu6> #0 parameters #> • 3: <nn_relu> #0 parameters #> • 4: <nn_linear> #30 parameters"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"ReLU module — nn_relu","title":"ReLU module — nn_relu","text":"Applies rectified linear unit function element-wise $$\\mbox{ReLU}(x) = (x)^+ = \\max(0, x)$$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReLU module — nn_relu","text":"","code":"nn_relu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ReLU module — nn_relu","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ReLU module — nn_relu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ReLU module — nn_relu","text":"","code":"if (torch_is_installed()) { m <- nn_relu() input <- torch_randn(2) m(input) } #> torch_tensor #>  0.2521 #>  0.0000 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"ReLu6 module — nn_relu6","title":"ReLu6 module — nn_relu6","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReLu6 module — nn_relu6","text":"","code":"nn_relu6(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ReLu6 module — nn_relu6","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ReLu6 module — nn_relu6","text":"$$   \\mbox{ReLU6}(x) = \\min(\\max(0,x), 6) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ReLu6 module — nn_relu6","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ReLu6 module — nn_relu6","text":"","code":"if (torch_is_installed()) { m <- nn_relu6() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"RNN module — nn_rnn","title":"RNN module — nn_rnn","text":"Applies multi-layer Elman RNN \\(\\tanh\\) \\(\\mbox{ReLU}\\) non-linearity input sequence.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RNN module — nn_rnn","text":"","code":"nn_rnn(   input_size,   hidden_size,   num_layers = 1,   nonlinearity = NULL,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RNN module — nn_rnn","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two RNNs together form stacked RNN, second RNN taking outputs first RNN computing final results. Default: 1 nonlinearity non-linearity use. Can either 'tanh' 'relu'. Default: 'tanh' bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs RNN layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional RNN. Default: FALSE ... arguments can passed super class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RNN module — nn_rnn","text":"element input sequence, layer computes following function: $$ h_t = \\tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh}) $$ \\(h_t\\) hidden state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0. nonlinearity 'relu', \\(\\mbox{ReLU}\\) used instead \\(\\tanh\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"RNN module — nn_rnn","text":"input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. Defaults zero provided. RNN bidirectional, num_directions 2, else 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"RNN module — nn_rnn","text":"output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features (h_t) last layer RNN, t.  :class:nn_packed_sequence given input, output also packed sequence. unpacked case, directions can separated using output$view(seq_len, batch, num_directions, hidden_size), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len. Like output, layers can separated using h_n$view(num_layers, num_directions, batch, hidden_size).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"RNN module — nn_rnn","text":"Input1: \\((L, N, H_{})\\) tensor containing input features \\(H_{}=\\mbox{input\\_size}\\) L represents sequence length. Input2: \\((S, N, H_{})\\) tensor containing initial hidden state element batch. \\(H_{}=\\mbox{hidden\\_size}\\) Defaults zero provided. \\(S=\\mbox{num\\_layers} * \\mbox{num\\_directions}\\) RNN bidirectional, num_directions 2, else 1. Output1: \\((L, N, H_{})\\) \\(H_{}=\\mbox{num\\_directions} * \\mbox{hidden\\_size}\\) Output2: \\((S, N, H_{})\\) tensor containing next hidden state element batch","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"RNN module — nn_rnn","text":"weight_ih_l[k]: learnable input-hidden weights k-th layer, shape (hidden_size, input_size) k = 0. Otherwise, shape (hidden_size, num_directions * hidden_size) weight_hh_l[k]: learnable hidden-hidden weights k-th layer, shape (hidden_size, hidden_size) bias_ih_l[k]: learnable input-hidden bias k-th layer, shape (hidden_size) bias_hh_l[k]: learnable hidden-hidden bias k-th layer, shape (hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RNN module — nn_rnn","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RNN module — nn_rnn","text":"","code":"if (torch_is_installed()) { rnn <- nn_rnn(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) rnn(input, h0) } #> [[1]] #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 9  0.6194  0.1261 -0.5859 -0.5130  0.2639  0.2436  0.7532 -0.1778 -0.1698 #>  -0.7915 -0.2606  0.5423 -0.2332 -0.5692  0.0797 -0.3458 -0.2994 -0.4752 #>   0.7498 -0.1321 -0.2842  0.4756 -0.1615  0.2806  0.3849  0.2648 -0.1627 #>  #> Columns 10 to 18 -0.4432 -0.2098 -0.7064 -0.0722 -0.5759 -0.7316  0.3305 -0.2668  0.0912 #>   0.3085 -0.2419  0.3502  0.3794 -0.2719  0.7153  0.0670  0.6914  0.6160 #>  -0.0390  0.7705  0.9537  0.7160 -0.5791  0.8475 -0.8662  0.0169 -0.3920 #>  #> Columns 19 to 20 -0.1107  0.3902 #>   0.4782 -0.1679 #>  -0.9254  0.8202 #>  #> (2,.,.) =  #>  Columns 1 to 9 -0.2733  0.5807 -0.5705 -0.2137 -0.1263  0.2720  0.6784  0.3367  0.2812 #>  -0.2298  0.4555 -0.4291  0.0732  0.4738 -0.2003  0.2658  0.3731 -0.0465 #>  -0.2809  0.1940 -0.0990  0.2505 -0.0127 -0.4197  0.1765  0.8446 -0.0952 #>  #> Columns 10 to 18  0.2151  0.3480  0.2816  0.6208 -0.4694 -0.2574 -0.3221  0.7333 -0.4520 #>  -0.2748 -0.2761 -0.7137  0.4835 -0.1636 -0.6345 -0.0590  0.2484  0.0123 #>  -0.5653  0.5783 -0.5571  0.2605  0.1348 -0.0049  0.2231  0.4494  0.2747 #>  #> Columns 19 to 20  0.4467  0.2031 #>  -0.3049  0.3067 #>  -0.0243  0.4457 #>  #> (3,.,.) =  #>  Columns 1 to 9 -0.4587  0.5419 -0.3534 -0.4045 -0.4276 -0.2290  0.0918  0.5118 -0.5355 #>  -0.1085  0.3107 -0.3817 -0.3413 -0.4206 -0.2209  0.3014  0.1557 -0.1130 #>   0.2597  0.2839 -0.4300  0.1852  0.0349  0.0473  0.3646 -0.3881  0.4646 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{5,3,20} ][ grad_fn = <StackBackward0> ] #>  #> [[2]] #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 9  0.0086 -0.2831 -0.5776  0.0057 -0.0709  0.6271  0.5970  0.2080  0.1378 #>   0.0209 -0.5796 -0.3668  0.0877 -0.3049  0.4987  0.4293 -0.7983 -0.5304 #>   0.2519 -0.3285  0.2158 -0.1921  0.4215  0.2205  0.2465 -0.4121 -0.2367 #>  #> Columns 10 to 18  0.8283  0.0237  0.4405  0.3959  0.7915 -0.3492 -0.5114  0.2015  0.4390 #>   0.4698 -0.1859  0.3392  0.6283  0.7378  0.7038  0.3066  0.7614 -0.0596 #>  -0.0976  0.1275  0.5496  0.4558 -0.0012  0.2623  0.1556 -0.0674 -0.4739 #>  #> Columns 19 to 20  0.4836  0.6168 #>  -0.4734  0.4156 #>  -0.2577  0.4979 #>  #> (2,.,.) =  #>  Columns 1 to 9 -0.1114  0.3486 -0.4200 -0.1580 -0.5198 -0.3118  0.2938  0.5197 -0.0018 #>   0.2732  0.4590 -0.4445  0.2851 -0.4892  0.0292  0.2732  0.1335  0.0589 #>   0.3064  0.3742 -0.6699 -0.2006 -0.5265 -0.1959  0.4728  0.1926 -0.0590 #>  #> Columns 10 to 18  0.0210  0.3693 -0.2735  0.4198 -0.6618 -0.2076 -0.0963  0.3921 -0.5134 #>   0.1978  0.4044 -0.1352  0.3989 -0.5551 -0.5138  0.0447  0.2919 -0.1836 #>  -0.1193 -0.0102  0.0760  0.4765 -0.0831 -0.3886  0.2522  0.4670 -0.1084 #>  #> Columns 19 to 20 -0.0799  0.3596 #>   0.2372  0.5238 #>   0.2247  0.4753 #> [ CPUFloatType{2,3,20} ][ grad_fn = <StackBackward0> ] #>"},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":null,"dir":"Reference","previous_headings":"","what":"RReLU module — nn_rrelu","title":"RReLU module — nn_rrelu","text":"Applies randomized leaky rectified liner unit function, element-wise, described paper:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RReLU module — nn_rrelu","text":"","code":"nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RReLU module — nn_rrelu","text":"lower lower bound uniform distribution. Default: \\(\\frac{1}{8}\\) upper upper bound uniform distribution. Default: \\(\\frac{1}{3}\\) inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RReLU module — nn_rrelu","text":"Empirical Evaluation Rectified Activations Convolutional Network. function defined : $$ \\mbox{RReLU}(x) = \\left\\{ \\begin{array}{ll} x & \\mbox{} x \\geq 0 \\\\ ax & \\mbox{ otherwise } \\end{array} \\right. $$ \\(\\) randomly sampled uniform distribution \\(\\mathcal{U}(\\mbox{lower}, \\mbox{upper})\\). See: https://arxiv.org/pdf/1505.00853.pdf","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"RReLU module — nn_rrelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RReLU module — nn_rrelu","text":"","code":"if (torch_is_installed()) { m <- nn_rrelu(0.1, 0.3) input <- torch_randn(2) m(input) } #> torch_tensor #> 0.01 * #> -1.9460 #> -29.5220 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"SELU module — nn_selu","title":"SELU module — nn_selu","text":"Applied element-wise, :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SELU module — nn_selu","text":"","code":"nn_selu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SELU module — nn_selu","text":"inplace (bool, optional): can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SELU module — nn_selu","text":"$$   \\mbox{SELU}(x) = \\mbox{scale} * (\\max(0,x) + \\min(0, \\alpha * (\\exp(x) - 1))) $$ \\(\\alpha = 1.6732632423543772848170429916717\\) \\(\\mbox{scale} = 1.0507009873554804934193349852946\\). details can found paper Self-Normalizing Neural Networks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"SELU module — nn_selu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SELU module — nn_selu","text":"","code":"if (torch_is_installed()) { m <- nn_selu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"A sequential container — nn_sequential","title":"A sequential container — nn_sequential","text":"sequential container. Modules added order passed constructor. See examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sequential container — nn_sequential","text":"","code":"nn_sequential(...)"},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A sequential container — nn_sequential","text":"... sequence modules added","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sequential container — nn_sequential","text":"","code":"if (torch_is_installed()) {  model <- nn_sequential(   nn_conv2d(1, 20, 5),   nn_relu(),   nn_conv2d(20, 64, 5),   nn_relu() ) input <- torch_randn(32, 1, 28, 28) output <- model(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid module — nn_sigmoid","title":"Sigmoid module — nn_sigmoid","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid module — nn_sigmoid","text":"","code":"nn_sigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sigmoid module — nn_sigmoid","text":"$$   \\mbox{Sigmoid}(x) = \\sigma(x) = \\frac{1}{1 + \\exp(-x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Sigmoid module — nn_sigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid module — nn_sigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_sigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"Applies Sigmoid Linear Unit (SiLU) function, element-wise. SiLU function also known swish function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"","code":"nn_silu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"See Gaussian Error Linear Units (GELUs) SiLU (Sigmoid Linear Unit) originally coined, see Sigmoid-Weighted Linear Units Neural Network Function Approximation Reinforcement Learning Swish: Self-Gated Activation Function SiLU experimented later.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth L1 loss — nn_smooth_l1_loss","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"Creates criterion uses squared term absolute element-wise error falls 1 L1 term otherwise. less sensitive outliers MSELoss cases prevents exploding gradients (e.g. see Fast R-CNN paper Ross Girshick). Also known Huber loss:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"","code":"nn_smooth_l1_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"$$   \\mbox{loss}(x, y) = \\frac{1}{n} \\sum_{} z_{} $$ \\(z_{}\\) given : $$   z_{} =   \\begin{array}{ll} 0.5 (x_i - y_i)^2, & \\mbox{} |x_i - y_i| < 1 \\\\ |x_i - y_i| - 0.5, & \\mbox{otherwise } \\end{array} $$ \\(x\\) \\(y\\) arbitrary shapes total \\(n\\) elements sum operation still operates elements, divides \\(n\\). division \\(n\\) can avoided sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Soft margin loss — nn_soft_margin_loss","title":"Soft margin loss — nn_soft_margin_loss","text":"Creates criterion optimizes two-class classification logistic loss input tensor \\(x\\) target tensor \\(y\\) (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soft margin loss — nn_soft_margin_loss","text":"","code":"nn_soft_margin_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soft margin loss — nn_soft_margin_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soft margin loss — nn_soft_margin_loss","text":"$$   \\mbox{loss}(x, y) = \\sum_i \\frac{\\log(1 + \\exp(-y[]*x[]))}{\\mbox{x.nelement}()} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Soft margin loss — nn_soft_margin_loss","text":"Input: \\((*)\\) \\(*\\) means, number additional dimensions Target: \\((*)\\), shape input Output: scalar. reduction 'none', shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax module — nn_softmax","title":"Softmax module — nn_softmax","text":"Applies Softmax function n-dimensional input Tensor rescaling elements n-dimensional output Tensor lie range [0,1] sum 1. Softmax defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax module — nn_softmax","text":"","code":"nn_softmax(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax module — nn_softmax","text":"dim (int): dimension along Softmax computed (every slice along dim sum 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax module — nn_softmax","text":": Tensor dimension shape input values range [0, 1]","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmax module — nn_softmax","text":"$$   \\mbox{Softmax}(x_{}) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)} $$ input Tensor sparse tensor unspecifed values treated -Inf.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Softmax module — nn_softmax","text":"module work directly NLLLoss, expects Log computed Softmax . Use LogSoftmax instead (faster better numerical properties).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmax module — nn_softmax","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax module — nn_softmax","text":"","code":"if (torch_is_installed()) { m <- nn_softmax(1) input <- torch_randn(2, 3) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax2d module — nn_softmax2d","title":"Softmax2d module — nn_softmax2d","text":"Applies SoftMax features spatial location. given image Channels x Height x Width, apply Softmax location \\((Channels, h_i, w_j)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax2d module — nn_softmax2d","text":"","code":"nn_softmax2d()"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax2d module — nn_softmax2d","text":"Tensor dimension shape input values range [0, 1]","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmax2d module — nn_softmax2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax2d module — nn_softmax2d","text":"","code":"if (torch_is_installed()) { m <- nn_softmax2d() input <- torch_randn(2, 3, 12, 13) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmin — nn_softmin","title":"Softmin — nn_softmin","text":"Applies Softmin function n-dimensional input Tensor rescaling elements n-dimensional output Tensor lie range [0, 1] sum 1. Softmin defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmin — nn_softmin","text":"","code":"nn_softmin(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmin — nn_softmin","text":"dim (int): dimension along Softmin computed (every slice along dim sum 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmin — nn_softmin","text":"Tensor dimension shape input, values range [0, 1].","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmin — nn_softmin","text":"$$   \\mbox{Softmin}(x_{}) = \\frac{\\exp(-x_i)}{\\sum_j \\exp(-x_j)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmin — nn_softmin","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmin — nn_softmin","text":"","code":"if (torch_is_installed()) { m <- nn_softmin(dim = 1) input <- torch_randn(2, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus module — nn_softplus","title":"Softplus module — nn_softplus","text":"Applies element-wise function: $$   \\mbox{Softplus}(x) = \\frac{1}{\\beta} * \\log(1 + \\exp(\\beta * x)) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus module — nn_softplus","text":"","code":"nn_softplus(beta = 1, threshold = 20)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus module — nn_softplus","text":"beta \\(\\beta\\) value Softplus formulation. Default: 1 threshold values revert linear function. Default: 20","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softplus module — nn_softplus","text":"SoftPlus smooth approximation ReLU function can used constrain output machine always positive. numerical stability implementation reverts linear function \\(input \\times \\beta > threshold\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softplus module — nn_softplus","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus module — nn_softplus","text":"","code":"if (torch_is_installed()) { m <- nn_softplus() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Softshrink module — nn_softshrink","title":"Softshrink module — nn_softshrink","text":"Applies soft shrinkage function elementwise:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softshrink module — nn_softshrink","text":"","code":"nn_softshrink(lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softshrink module — nn_softshrink","text":"lambd \\(\\lambda\\) (must less zero) value Softshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softshrink module — nn_softshrink","text":"$$   \\mbox{SoftShrinkage}(x) =   \\left\\{ \\begin{array}{ll} x - \\lambda, & \\mbox{ } x > \\lambda \\\\ x + \\lambda, & \\mbox{ } x < -\\lambda \\\\ 0, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softshrink module — nn_softshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softshrink module — nn_softshrink","text":"","code":"if (torch_is_installed()) { m <- nn_softshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign module — nn_softsign","title":"Softsign module — nn_softsign","text":"Applies element-wise function: $$   \\mbox{SoftSign}(x) = \\frac{x}{ 1 + |x|} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign module — nn_softsign","text":"","code":"nn_softsign()"},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softsign module — nn_softsign","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign module — nn_softsign","text":"","code":"if (torch_is_installed()) { m <- nn_softsign() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanh module — nn_tanh","title":"Tanh module — nn_tanh","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanh module — nn_tanh","text":"","code":"nn_tanh()"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tanh module — nn_tanh","text":"$$   \\mbox{Tanh}(x) = \\tanh(x) = \\frac{\\exp(x) - \\exp(-x)} {\\exp(x) + \\exp(-x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Tanh module — nn_tanh","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanh module — nn_tanh","text":"","code":"if (torch_is_installed()) { m <- nn_tanh() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanhshrink module — nn_tanhshrink","title":"Tanhshrink module — nn_tanhshrink","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanhshrink module — nn_tanhshrink","text":"","code":"nn_tanhshrink()"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tanhshrink module — nn_tanhshrink","text":"$$   \\mbox{Tanhshrink}(x) = x - \\tanh(x) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Tanhshrink module — nn_tanhshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanhshrink module — nn_tanhshrink","text":"","code":"if (torch_is_installed()) { m <- nn_tanhshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold module — nn_threshold","title":"Threshold module — nn_threshold","text":"Thresholds element input Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold module — nn_threshold","text":"","code":"nn_threshold(threshold, value, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold module — nn_threshold","text":"threshold value threshold value value replace inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Threshold module — nn_threshold","text":"Threshold defined : $$   y =   \\left\\{ \\begin{array}{ll}   x, &\\mbox{ } x > \\mbox{threshold} \\\\   \\mbox{value}, &\\mbox{ otherwise }   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Threshold module — nn_threshold","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Threshold module — nn_threshold","text":"","code":"if (torch_is_installed()) { m <- nn_threshold(0.1, 20) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin loss — nn_triplet_margin_loss","title":"Triplet margin loss — nn_triplet_margin_loss","text":"Creates criterion measures triplet loss given input tensors \\(x1\\), \\(x2\\), \\(x3\\) margin value greater \\(0\\). used measuring relative similarity samples. triplet composed , p n (.e., anchor, positive examples negative examples respectively). shapes input tensors \\((N, D)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin loss — nn_triplet_margin_loss","text":"","code":"nn_triplet_margin_loss(   margin = 1,   p = 2,   eps = 1e-06,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin loss — nn_triplet_margin_loss","text":"margin (float, optional): Default: \\(1\\). p (int, optional): norm degree pairwise distance. Default: \\(2\\). eps constant avoid NaN's swap (bool, optional): distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triplet margin loss — nn_triplet_margin_loss","text":"distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. loss function sample mini-batch : $$   L(, p, n) = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\} $$ $$   d(x_i, y_i) = | {\\bf x}_i - {\\bf y}_i |_p $$ See also nn_triplet_margin_with_distance_loss(), computes triplet margin loss input tensors using custom distance function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Triplet margin loss — nn_triplet_margin_loss","text":"Input: \\((N, D)\\) \\(D\\) vector dimension. Output: Tensor shape \\((N)\\) reduction 'none', scalar otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triplet margin loss — nn_triplet_margin_loss","text":"","code":"if (torch_is_installed()) { triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2) anchor <- torch_randn(100, 128, requires_grad = TRUE) positive <- torch_randn(100, 128, requires_grad = TRUE) negative <- torch_randn(100, 128, requires_grad = TRUE) output <- triplet_loss(anchor, positive, negative) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"Creates criterion measures triplet loss given input tensors \\(\\), \\(p\\), \\(n\\) (representing anchor, positive, negative examples, respectively), nonnegative, real-valued function (\"distance function\") used compute relationship anchor positive example (\"positive distance\") anchor negative example (\"negative distance\").","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"","code":"nn_triplet_margin_with_distance_loss(   distance_function = NULL,   margin = 1,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"distance_function (callable, optional): nonnegative, real-valued function quantifies closeness two tensors. specified, nn_pairwise_distance() used.  Default: None margin (float, optional): non-negative margin representing minimum difference positive negative distances required loss 0. Larger margins penalize cases negative examples distant enough anchors, relative positives. Default: \\(1\\). swap (bool, optional): Whether use distance swap described paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. TRUE, positive example closer negative example anchor , swaps positive example anchor loss computation. Default: FALSE. reduction (string, optional): Specifies (optional) reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"unreduced loss (.e., reduction set 'none') can described : $$   \\ell(, p, n) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_i = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\} $$ \\(N\\) batch size; \\(d\\) nonnegative, real-valued function quantifying closeness two tensors, referred distance_function; \\(margin\\) non-negative margin representing minimum difference positive negative distances required loss 0.  input tensors \\(N\\) elements can shape distance function can handle. reduction 'none' (default 'mean'), : $$ \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), &  \\mbox{reduction} = \\mbox{`mean';}\\\\             \\mbox{sum}(L),  &  \\mbox{reduction} = \\mbox{`sum'.} \\end{array} $$ See also nn_triplet_margin_loss(), computes triplet loss input tensors using \\(l_p\\) distance distance function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"Input: \\((N, *)\\) \\(*\\) represents number additional dimensions supported distance function. Output: Tensor shape \\((N)\\) reduction 'none', scalar otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"","code":"if (torch_is_installed()) { # Initialize embeddings embedding <- nn_embedding(1000, 128) anchor_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) positive_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) negative_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) anchor <- embedding(anchor_ids) positive <- embedding(positive_ids) negative <- embedding(negative_ids)  # Built-in Distance Function triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = nn_pairwise_distance() ) output <- triplet_loss(anchor, positive, negative)  # Custom Distance Function l_infinity <- function(x1, x2) {   torch_max(torch_abs(x1 - x2), dim = 1)[[1]] }  triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = l_infinity, margin = 1.5 ) output <- triplet_loss(anchor, positive, negative)  # Custom Distance Function (Lambda) triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = function(x, y) {     1 - nnf_cosine_similarity(x, y)   } )  output <- triplet_loss(anchor, positive, negative) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"Unflattens tensor dim expanding desired shape. use [nn_sequential.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"","code":"nn_unflatten(dim, unflattened_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"dim Dimension unflattened unflattened_size New shape unflattened dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"","code":"if (torch_is_installed()) { input <- torch_randn(2, 50)  m <- nn_sequential(   nn_linear(50, 50),   nn_unflatten(2, c(2, 5, 5)) ) output <- m(input) output$size() } #> [1] 2 2 5 5"},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample module — nn_upsample","title":"Upsample module — nn_upsample","text":"Upsamples given multi-channel 1D (temporal), 2D (spatial) 3D (volumetric) data. input data assumed form minibatch x channels x optional depth x optional height] x width. Hence, spatial inputs, expect 4D Tensor volumetric inputs, expect 5D Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample module — nn_upsample","text":"","code":"nn_upsample(   size = NULL,   scale_factor = NULL,   mode = \"nearest\",   align_corners = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample module — nn_upsample","text":"size (int Tuple[int] Tuple[int, int] Tuple[int, int, int], optional): output spatial sizes scale_factor (float Tuple[float] Tuple[float, float] Tuple[float, float, float], optional): multiplier spatial size. match input size tuple. mode (str, optional): upsampling algorithm: one 'nearest', 'linear', 'bilinear', 'bicubic' 'trilinear'. Default: 'nearest' align_corners (bool, optional): TRUE, corner pixels input output tensors aligned, thus preserving values pixels. effect mode 'linear', 'bilinear', 'trilinear'. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Upsample module — nn_upsample","text":"algorithms available upsampling nearest neighbor linear, bilinear, bicubic trilinear 3D, 4D 5D input Tensor, respectively. One can either give scale_factor target output size calculate output size. (give , ambiguous)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample module — nn_upsample","text":"","code":"if (torch_is_installed()) { input <- torch_arange(start = 1, end = 4, dtype = torch_float())$view(c(1, 1, 2, 2)) nn_upsample(scale_factor = c(2), mode = \"nearest\")(input) nn_upsample(scale_factor = c(2, 2), mode = \"nearest\")(input) } #> torch_tensor #> (1,1,.,.) =  #>   1  1  2  2 #>   1  1  2  2 #>   3  3  4  4 #>   3  3  4  4 #> [ CPUFloatType{1,1,4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":null,"dir":"Reference","previous_headings":"","what":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"norm computed gradients together, concatenated single vector. Gradients modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"","code":"nn_utils_clip_grad_norm_(parameters, max_norm, norm_type = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"parameters (IterableTensor Tensor): iterable Tensors single Tensor gradients normalized max_norm (float int): max norm gradients norm_type (float int): type used p-norm. Can Inf infinity norm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"Total norm parameters (viewed single vector).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":null,"dir":"Reference","previous_headings":"","what":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"Gradients modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"","code":"nn_utils_clip_grad_value_(parameters, clip_value)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"parameters (Iterable(Tensor) Tensor): iterable Tensors single Tensor gradients normalized clip_value (float int): maximum allowed value gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"gradients clipped range \\(\\left[\\mbox{-clip\\_value}, \\mbox{clip\\_value}\\right]\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"input can size T x B x * T length longest sequence (equal lengths[1]), B batch size, * number dimensions (including 0). batch_first TRUE, B x T x * input expected.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"","code":"nn_utils_rnn_pack_padded_sequence(   input,   lengths,   batch_first = FALSE,   enforce_sorted = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"input (Tensor): padded batch variable length sequences. lengths (Tensor): list sequences lengths batch element. batch_first (bool, optional): TRUE, input expected B x T x * format. enforce_sorted (bool, optional): TRUE, input expected contain sequences sorted length decreasing order. FALSE, input get sorted unconditionally. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"PackedSequence object","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"unsorted sequences, use enforce_sorted = FALSE. enforce_sorted TRUE, sequences sorted length decreasing order, .e. input[,1] longest sequence, input[,B] shortest one. enforce_sorted = TRUE necessary ONNX export.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"function accepts input least two dimensions. can apply pack labels, use output RNN compute loss directly. Tensor can retrieved PackedSequence object accessing .data attribute.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"sequences list Tensors size L x *, L length sequence * number trailing dimensions, including zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"","code":"nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"sequences (list[Tensor]): list sequences decreasing length. enforce_sorted (bool, optional): TRUE, checks input contains sequences sorted length decreasing order. FALSE, condition checked. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"PackedSequence object","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"unsorted sequences, use enforce_sorted = FALSE. enforce_sorted TRUE, sequences sorted order decreasing length. enforce_sorted = TRUE necessary ONNX export.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(c(1, 2, 3), dtype = torch_long()) y <- torch_tensor(c(4, 5), dtype = torch_long()) z <- torch_tensor(c(6), dtype = torch_long())  p <- nn_utils_rnn_pack_sequence(list(x, y, z)) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"inverse operation nn_utils_rnn_pack_padded_sequence().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"","code":"nn_utils_rnn_pad_packed_sequence(   sequence,   batch_first = FALSE,   padding_value = 0,   total_length = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"sequence (PackedSequence): batch pad batch_first (bool, optional): True, output ``B x T x *` format. padding_value (float, optional): values padded elements. total_length (int, optional): NULL, output padded length total_length. method throw ValueError total_length less max sequence length sequence.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"Tuple Tensor containing padded sequence, Tensor containing list lengths sequence batch. Batch elements re-ordered ordered originally batch passed nn_utils_rnn_pack_padded_sequence() nn_utils_rnn_pack_sequence().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"returned Tensor's data size T x B x *, T length longest sequence B batch size. batch_first TRUE, data transposed B x T x * format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"total_length useful implement pack sequence -> recurrent network -> unpack sequence pattern nn_module wrapped ~torch.nn.DataParallel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"","code":"if (torch_is_installed()) { seq <- torch_tensor(rbind(c(1, 2, 0), c(3, 0, 0), c(4, 5, 6))) lens <- c(2, 1, 3) packed <- nn_utils_rnn_pack_padded_sequence(seq, lens,   batch_first = TRUE,   enforce_sorted = FALSE ) packed nn_utils_rnn_pad_packed_sequence(packed, batch_first = TRUE) } #> [[1]] #> torch_tensor #>  1  2  0 #>  3  0  0 #>  4  5  6 #> [ CPUFloatType{3,3} ] #>  #> [[2]] #> torch_tensor #>  2 #>  1 #>  3 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"pad_sequence stacks list Tensors along new dimension, pads equal length. example, input list sequences size L x * batch_first False, T x B x * otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"","code":"nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"sequences (list[Tensor]): list variable length sequences. batch_first (bool, optional): output B x T x * TRUE, T x B x * otherwise padding_value (float, optional): value padded elements. Default: 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"Tensor size T x B x * batch_first FALSE. Tensor size B x T x * otherwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"B batch size. equal number elements sequences. T length longest sequence. L length sequence. * number trailing dimensions, including none.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"function returns Tensor size T x B x * B x T x * T length longest sequence. function assumes trailing dimensions type Tensors sequences .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"","code":"if (torch_is_installed()) { a <- torch_ones(25, 300) b <- torch_ones(22, 300) c <- torch_ones(15, 300) nn_utils_rnn_pad_sequence(list(a, b, c))$size() } #> [1]  25   3 300"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"nn_utils_weight_norm — nn_utils_weight_norm","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"Applies weight normalization parameter given module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"original module weight_v weight_g paramters.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"Weight normalization reparameterization decouples magnitude weight tensor direction. replaces parameter specified name  (e.g. 'weight') two parameters: one specifying magnitude (e.g. 'weight_g') one specifying direction (e.g. 'weight_v').","code":"\\eqn{\\mathbf{w} = g \\dfrac{\\mathbf{v}}{\\|\\mathbf{v}\\|}}"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"pytorch Weight normalization implemented via hook recomputes weight tensor magnitude direction every forward() call. Since torch R still support hooks, weight recomputation need done explicitly inside forward() definition trough call recompute() method. See examples. default, dim = 0, norm computed independently per output channel/plane. compute norm entire weight tensor, use dim = NULL. @references https://arxiv.org/abs/1602.07868","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"nn_utils_weight_norm$new() nn_utils_weight_norm$compute_weight() nn_utils_weight_norm$apply() nn_utils_weight_norm$call() nn_utils_weight_norm$recompute() nn_utils_weight_norm$remove() nn_utils_weight_norm$clone()","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$new(name, dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"name (str, optional): name weight parameter dim (int, optional): dimension compute norm","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$compute_weight(module, name = NULL, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module name (str, optional): name weight parameter dim (int, optional): dimension compute norm","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$apply(module, name = NULL, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module name (str, optional): name weight parameter dim (int, optional): dimension compute norm","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$call(module)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$recompute(module)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$remove(module, name = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module name (str, optional): name weight parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"if (torch_is_installed()) { x = nn_linear(in_features = 20, out_features = 40) weight_norm = nn_utils_weight_norm$new(name = 'weight', dim = 2) weight_norm$apply(x) x$weight_g$size() x$weight_v$size() x$weight  # the recompute() method recomputes the weight using g and v. It must be called # explicitly inside `forward()`. weight_norm$recompute(x)  }"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"Applies 1D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"","code":"nnf_adaptive_avg_pool1d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) output_size target output size (single integer)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"Applies 2D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"","code":"nnf_adaptive_avg_pool2d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) output_size target output size (single integer double-integer tuple)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"Applies 3D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"","code":"nnf_adaptive_avg_pool3d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) output_size target output size (single integer triple-integer tuple)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"Applies 1D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"","code":"nnf_adaptive_max_pool1d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) output_size target output size (single integer) return_indices whether return pooling indices. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"Applies 2D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"","code":"nnf_adaptive_max_pool2d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) output_size target output size (single integer double-integer tuple) return_indices whether return pooling indices. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"Applies 3D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"","code":"nnf_adaptive_max_pool3d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) output_size target output size (single integer triple-integer tuple) return_indices whether return pooling indices. Default:FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Affine_grid — nnf_affine_grid","title":"Affine_grid — nnf_affine_grid","text":"Generates 2D 3D flow field (sampling grid), given batch affine matrices theta.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Affine_grid — nnf_affine_grid","text":"","code":"nnf_affine_grid(theta, size, align_corners = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Affine_grid — nnf_affine_grid","text":"theta (Tensor) input batch affine matrices shape (\\(N \\times 2 \\times 3\\)) 2D  (\\(N \\times 3 \\times 4\\)) 3D size (torch.Size) target output image size. (\\(N \\times C \\times H \\times W\\) 2D \\(N \\times C \\times D \\times H \\times W\\) 3D) Example: torch.Size((32, 3, 24, 24)) align_corners (bool, optional) True, consider -1 1 refer centers corner pixels rather image corners. Refer nnf_grid_sample() complete description. grid generated nnf_affine_grid() passed nnf_grid_sample()  setting option. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Affine_grid — nnf_affine_grid","text":"function often used conjunction nnf_grid_sample() build Spatial Transformer Networks_ .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Alpha_dropout — nnf_alpha_dropout","title":"Alpha_dropout — nnf_alpha_dropout","text":"Applies alpha dropout input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Alpha_dropout — nnf_alpha_dropout","text":"","code":"nnf_alpha_dropout(input, p = 0.5, training = FALSE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Alpha_dropout — nnf_alpha_dropout","text":"input input tensor p probability element zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool1d — nnf_avg_pool1d","title":"Avg_pool1d — nnf_avg_pool1d","text":"Applies 1D average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool1d — nnf_avg_pool1d","text":"","code":"nnf_avg_pool1d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool1d — nnf_avg_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) kernel_size size window. Can single number tuple (kW,). stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 ceil_mode True, use ceil instead floor compute output shape. Default: FALSE count_include_pad True, include zero-padding averaging calculation. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool2d — nnf_avg_pool2d","title":"Avg_pool2d — nnf_avg_pool2d","text":"Applies 2D average-pooling operation \\(kH * kW\\) regions step size \\(sH * sW\\) steps. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool2d — nnf_avg_pool2d","text":"","code":"nnf_avg_pool2d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool2d — nnf_avg_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) kernel_size size pooling region. Can single number tuple (kH, kW) stride stride pooling operation. Can single number tuple (sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padH, padW). Default: 0 ceil_mode True, use ceil instead floor formula compute output shape. Default: FALSE count_include_pad True, include zero-padding averaging calculation. Default: TRUE divisor_override specified, used divisor, otherwise size pooling region used. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool3d — nnf_avg_pool3d","title":"Avg_pool3d — nnf_avg_pool3d","text":"Applies 3D average-pooling operation \\(kT * kH * kW\\) regions step size \\(sT * sH * sW\\) steps. number output features equal \\(\\lfloor \\frac{ \\mbox{input planes} }{sT} \\rfloor\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool3d — nnf_avg_pool3d","text":"","code":"nnf_avg_pool3d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool3d — nnf_avg_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) kernel_size size pooling region. Can single number tuple (kT, kH, kW) stride stride pooling operation. Can single number tuple (sT, sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padT, padH, padW), Default: 0 ceil_mode True, use ceil instead floor formula compute output shape count_include_pad True, include zero-padding averaging calculation divisor_override NA specified, used divisor, otherwise size pooling region used. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch_norm — nnf_batch_norm","title":"Batch_norm — nnf_batch_norm","text":"Applies Batch Normalization channel across batch data.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch_norm — nnf_batch_norm","text":"","code":"nnf_batch_norm(   input,   running_mean,   running_var,   weight = NULL,   bias = NULL,   training = FALSE,   momentum = 0.1,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch_norm — nnf_batch_norm","text":"input input tensor running_mean running_mean tensor running_var running_var tensor weight weight tensor bias bias tensor training bool wether training. Default: FALSE momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":null,"dir":"Reference","previous_headings":"","what":"Bilinear — nnf_bilinear","title":"Bilinear — nnf_bilinear","text":"Applies bilinear transformation incoming data: \\(y = x_1 x_2 + b\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bilinear — nnf_bilinear","text":"","code":"nnf_bilinear(input1, input2, weight, bias = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bilinear — nnf_bilinear","text":"input1 \\((N, *, H_{in1})\\) \\(H_{in1}=\\mbox{in1\\_features}\\) \\(*\\) means number additional dimensions. last dimension inputs . input2 \\((N, *, H_{in2})\\) \\(H_{in2}=\\mbox{in2\\_features}\\) weight \\((\\mbox{\\_features}, \\mbox{in1\\_features}, \\mbox{in2\\_features})\\) bias \\((\\mbox{\\_features})\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bilinear — nnf_bilinear","text":"output \\((N, *, H_{})\\) \\(H_{}=\\mbox{\\_features}\\) last dimension shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary_cross_entropy — nnf_binary_cross_entropy","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"Function measures Binary Cross Entropy target output.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"","code":"nnf_binary_cross_entropy(   input,   target,   weight = NULL,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input weight (tensor) weight value. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"Function measures Binary Cross Entropy target output logits.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"","code":"nnf_binary_cross_entropy_with_logits(   input,   target,   weight = NULL,   reduction = c(\"mean\", \"sum\", \"none\"),   pos_weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"input Tensor arbitrary shape target Tensor shape input weight (Tensor, optional) manual rescaling weight provided repeated match input tensor shape. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean' pos_weight (Tensor, optional) weight positive examples. Must vector length equal number classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu — nnf_celu","title":"Celu — nnf_celu","text":"Applies element-wise, \\(CELU(x) = max(0,x) + min(0, \\alpha * (exp(x \\alpha) - 1))\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu — nnf_celu","text":"","code":"nnf_celu(input, alpha = 1, inplace = FALSE)  nnf_celu_(input, alpha = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu — nnf_celu","text":"input (N,*) tensor, * means, number additional dimensions alpha alpha value CELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparsemax — nnf_contrib_sparsemax","title":"Sparsemax — nnf_contrib_sparsemax","text":"Applies SparseMax activation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparsemax — nnf_contrib_sparsemax","text":"","code":"nnf_contrib_sparsemax(input, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparsemax — nnf_contrib_sparsemax","text":"input input tensor dim dimension apply sparsemax function. (-1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sparsemax — nnf_contrib_sparsemax","text":"SparseMax activation described 'Softmax Sparsemax: Sparse Model Attention Multi-Label Classification' implementation based aced125/sparsemax","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1d — nnf_conv1d","title":"Conv1d — nnf_conv1d","text":"Applies 1D convolution input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1d — nnf_conv1d","text":"","code":"nnf_conv1d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1d — nnf_conv1d","text":"input input tensor shape (minibatch, in_channels , iW) weight filters shape (out_channels, in_channels/groups , kW) bias optional bias shape (out_channels). Default: NULL stride stride convolving kernel. Can single number one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can single number one-element tuple (padW,). Default: 0 dilation spacing kernel elements. Can single number one-element tuple (dW,). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2d — nnf_conv2d","title":"Conv2d — nnf_conv2d","text":"Applies 2D convolution input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2d — nnf_conv2d","text":"","code":"nnf_conv2d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2d — nnf_conv2d","text":"input input tensor shape (minibatch, in_channels, iH , iW) weight filters shape (out_channels , in_channels/groups, kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padH, padW). Default: 0 dilation spacing kernel elements. Can single number tuple (dH, dW). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3d — nnf_conv3d","title":"Conv3d — nnf_conv3d","text":"Applies 3D convolution input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3d — nnf_conv3d","text":"","code":"nnf_conv3d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3d — nnf_conv3d","text":"input input tensor shape (minibatch, in_channels , , iH , iW) weight filters shape (out_channels , in_channels/groups, kT , kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padT, padH, padW). Default: 0 dilation spacing kernel elements. Can single number tuple (dT, dH, dW). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_tbc — nnf_conv_tbc","title":"Conv_tbc — nnf_conv_tbc","text":"Applies 1-dimensional sequence convolution input sequence. Input output dimensions (Time, Batch, Channels) - hence TBC.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_tbc — nnf_conv_tbc","text":"","code":"nnf_conv_tbc(input, weight, bias, pad = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_tbc — nnf_conv_tbc","text":"input input tensor shape \\((\\mbox{sequence length} \\times batch \\times \\mbox{\\_channels})\\) weight filter shape (\\(\\mbox{kernel width} \\times \\mbox{\\_channels} \\times \\mbox{\\_channels}\\)) bias bias shape (\\(\\mbox{\\_channels}\\)) pad number timesteps pad. Default: 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose1d — nnf_conv_transpose1d","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"Applies 1D transposed convolution operator input signal composed several input planes, sometimes also called \"deconvolution\".","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"","code":"nnf_conv_transpose1d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"input input tensor shape (minibatch, in_channels , iW) weight filters shape (out_channels, in_channels/groups , kW) bias optional bias shape (out_channels). Default: NULL stride stride convolving kernel. Can single number one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can single number one-element tuple (padW,). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number one-element tuple (dW,). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose2d — nnf_conv_transpose2d","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\".","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"","code":"nnf_conv_transpose2d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"input input tensor shape (minibatch, in_channels, iH , iW) weight filters shape (out_channels , in_channels/groups, kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padH, padW). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number tuple (dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose3d — nnf_conv_transpose3d","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"","code":"nnf_conv_transpose3d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"input input tensor shape (minibatch, in_channels , , iH , iW) weight filters shape (out_channels , in_channels/groups, kT , kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padT, padH, padW). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number tuple (dT, dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_embedding_loss — nnf_cosine_embedding_loss","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"Creates criterion measures loss given input tensors x_1, x_2 Tensor label y values 1 -1. used measuring whether two inputs similar dissimilar, using cosine distance, typically used learning nonlinear embeddings semi-supervised learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"","code":"nnf_cosine_embedding_loss(   input1,   input2,   target,   margin = 0,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"input1 input x_1 tensor input2 input x_2 tensor target target tensor margin number -1 1 , 0 0.5 suggested. margin missing, default value 0. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_similarity — nnf_cosine_similarity","title":"Cosine_similarity — nnf_cosine_similarity","text":"Returns cosine similarity x1 x2, computed along dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_similarity — nnf_cosine_similarity","text":"","code":"nnf_cosine_similarity(x1, x2, dim = 2, eps = 1e-08)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_similarity — nnf_cosine_similarity","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). dim (int, optional) Dimension vectors. Default: 2 eps (float, optional) Small value avoid division zero. Default: 1e-8","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cosine_similarity — nnf_cosine_similarity","text":"$$     \\mbox{similarity} = \\frac{x_1 \\cdot x_2}{\\max(\\Vert x_1 \\Vert _2 \\cdot \\Vert x_2 \\Vert _2, \\epsilon)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross_entropy — nnf_cross_entropy","title":"Cross_entropy — nnf_cross_entropy","text":"criterion combines log_softmax nll_loss single function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross_entropy — nnf_cross_entropy","text":"","code":"nnf_cross_entropy(   input,   target,   weight = NULL,   ignore_index = -100,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross_entropy — nnf_cross_entropy","text":"input (Tensor) \\((N, C)\\) C = number classes \\((N, C, H, W)\\) case 2D Loss, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. target (Tensor) \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional loss. weight (Tensor, optional) manual rescaling weight given class. given, Tensor size C ignore_index (int, optional) Specifies target value ignored contribute input gradient. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Ctc_loss — nnf_ctc_loss","title":"Ctc_loss — nnf_ctc_loss","text":"Connectionist Temporal Classification loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ctc_loss — nnf_ctc_loss","text":"","code":"nnf_ctc_loss(   log_probs,   targets,   input_lengths,   target_lengths,   blank = 0,   reduction = c(\"mean\", \"sum\", \"none\"),   zero_infinity = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ctc_loss — nnf_ctc_loss","text":"log_probs \\((T, N, C)\\) C = number characters alphabet including blank, T = input length, N = batch size. logarithmized probabilities outputs (e.g. obtained nnf_log_softmax). targets \\((N, S)\\) (sum(target_lengths)). Targets blank. second form, targets assumed concatenated. input_lengths \\((N)\\). Lengths inputs (must \\(\\leq T\\)) target_lengths \\((N)\\). Lengths targets blank (int, optional) Blank label. Default \\(0\\). reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean' zero_infinity (bool, optional) Whether zero infinite losses associated gradients. Default: FALSE Infinite losses mainly occur inputs short aligned targets.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout — nnf_dropout","title":"Dropout — nnf_dropout","text":"training, randomly zeroes elements input tensor probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout — nnf_dropout","text":"","code":"nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout — nnf_dropout","text":"input input tensor p probability element zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout2d — nnf_dropout2d","title":"Dropout2d — nnf_dropout2d","text":"Randomly zero entire channels (channel 2D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 2D tensor \\(input[, j]\\)) input tensor). channel zeroed independently every forward call probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout2d — nnf_dropout2d","text":"","code":"nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout2d — nnf_dropout2d","text":"input input tensor p probability channel zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE. inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout3d — nnf_dropout3d","title":"Dropout3d — nnf_dropout3d","text":"Randomly zero entire channels (channel 3D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 3D tensor \\(input[, j]\\)) input tensor). channel zeroed independently every forward call probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout3d — nnf_dropout3d","text":"","code":"nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout3d — nnf_dropout3d","text":"input input tensor p probability channel zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE. inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Elu — nnf_elu","title":"Elu — nnf_elu","text":"Applies element-wise, $$ELU(x) = max(0,x) + min(0, \\alpha * (exp(x) - 1))$$.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Elu — nnf_elu","text":"","code":"nnf_elu(input, alpha = 1, inplace = FALSE)  nnf_elu_(input, alpha = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Elu — nnf_elu","text":"input (N,*) tensor, * means, number additional dimensions alpha alpha value ELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Elu — nnf_elu","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, 2) y <- nnf_elu(x, alpha = 1) nnf_elu_(x, alpha = 1) torch_equal(x, y) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding — nnf_embedding","title":"Embedding — nnf_embedding","text":"simple lookup table looks embeddings fixed dictionary size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding — nnf_embedding","text":"","code":"nnf_embedding(   input,   weight,   padding_idx = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   sparse = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding — nnf_embedding","text":"input (LongTensor) Tensor containing indices embedding matrix weight (Tensor) embedding matrix number rows equal maximum possible index + 1, number columns equal embedding size padding_idx (int, optional) given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. max_norm (float, optional) given, embedding vector norm larger max_norm renormalized norm max_norm. Note: modify weight -place. norm_type (float, optional) p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional) given, scale gradients inverse frequency words mini-batch. Default FALSE. sparse (bool, optional) TRUE, gradient w.r.t. weight sparse tensor. See Notes nn_embedding details regarding sparse gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Embedding — nnf_embedding","text":"module often used retrieve word embeddings using indices. input module list indices, embedding matrix, output corresponding word embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding_bag — nnf_embedding_bag","title":"Embedding_bag — nnf_embedding_bag","text":"Computes sums, means maxes bags embeddings, without instantiating intermediate embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding_bag — nnf_embedding_bag","text":"","code":"nnf_embedding_bag(   input,   weight,   offsets = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   mode = \"mean\",   sparse = FALSE,   per_sample_weights = NULL,   include_last_offset = FALSE,   padding_idx = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding_bag — nnf_embedding_bag","text":"input (LongTensor) Tensor containing bags indices embedding matrix weight (Tensor) embedding matrix number rows equal maximum possible index + 1, number columns equal embedding size offsets (LongTensor, optional) used input 1D. offsets determines starting index position bag (sequence) input. max_norm (float, optional) given, embedding vector norm larger max_norm renormalized norm max_norm. Note: modify weight -place. norm_type (float, optional) p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional) given, scale gradients inverse frequency words mini-batch. Default FALSE.                                            Note: option supported mode=\"max\". mode (string, optional) \"sum\", \"mean\" \"max\". Specifies way reduce bag. Default: 'mean' sparse (bool, optional) TRUE, gradient w.r.t. weight sparse tensor. See Notes nn_embedding details regarding sparse gradients. Note: option supported mode=\"max\". per_sample_weights (Tensor, optional) tensor float / double weights, NULL indicate weights taken 1. specified, per_sample_weights must exactly shape input treated offsets, NULL. include_last_offset (bool, optional) TRUE, size offsets equal number bags + 1. padding_idx (int, optional) given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold — nnf_fold","title":"Fold — nnf_fold","text":"Combines array sliding local blocks large containing tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold — nnf_fold","text":"","code":"nnf_fold(   input,   output_size,   kernel_size,   dilation = 1,   padding = 0,   stride = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold — nnf_fold","text":"input input tensor output_size shape spatial dimensions output (.e., output$sizes()[-c(1,2)]) kernel_size size sliding blocks dilation parameter controls stride elements within neighborhood. Default: 1 padding implicit zero padding added sides input. Default: 0 stride stride sliding blocks input spatial dimensions. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Fold — nnf_fold","text":"Currently, 4-D output tensors (batched image-like tensors) supported.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional_max_pool2d — nnf_fractional_max_pool2d","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"Applies 2D fractional max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"","code":"nnf_fractional_max_pool2d(   input,   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE,   random_samples = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"input input tensor kernel_size size window take max . Can single number \\(k\\) (square kernel \\(k * k\\)) tuple (kH, kW) output_size target output size image form \\(oH * oW\\). Can tuple (oH, oW) single number \\(oH\\) square image \\(oH * oH\\) output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices True, return indices along outputs. random_samples optional random samples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling_ Ben Graham max-pooling operation applied \\(kH * kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional_max_pool3d — nnf_fractional_max_pool3d","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"Applies 3D fractional max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"","code":"nnf_fractional_max_pool3d(   input,   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE,   random_samples = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"input input tensor kernel_size size window take max . Can single number \\(k\\) (square kernel \\(k * k * k\\)) tuple (kT, kH, kW) output_size target output size form \\(oT * oH * oW\\). Can tuple (oT, oH, oW) single number \\(oH\\) cubic output \\(oH * oH * oH\\) output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices True, return indices along outputs. random_samples undocumented argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling_ Ben Graham max-pooling operation applied \\(kT * kH * kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gelu — nnf_gelu","title":"Gelu — nnf_gelu","text":"Gelu","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gelu — nnf_gelu","text":"","code":"nnf_gelu(input, approximate = \"none\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gelu — nnf_gelu","text":"input (N,*) tensor, * means, number additional dimensions approximate default none, applies element-wise x*pnorm(x), 'tanh', GELU estimated. See GELU info.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"gelu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"gelu(input) -> Tensor","title":"Gelu — nnf_gelu","text":"Applies element-wise function \\(GELU(x) = x * \\Phi(x)\\) \\(\\Phi(x)\\) Cumulative Distribution Function Gaussian Distribution. See Gaussian Error Linear Units (GELUs).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":null,"dir":"Reference","previous_headings":"","what":"Glu — nnf_glu","title":"Glu — nnf_glu","text":"gated linear unit. Computes:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glu — nnf_glu","text":"","code":"nnf_glu(input, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glu — nnf_glu","text":"input (Tensor) input tensor dim (int) dimension split input. Default: -1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glu — nnf_glu","text":"$$GLU(, b) = \\otimes \\sigma(b)$$ input split half along dim form b, \\(\\sigma\\) sigmoid function \\(\\otimes\\) element-wise product matrices. See Language Modeling Gated Convolutional Networks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid_sample — nnf_grid_sample","title":"Grid_sample — nnf_grid_sample","text":"Given input flow-field grid, computes output using input values pixel locations grid.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid_sample — nnf_grid_sample","text":"","code":"nnf_grid_sample(   input,   grid,   mode = c(\"bilinear\", \"nearest\"),   padding_mode = c(\"zeros\", \"border\", \"reflection\"),   align_corners = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid_sample — nnf_grid_sample","text":"input (Tensor) input shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\) (4-D case)                    \\((N, C, D_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}})\\) (5-D case) grid (Tensor) flow-field shape \\((N, H_{\\mbox{}}, W_{\\mbox{}}, 2)\\) (4-D case)                   \\((N, D_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}}, 3)\\) (5-D case) mode (str) interpolation mode calculate output values 'bilinear' | 'nearest'. Default: 'bilinear' padding_mode (str) padding mode outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros' align_corners (bool, optional) Geometrically, consider pixels input  squares rather points. set True, extrema (-1 1) considered referring center points input's corner pixels. set False, instead considered referring corner points input's corner pixels, making sampling resolution agnostic. option parallels align_corners option  nnf_interpolate(), whichever option used also used resize input image grid sampling. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Grid_sample — nnf_grid_sample","text":"Currently, spatial (4-D) volumetric (5-D) input supported. spatial (4-D) case, input shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\) grid shape \\((N, H_{\\mbox{}}, W_{\\mbox{}}, 2)\\), output shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\). output location output[n, :, h, w], size-2 vector grid[n, h, w] specifies input pixel locations x y, used interpolate output value output[n, :, h, w]. case 5D inputs, grid[n, d, h, w] specifies x, y, z pixel locations interpolating output[n, :, d, h, w]. mode argument specifies nearest bilinear interpolation method sample input pixels. grid specifies sampling pixel locations normalized input spatial dimensions. Therefore, values range [-1, 1]. example, values x = -1, y = -1 left-top pixel input, values  x = 1, y = 1 right-bottom pixel input. grid values outside range [-1, 1], corresponding outputs handled defined padding_mode. Options padding_mode=\"zeros\": use 0 --bound grid locations, padding_mode=\"border\": use border values --bound grid locations, padding_mode=\"reflection\": use values locations reflected border --bound grid locations. location far away border, keep reflected becoming bound, e.g., (normalized) pixel location x = -3.5 reflects border -1 becomes x' = 1.5, reflects border 1 becomes x'' = -0.5.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Grid_sample — nnf_grid_sample","text":"function often used conjunction nnf_affine_grid() build Spatial Transformer Networks_ .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Group_norm — nnf_group_norm","title":"Group_norm — nnf_group_norm","text":"Applies Group Normalization last certain number dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group_norm — nnf_group_norm","text":"","code":"nnf_group_norm(input, num_groups, weight = NULL, bias = NULL, eps = 1e-05)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group_norm — nnf_group_norm","text":"input input tensor num_groups number groups separate channels weight weight tensor bias bias tensor eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Gumbel_softmax — nnf_gumbel_softmax","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"Samples Gumbel-Softmax distribution optionally discretizes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"","code":"nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"logits [..., num_features] unnormalized log probabilities tau non-negative scalar temperature hard True, returned samples discretized one-hot vectors,        differentiated soft sample autograd dim (int) dimension along softmax computed. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardshrink — nnf_hardshrink","title":"Hardshrink — nnf_hardshrink","text":"Applies hard shrinkage function element-wise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardshrink — nnf_hardshrink","text":"","code":"nnf_hardshrink(input, lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardshrink — nnf_hardshrink","text":"input (N,*) tensor, * means, number additional dimensions lambd lambda value Hardshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardsigmoid — nnf_hardsigmoid","title":"Hardsigmoid — nnf_hardsigmoid","text":"Applies element-wise function \\(\\mbox{Hardsigmoid}(x) = \\frac{ReLU6(x + 3)}{6}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardsigmoid — nnf_hardsigmoid","text":"","code":"nnf_hardsigmoid(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardsigmoid — nnf_hardsigmoid","text":"input (N,*) tensor, * means, number additional dimensions inplace NA set True, operation -place. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardswish — nnf_hardswish","title":"Hardswish — nnf_hardswish","text":"Applies hardswish function, element-wise, described paper: Searching MobileNetV3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardswish — nnf_hardswish","text":"","code":"nnf_hardswish(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardswish — nnf_hardswish","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardswish — nnf_hardswish","text":"$$ \\mbox{Hardswish}(x) = \\left\\{   \\begin{array}{ll}   0 & \\mbox{} x \\le -3, \\\\   x & \\mbox{} x \\ge +3, \\\\   x \\cdot (x + 3)/6 & \\mbox{otherwise}   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardtanh — nnf_hardtanh","title":"Hardtanh — nnf_hardtanh","text":"Applies HardTanh function element-wise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardtanh — nnf_hardtanh","text":"","code":"nnf_hardtanh(input, min_val = -1, max_val = 1, inplace = FALSE)  nnf_hardtanh_(input, min_val = -1, max_val = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardtanh — nnf_hardtanh","text":"input (N,*) tensor, * means, number additional dimensions min_val minimum value linear region range. Default: -1 max_val maximum value linear region range. Default: 1 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Hinge_embedding_loss — nnf_hinge_embedding_loss","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"Measures loss given input tensor xx labels tensor yy (containing 1 -1). usually used measuring whether two inputs similar dissimilar, e.g. using L1 pairwise distance xx , typically used learning nonlinear embeddings semi-supervised learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"","code":"nnf_hinge_embedding_loss(input, target, margin = 1, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input margin default value 1. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Instance_norm — nnf_instance_norm","title":"Instance_norm — nnf_instance_norm","text":"Applies Instance Normalization channel data sample batch.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instance_norm — nnf_instance_norm","text":"","code":"nnf_instance_norm(   input,   running_mean = NULL,   running_var = NULL,   weight = NULL,   bias = NULL,   use_input_stats = TRUE,   momentum = 0.1,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instance_norm — nnf_instance_norm","text":"input input tensor running_mean running_mean tensor running_var running var tensor weight weight tensor bias bias tensor use_input_stats whether use input stats momentum double momentum eps eps double numerical stability","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpolate — nnf_interpolate","title":"Interpolate — nnf_interpolate","text":"/samples input either given size given scale_factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpolate — nnf_interpolate","text":"","code":"nnf_interpolate(   input,   size = NULL,   scale_factor = NULL,   mode = \"nearest\",   align_corners = FALSE,   recompute_scale_factor = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpolate — nnf_interpolate","text":"input (Tensor) input tensor size (int Tuple[int] Tuple[int, int] Tuple[int, int, int]) output spatial size. scale_factor (float Tuple[float]) multiplier spatial size. match input size tuple. mode (str) algorithm used upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' Default: 'nearest' align_corners (bool, optional) Geometrically, consider pixels input output squares rather points. set TRUE, input output tensors aligned center points corner pixels, preserving values corner pixels. set False, input output tensors aligned corner points corner pixels, interpolation uses edge value padding --boundary values, making operation independent input size scale_factor kept . effect mode  'linear', 'bilinear', 'bicubic' 'trilinear'.  Default: False recompute_scale_factor (bool, optional) recompute scale_factor use interpolation calculation.  scale_factor passed parameter, used compute output_size.  recompute_scale_factor ```True`` specified, new scale_factor computed based output input sizes use interpolation computation (.e. computation identical computed `output_size` passed-explicitly).  Otherwise, passed-`scale_factor` used interpolation computation.  Note `scale_factor` floating-point, recomputed scale_factor may differ one passed due rounding precision issues.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interpolate — nnf_interpolate","text":"algorithm used interpolation determined mode. Currently temporal, spatial volumetric sampling supported, .e. expected inputs 3-D, 4-D 5-D shape. input dimensions interpreted form: mini-batch x channels x [optional depth] x [optional height] x width. modes available resizing : nearest, linear (3D-), bilinear, bicubic (4D-), trilinear (5D-), area","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Kl_div — nnf_kl_div","title":"Kl_div — nnf_kl_div","text":"Kullback-Leibler divergence Loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kl_div — nnf_kl_div","text":"","code":"nnf_kl_div(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kl_div — nnf_kl_div","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"L1_loss — nnf_l1_loss","title":"L1_loss — nnf_l1_loss","text":"Function takes mean element-wise absolute value difference.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L1_loss — nnf_l1_loss","text":"","code":"nnf_l1_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L1_loss — nnf_l1_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer_norm — nnf_layer_norm","title":"Layer_norm — nnf_layer_norm","text":"Applies Layer Normalization last certain number dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer_norm — nnf_layer_norm","text":"","code":"nnf_layer_norm(   input,   normalized_shape,   weight = NULL,   bias = NULL,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer_norm — nnf_layer_norm","text":"input input tensor normalized_shape input shape expected input size. single integer used, treated singleton list, module normalize last dimension expected specific size. weight weight tensor bias bias tensor eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky_relu — nnf_leaky_relu","title":"Leaky_relu — nnf_leaky_relu","text":"Applies element-wise, \\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky_relu — nnf_leaky_relu","text":"","code":"nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky_relu — nnf_leaky_relu","text":"input (N,*) tensor, * means, number additional dimensions negative_slope Controls angle negative slope. Default: 1e-2 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear — nnf_linear","title":"Linear — nnf_linear","text":"Applies linear transformation incoming data: \\(y = xA^T + b\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear — nnf_linear","text":"","code":"nnf_linear(input, weight, bias = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear — nnf_linear","text":"input \\((N, *, \\_features)\\) * means number additional dimensions weight \\((\\_features, \\_features)\\) weights tensor. bias optional tensor \\((\\_features)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Local_response_norm — nnf_local_response_norm","title":"Local_response_norm — nnf_local_response_norm","text":"Applies local response normalization input signal composed several input planes, channels occupy second dimension. Applies normalization across channels.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Local_response_norm — nnf_local_response_norm","text":"","code":"nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Local_response_norm — nnf_local_response_norm","text":"input input tensor size amount neighbouring channels used normalization alpha multiplicative factor. Default: 0.0001 beta exponent. Default: 0.75 k additive factor. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log_softmax — nnf_log_softmax","title":"Log_softmax — nnf_log_softmax","text":"Applies softmax followed logarithm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log_softmax — nnf_log_softmax","text":"","code":"nnf_log_softmax(input, dim = NULL, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log_softmax — nnf_log_softmax","text":"input (Tensor) input dim (int) dimension along log_softmax computed. dtype (torch.dtype, optional) desired data type returned tensor. specified, input tensor casted dtype operation performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Log_softmax — nnf_log_softmax","text":"mathematically equivalent log(softmax(x)), two operations separately slower, numerically unstable. function uses alternative formulation compute output gradient correctly.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logsigmoid — nnf_logsigmoid","title":"Logsigmoid — nnf_logsigmoid","text":"Applies element-wise \\(LogSigmoid(x_i) = log(\\frac{1}{1 + exp(-x_i)})\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logsigmoid — nnf_logsigmoid","text":"","code":"nnf_logsigmoid(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logsigmoid — nnf_logsigmoid","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Lp_pool1d — nnf_lp_pool1d","title":"Lp_pool1d — nnf_lp_pool1d","text":"Applies 1D power-average pooling input signal composed several input planes. sum inputs power p zero, gradient set zero well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lp_pool1d — nnf_lp_pool1d","text":"","code":"nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lp_pool1d — nnf_lp_pool1d","text":"input input tensor norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode True, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Lp_pool2d — nnf_lp_pool2d","title":"Lp_pool2d — nnf_lp_pool2d","text":"Applies 2D power-average pooling input signal composed several input planes. sum inputs power p zero, gradient set zero well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lp_pool2d — nnf_lp_pool2d","text":"","code":"nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lp_pool2d — nnf_lp_pool2d","text":"input input tensor norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode True, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Margin_ranking_loss — nnf_margin_ranking_loss","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"Creates criterion measures loss given inputs x1 , x2 , two 1D mini-batch Tensors, label 1D mini-batch tensor y (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"","code":"nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"input1 first tensor input2 second input tensor target target tensor margin default value 00 . reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool1d — nnf_max_pool1d","title":"Max_pool1d — nnf_max_pool1d","text":"Applies 1D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool1d — nnf_max_pool1d","text":"","code":"nnf_max_pool1d(   input,   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool1d — nnf_max_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) kernel_size size window. Can single number tuple (kW,). stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor compute output shape. Default: FALSE return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool2d — nnf_max_pool2d","title":"Max_pool2d — nnf_max_pool2d","text":"Applies 2D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool2d — nnf_max_pool2d","text":"","code":"nnf_max_pool2d(   input,   kernel_size,   stride = kernel_size,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool2d — nnf_max_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) kernel_size size pooling region. Can single number tuple (kH, kW) stride stride pooling operation. Can single number tuple (sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padH, padW). Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor formula compute output shape. Default: FALSE return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool3d — nnf_max_pool3d","title":"Max_pool3d — nnf_max_pool3d","text":"Applies 3D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool3d — nnf_max_pool3d","text":"","code":"nnf_max_pool3d(   input,   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool3d — nnf_max_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) kernel_size size pooling region. Can single number tuple (kT, kH, kW) stride stride pooling operation. Can single number tuple (sT, sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padT, padH, padW), Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor formula compute output shape return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool1d — nnf_max_unpool1d","title":"Max_unpool1d — nnf_max_unpool1d","text":"Computes partial inverse MaxPool1d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool1d — nnf_max_unpool1d","text":"","code":"nnf_max_unpool1d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool1d — nnf_max_unpool1d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool2d — nnf_max_unpool2d","title":"Max_unpool2d — nnf_max_unpool2d","text":"Computes partial inverse MaxPool2d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool2d — nnf_max_unpool2d","text":"","code":"nnf_max_unpool2d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool2d — nnf_max_unpool2d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool3d — nnf_max_unpool3d","title":"Max_unpool3d — nnf_max_unpool3d","text":"Computes partial inverse MaxPool3d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool3d — nnf_max_unpool3d","text":"","code":"nnf_max_unpool3d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool3d — nnf_max_unpool3d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Mse_loss — nnf_mse_loss","title":"Mse_loss — nnf_mse_loss","text":"Measures element-wise mean squared error.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mse_loss — nnf_mse_loss","text":"","code":"nnf_mse_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mse_loss — nnf_mse_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi head attention forward — nnf_multi_head_attention_forward","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"Allows model jointly attend information different representation subspaces. See reference: Attention Need","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"","code":"nnf_multi_head_attention_forward(   query,   key,   value,   embed_dim_to_check,   num_heads,   in_proj_weight,   in_proj_bias,   bias_k,   bias_v,   add_zero_attn,   dropout_p,   out_proj_weight,   out_proj_bias,   training = TRUE,   key_padding_mask = NULL,   need_weights = TRUE,   attn_mask = NULL,   avg_weights = TRUE,   use_separate_proj_weight = FALSE,   q_proj_weight = NULL,   k_proj_weight = NULL,   v_proj_weight = NULL,   static_k = NULL,   static_v = NULL,   batch_first = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"query \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. batch_first TRUE, first two dimensions transposed. key \\((S, N, E)\\), S source sequence length, N batch size, E embedding dimension. batch_first TRUE, first two dimensions transposed. value \\((S, N, E)\\) S source sequence length, N batch size, E embedding dimension. batch_first TRUE, first two dimensions transposed. embed_dim_to_check total dimension model. num_heads parallel attention heads. in_proj_weight input projection weight. in_proj_bias input projection bias. bias_k bias key value sequences added dim=0. bias_v currently undocumented. add_zero_attn add new batch zeros key value sequences dim=1. dropout_p probability element zeroed. out_proj_weight output projection weight. out_proj_bias output projection bias. training apply dropout TRUE. key_padding_mask \\((N, S)\\) N batch size, S source sequence length. ByteTensor provided, non-zero positions ignored position zero positions unchanged. BoolTensor provided, positions value True ignored position value False unchanged. need_weights output attn_output_weights. attn_mask 2D mask \\((L, S)\\) L target sequence length, S source sequence length. 3D mask \\((N*num_heads, L, S)\\) N batch size, L target sequence length, S source sequence length. attn_mask ensure position allowed attend unmasked positions. ByteTensor provided, non-zero positions allowed attend zero positions unchanged. BoolTensor provided, positions True allowed attend False values unchanged. FloatTensor provided, added attention weight. avg_weights Logical; whether average attn_output_weights attention heads outputting . change returned value attn_output; affects returned attention weight matrix. use_separate_proj_weight function accept proj. weights query, key, value different forms. false, in_proj_weight used, combination q_proj_weight, k_proj_weight, v_proj_weight. q_proj_weight input projection weight bias. k_proj_weight currently undocumented. v_proj_weight currently undocumented. static_k static key value used attention operators. static_v currently undocumented. batch_first Logical; whether expect query, key, value batch first parameter, return output batch first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi_margin_loss — nnf_multi_margin_loss","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"Creates criterion optimizes multi-class classification hinge loss (margin-based loss) input x (2D mini-batch Tensor) output y (1D tensor target class indices, 0 <= y <= x$size(2) - 1 ).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"","code":"nnf_multi_margin_loss(   input,   target,   p = 1,   margin = 1,   weight = NULL,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input p default value 1. 1 2 supported values. margin default value 1. weight manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel_margin_loss — nnf_multilabel_margin_loss","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"Creates criterion optimizes multi-class multi-classification hinge loss (margin-based loss) input x (2D mini-batch Tensor) output y (2D Tensor target class indices).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"","code":"nnf_multilabel_margin_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"Creates criterion optimizes multi-label one-versus-loss based max-entropy, input x target y size (N, C).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"","code":"nnf_multilabel_soft_margin_loss(   input,   target,   weight = NULL,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input weight weight tensor apply loss. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"takes one hot encoded target vector input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Nll_loss — nnf_nll_loss","title":"Nll_loss — nnf_nll_loss","text":"negative log likelihood loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nll_loss — nnf_nll_loss","text":"","code":"nnf_nll_loss(   input,   target,   weight = NULL,   ignore_index = -100,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nll_loss — nnf_nll_loss","text":"input \\((N, C)\\) C = number classes \\((N, C, H, W)\\) case 2D Loss, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. target \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional loss. weight (Tensor, optional) manual rescaling weight given class. given, Tensor size C ignore_index (int, optional) Specifies target value ignored contribute input gradient. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize — nnf_normalize","title":"Normalize — nnf_normalize","text":"Performs \\(L_p\\) normalization inputs specified dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize — nnf_normalize","text":"","code":"nnf_normalize(input, p = 2, dim = 2, eps = 1e-12, out = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize — nnf_normalize","text":"input input tensor shape p (float) exponent value norm formulation. Default: 2 dim (int) dimension reduce. Default: 1 eps (float) small value avoid division zero. Default: 1e-12 (Tensor, optional) output tensor. used,                            operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalize — nnf_normalize","text":"tensor input sizes \\((n_0, ..., n_{dim}, ..., n_k)\\), \\(n_{dim}\\) -element vector \\(v\\) along dimension dim transformed $$         v = \\frac{v}{\\max(\\Vert v \\Vert_p, \\epsilon)}. $$ default arguments uses Euclidean norm vectors along dimension \\(1\\) normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"One_hot — nnf_one_hot","title":"One_hot — nnf_one_hot","text":"Takes LongTensor index values shape (*) returns tensor shape (*, num_classes) zeros everywhere except index last dimension matches corresponding value input tensor, case 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One_hot — nnf_one_hot","text":"","code":"nnf_one_hot(tensor, num_classes = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One_hot — nnf_one_hot","text":"tensor (LongTensor) class values shape. num_classes (int) Total number classes. set -1, number classes inferred one greater largest class value input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"One_hot — nnf_one_hot","text":"One-hot Wikipedia: https://en.wikipedia.org/wiki/One-hot","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad — nnf_pad","title":"Pad — nnf_pad","text":"Pads tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad — nnf_pad","text":"","code":"nnf_pad(input, pad, mode = \"constant\", value = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad — nnf_pad","text":"input (Tensor) N-dimensional tensor pad (tuple) m-elements tuple, \\(\\frac{m}{2} \\leq\\) input dimensions \\(m\\) even. mode 'constant', 'reflect', 'replicate' 'circular'. Default: 'constant' value fill value 'constant' padding. Default: 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"padding-size","dir":"Reference","previous_headings":"","what":"Padding size","title":"Pad — nnf_pad","text":"padding size pad dimensions input described starting last dimension moving forward. \\(\\left\\lfloor\\frac{\\mbox{len(pad)}}{2}\\right\\rfloor\\) dimensions input padded. example, pad last dimension input tensor, pad form \\((\\mbox{padding\\_left}, \\mbox{padding\\_right})\\); pad last 2 dimensions input tensor, use \\((\\mbox{padding\\_left}, \\mbox{padding\\_right},\\) \\(\\mbox{padding\\_top}, \\mbox{padding\\_bottom})\\); pad last 3 dimensions, use \\((\\mbox{padding\\_left}, \\mbox{padding\\_right},\\) \\(\\mbox{padding\\_top}, \\mbox{padding\\_bottom}\\) \\(\\mbox{padding\\_front}, \\mbox{padding\\_back})\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"padding-mode","dir":"Reference","previous_headings":"","what":"Padding mode","title":"Pad — nnf_pad","text":"See nn_constant_pad_2d, nn_reflection_pad_2d, nn_replication_pad_2d concrete examples padding modes works. Constant padding implemented arbitrary dimensions. tensor, last 2 dimensions 4D input tensor, last dimension 3D input tensor. Reflect padding implemented padding last 2 dimensions 4D input tensor, last dimension 3D input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise_distance — nnf_pairwise_distance","title":"Pairwise_distance — nnf_pairwise_distance","text":"Computes batchwise pairwise distance vectors using p-norm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise_distance — nnf_pairwise_distance","text":"","code":"nnf_pairwise_distance(x1, x2, p = 2, eps = 1e-06, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise_distance — nnf_pairwise_distance","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). p norm degree. Default: 2 eps (float, optional) Small value avoid division zero. Default: 1e-8 keepdim Determines whether keep vector dimension. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Pdist — nnf_pdist","title":"Pdist — nnf_pdist","text":"Computes p-norm distance every pair row vectors input. identical upper triangular portion, excluding diagonal, torch_norm(input[:, None] - input, dim=2, p=p). function faster rows contiguous.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pdist — nnf_pdist","text":"","code":"nnf_pdist(input, p = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pdist — nnf_pdist","text":"input input tensor shape \\(N \\times M\\). p p value p-norm distance calculate vector pair \\(\\[0, \\infty]\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pdist — nnf_pdist","text":"input shape \\(N \\times M\\) output shape \\(\\frac{1}{2} N (N - 1)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Pixel_shuffle — nnf_pixel_shuffle","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"Rearranges elements tensor shape \\((*, C \\times r^2, H, W)\\) tensor shape \\((*, C, H \\times r, W \\times r)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"","code":"nnf_pixel_shuffle(input, upscale_factor)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"input (Tensor) input tensor upscale_factor (int) factor increase spatial resolution ","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson_nll_loss — nnf_poisson_nll_loss","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"Poisson negative log likelihood loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"","code":"nnf_poisson_nll_loss(   input,   target,   log_input = TRUE,   full = FALSE,   eps = 1e-08,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input log_input TRUE loss computed \\(\\exp(\\mbox{input}) - \\mbox{target} * \\mbox{input}\\), FALSE loss \\(\\mbox{input} - \\mbox{target} * \\log(\\mbox{input}+\\mbox{eps})\\). Default: TRUE. full whether compute full loss, . e. add Stirling approximation term. Default: FALSE. eps (float, optional) Small value avoid evaluation \\(\\log(0)\\) log_input=FALSE. Default: 1e-8 reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Prelu — nnf_prelu","title":"Prelu — nnf_prelu","text":"Applies element-wise function \\(PReLU(x) = max(0,x) + weight * min(0,x)\\) weight learnable parameter.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prelu — nnf_prelu","text":"","code":"nnf_prelu(input, weight)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prelu — nnf_prelu","text":"input (N,*) tensor, * means, number additional dimensions weight (Tensor) learnable weights","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu — nnf_relu","title":"Relu — nnf_relu","text":"Applies rectified linear unit function element-wise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu — nnf_relu","text":"","code":"nnf_relu(input, inplace = FALSE)  nnf_relu_(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu — nnf_relu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 — nnf_relu6","title":"Relu6 — nnf_relu6","text":"Applies element-wise function \\(ReLU6(x) = min(max(0,x), 6)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 — nnf_relu6","text":"","code":"nnf_relu6(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 — nnf_relu6","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rrelu — nnf_rrelu","title":"Rrelu — nnf_rrelu","text":"Randomized leaky ReLU.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rrelu — nnf_rrelu","text":"","code":"nnf_rrelu(input, lower = 1/8, upper = 1/3, training = FALSE, inplace = FALSE)  nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rrelu — nnf_rrelu","text":"input (N,*) tensor, * means, number additional dimensions lower lower bound uniform distribution. Default: 1/8 upper upper bound uniform distribution. Default: 1/3 training bool wether training pass. DEfault: FALSE inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu — nnf_selu","title":"Selu — nnf_selu","text":"Applies element-wise, $$SELU(x) = scale * (max(0,x) + min(0, \\alpha * (exp(x) - 1)))$$, \\(\\alpha=1.6732632423543772848170429916717\\) \\(scale=1.0507009873554804934193349852946\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu — nnf_selu","text":"","code":"nnf_selu(input, inplace = FALSE)  nnf_selu_(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu — nnf_selu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selu — nnf_selu","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, 2) y <- nnf_selu(x) nnf_selu_(x) torch_equal(x, y) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid — nnf_sigmoid","title":"Sigmoid — nnf_sigmoid","text":"Applies element-wise \\(Sigmoid(x_i) = \\frac{1}{1 + exp(-x_i)}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid — nnf_sigmoid","text":"","code":"nnf_sigmoid(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid — nnf_sigmoid","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","text":"Applies Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() information.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","text":"","code":"nnf_silu(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth_l1_loss — nnf_smooth_l1_loss","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"Function uses squared term absolute element-wise error falls 1 L1 term otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"","code":"nnf_smooth_l1_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Soft_margin_loss — nnf_soft_margin_loss","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"Creates criterion optimizes two-class classification logistic loss input tensor x target tensor y (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"","code":"nnf_soft_margin_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax — nnf_softmax","title":"Softmax — nnf_softmax","text":"Applies softmax function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax — nnf_softmax","text":"","code":"nnf_softmax(input, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax — nnf_softmax","text":"input (Tensor) input dim (int) dimension along softmax computed. dtype (torch.dtype, optional) desired data type returned tensor.      specified, input tensor casted dtype operation      performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmax — nnf_softmax","text":"Softmax defined : $$Softmax(x_{}) = exp(x_i)/\\sum_j exp(x_j)$$ applied slices along dim, re-scale elements lie range [0, 1] sum 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmin — nnf_softmin","title":"Softmin — nnf_softmin","text":"Applies softmin function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmin — nnf_softmin","text":"","code":"nnf_softmin(input, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmin — nnf_softmin","text":"input (Tensor) input dim (int) dimension along softmin computed (every slice        along dim sum 1). dtype (torch.dtype, optional) desired data type returned tensor.      specified, input tensor casted dtype operation      performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmin — nnf_softmin","text":"Note $$Softmin(x) = Softmax(-x)$$. See nnf_softmax definition mathematical formula.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus — nnf_softplus","title":"Softplus — nnf_softplus","text":"Applies element-wise, function \\(Softplus(x) = 1/\\beta * log(1 + exp(\\beta * x))\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus — nnf_softplus","text":"","code":"nnf_softplus(input, beta = 1, threshold = 20)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus — nnf_softplus","text":"input (N,*) tensor, * means, number additional dimensions beta beta value Softplus formulation. Default: 1 threshold values revert linear function. Default: 20","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softplus — nnf_softplus","text":"numerical stability implementation reverts linear function \\(input * \\beta > threshold\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Softshrink — nnf_softshrink","title":"Softshrink — nnf_softshrink","text":"Applies soft shrinkage function elementwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softshrink — nnf_softshrink","text":"","code":"nnf_softshrink(input, lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softshrink — nnf_softshrink","text":"input (N,*) tensor, * means, number additional dimensions lambd lambda (must less zero) value Softshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign — nnf_softsign","title":"Softsign — nnf_softsign","text":"Applies element-wise, function \\(SoftSign(x) = x/(1 + |x|\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign — nnf_softsign","text":"","code":"nnf_softsign(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign — nnf_softsign","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanhshrink — nnf_tanhshrink","title":"Tanhshrink — nnf_tanhshrink","text":"Applies element-wise, \\(Tanhshrink(x) = x - Tanh(x)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanhshrink — nnf_tanhshrink","text":"","code":"nnf_tanhshrink(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tanhshrink — nnf_tanhshrink","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold — nnf_threshold","title":"Threshold — nnf_threshold","text":"Thresholds element input Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold — nnf_threshold","text":"","code":"nnf_threshold(input, threshold, value, inplace = FALSE)  nnf_threshold_(input, threshold, value)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold — nnf_threshold","text":"input (N,*) tensor, * means, number additional dimensions threshold value threshold value value replace inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet_margin_loss — nnf_triplet_margin_loss","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"Creates criterion measures triplet loss given input tensors x1 , x2 , x3 margin value greater 0 . used measuring relative similarity samples. triplet composed , p n (.e., anchor, positive examples negative examples respectively). shapes input tensors (N, D).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"","code":"nnf_triplet_margin_loss(   anchor,   positive,   negative,   margin = 1,   p = 2,   eps = 1e-06,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"anchor anchor input tensor positive positive input tensor negative negative input tensor margin Default: 1. p norm degree pairwise distance. Default: 2. eps (float, optional) Small value avoid division zero. swap distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"See nn_triplet_margin_with_distance_loss()","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"","code":"nnf_triplet_margin_with_distance_loss(   anchor,   positive,   negative,   distance_function = NULL,   margin = 1,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"anchor anchor input tensor positive positive input tensor negative negative input tensor distance_function (callable, optional): nonnegative, real-valued function quantifies closeness two tensors. specified, nn_pairwise_distance() used.  Default: None margin Default: 1. swap distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":null,"dir":"Reference","previous_headings":"","what":"Unfold — nnf_unfold","title":"Unfold — nnf_unfold","text":"Extracts sliding local blocks batched input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unfold — nnf_unfold","text":"","code":"nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unfold — nnf_unfold","text":"input input tensor kernel_size size sliding blocks dilation parameter controls stride elements within neighborhood. Default: 1 padding implicit zero padding added sides input. Default: 0 stride stride sliding blocks input spatial dimensions. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unfold — nnf_unfold","text":"one element unfolded tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensor, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Adadelta optimizer — optim_adadelta","title":"Adadelta optimizer — optim_adadelta","text":"proposed ADADELTA: Adaptive Learning Rate Method","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adadelta optimizer — optim_adadelta","text":"","code":"optim_adadelta(params, lr = 1, rho = 0.9, eps = 1e-06, weight_decay = 0)"},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adadelta optimizer — optim_adadelta","text":"params (iterable): list parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-3) rho (float, optional): coefficient used computing running average squared gradients (default: 0.9) eps (float, optional): term added denominator improve numerical stability (default: 1e-6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Adadelta optimizer — optim_adadelta","text":"According original paper, decaying average squared gradients computed follows: $$ E[g^2]_{t} = \\rho E[g^2]_{t- 1} + (1 - \\rho){g_{t}}^2 $$ RMS previous squared gradients time t: $$ RMS[g_{t}] = \\sqrt{E[g^2]_{t} + \\epsilon } $$ Adadelta update rule: $$  \\begin{array}{ll}  \\Delta \\theta_{t} = - \\frac{RMS [\\Delta \\theta]_{t - 1} }{RMS[g]_{t}}  \\theta_{t+1} = \\theta_{t} + \\Delta \\theta_{t} \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Adadelta optimizer — optim_adadelta","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adadelta optimizer — optim_adadelta","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_adadelta(model$parameters, lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Adagrad optimizer — optim_adagrad","title":"Adagrad optimizer — optim_adagrad","text":"Proposed Adaptive Subgradient Methods Online Learning Stochastic Optimization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adagrad optimizer — optim_adagrad","text":"","code":"optim_adagrad(   params,   lr = 0.01,   lr_decay = 0,   weight_decay = 0,   initial_accumulator_value = 0,   eps = 1e-10 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adagrad optimizer — optim_adagrad","text":"params (iterable): list parameters optimize list parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) initial_accumulator_value initial value accumulator. (default: 0) Adagrad especially good optimizer sparse data. individually modifies learning rate every single parameter, dividing original learning rate value sum squares gradients. causes rarely occurring features get greater learning rates. main downside method fact learning rate may getting small fast, point model learn anymore. eps (float, optional): term added denominator improve numerical stability (default: 1e-10)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Adagrad optimizer — optim_adagrad","text":"Update rule: $$ \\theta_{t+1} = \\theta_{t} - \\frac{\\eta }{\\sqrt{G_{t} + \\epsilon}} \\odot g_{t} $$ equation remarks quoted overview gradient descent optimization algorithms Sebastian Ruder.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Adagrad optimizer — optim_adagrad","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements Adam algorithm. — optim_adam","title":"Implements Adam algorithm. — optim_adam","text":"proposed Adam: Method Stochastic Optimization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements Adam algorithm. — optim_adam","text":"","code":"optim_adam(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements Adam algorithm. — optim_adam","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Implements Adam algorithm. — optim_adam","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements Adam algorithm. — optim_adam","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_adam(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() }  }"},{"path":"https://torch.mlverse.org/docs/reference/optim_adamw.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements AdamW algorithm — optim_adamw","title":"Implements AdamW algorithm — optim_adamw","text":"details regarding algorithm refer Decoupled Weight Decay Regularization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adamw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements AdamW algorithm — optim_adamw","text":"","code":"optim_adamw(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0.01,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adamw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements AdamW algorithm — optim_adamw","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"Proposed Acceleration stochastic approximation averaging","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"","code":"optim_asgd(   params,   lr = 0.01,   lambda = 1e-04,   alpha = 0.75,   t0 = 1e+06,   weight_decay = 0 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"params (iterable): iterable parameters optimize lists defining parameter groups lr (float): learning rate lambda (float, optional): decay term (default: 1e-4) alpha (float, optional): power eta update (default: 0.75) t0 (float, optional): point start averaging (default: 1e6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_asgd(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() }  }"},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":null,"dir":"Reference","previous_headings":"","what":"LBFGS optimizer — optim_lbfgs","title":"LBFGS optimizer — optim_lbfgs","text":"Implements L-BFGS algorithm, heavily inspired minFunc","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LBFGS optimizer — optim_lbfgs","text":"","code":"optim_lbfgs(   params,   lr = 1,   max_iter = 20,   max_eval = NULL,   tolerance_grad = 1e-07,   tolerance_change = 1e-09,   history_size = 100,   line_search_fn = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LBFGS optimizer — optim_lbfgs","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate (default: 1) max_iter (int): maximal number iterations per optimization step (default: 20) max_eval (int): maximal number function evaluations per optimization step (default: max_iter * 1.25). tolerance_grad (float): termination tolerance first order optimality (default: 1e-5). tolerance_change (float): termination tolerance function value/parameter changes (default: 1e-9). history_size (int): update history size (default: 100). line_search_fn (str): either 'strong_wolfe' None (default: None).","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LBFGS optimizer — optim_lbfgs","text":"optimizer different others optimizer$step(), needs passed closure (1) calculates loss, (2) calls backward() , (3) returns . See example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"LBFGS optimizer — optim_lbfgs","text":"memory intensive optimizer (requires additional param_bytes * (history_size + 1) bytes). fit memory try reducing history size, use different algorithm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"LBFGS optimizer — optim_lbfgs","text":"optimizer support per-parameter options parameter groups (can one). Right now parameters single device. improved future. need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LBFGS optimizer — optim_lbfgs","text":"","code":"if (torch_is_installed()) { a <- 1 b <- 5 rosenbrock <- function(x) {   x1 <- x[1]   x2 <- x[2]   (a - x1)^2 + b * (x2 - x1^2)^2 }   x <- torch_tensor(c(-1, 1), requires_grad = TRUE)  optimizer <- optim_lbfgs(x) calc_loss <- function() {   optimizer$zero_grad()   value <- rosenbrock(x)   value$backward()   value }    num_iterations <- 2 for (i in 1:num_iterations) {   optimizer$step(calc_loss) }      rosenbrock(x)  } #> torch_tensor #> 1e-12 * #>  5.7554 #> [ CPUFloatType{1} ][ grad_fn = <AddBackward0> ]"},{"path":"https://torch.mlverse.org/docs/reference/optim_required.html","id":null,"dir":"Reference","previous_headings":"","what":"Dummy value indicating a required value. — optim_required","title":"Dummy value indicating a required value. — optim_required","text":"export","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_required.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dummy value indicating a required value. — optim_required","text":"","code":"optim_required()"},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"RMSprop optimizer — optim_rmsprop","title":"RMSprop optimizer — optim_rmsprop","text":"Proposed G. Hinton course.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RMSprop optimizer — optim_rmsprop","text":"","code":"optim_rmsprop(   params,   lr = 0.01,   alpha = 0.99,   eps = 1e-08,   weight_decay = 0,   momentum = 0,   centered = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RMSprop optimizer — optim_rmsprop","text":"params (iterable): iterable parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-2) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay optional weight decay penalty. (default: 0) momentum (float, optional): momentum factor (default: 0) centered (bool, optional) : TRUE, compute centered RMSProp, gradient normalized estimation variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RMSprop optimizer — optim_rmsprop","text":"centered version first appears Generating Sequences Recurrent Neural Networks. implementation takes square root gradient average adding epsilon (note TensorFlow interchanges two operations). effective learning rate thus \\(\\alpha/(\\sqrt{v} + \\epsilon)\\) \\(\\alpha\\) scheduled learning rate \\(v\\) weighted moving average squared gradient. Update rule: $$ \\theta_{t+1} = \\theta_{t} - \\frac{\\eta }{\\sqrt{{E[g^2]}_{t} + \\epsilon}} * g_{t} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"RMSprop optimizer — optim_rmsprop","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements the resilient backpropagation algorithm. — optim_rprop","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"Proposed first RPROP - Fast Adaptive Learning Algorithm","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"","code":"optim_rprop(params, lr = 0.01, etas = c(0.5, 1.2), step_sizes = c(1e-06, 50))"},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"params (iterable): iterable parameters optimize lists defining parameter groups lr (float, optional): learning rate (default: 1e-2) etas (Tuple(float, float), optional): pair (etaminus, etaplis), multiplicative increase decrease factors (default: (0.5, 1.2)) step_sizes (vector(float, float), optional): pair minimal maximal allowed step sizes (default: (1e-6, 50))","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_rprop(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"SGD optimizer — optim_sgd","title":"SGD optimizer — optim_sgd","text":"Implements stochastic gradient descent (optionally momentum). Nesterov momentum based formula importance initialization momentum deep learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SGD optimizer — optim_sgd","text":"","code":"optim_sgd(   params,   lr = optim_required(),   momentum = 0,   dampening = 0,   weight_decay = 0,   nesterov = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SGD optimizer — optim_sgd","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate momentum (float, optional): momentum factor (default: 0) dampening (float, optional): dampening momentum (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) nesterov (bool, optional): enables Nesterov momentum (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"SGD optimizer — optim_sgd","text":"implementation SGD Momentum-Nesterov subtly differs Sutskever et. al. implementations frameworks. Considering specific case Momentum, update can written $$   \\begin{array}{ll} v_{t+1} & = \\mu * v_{t} + g_{t+1}, \\\\ p_{t+1} & = p_{t} - \\mbox{lr} * v_{t+1}, \\end{array} $$ \\(p\\), \\(g\\), \\(v\\) \\(\\mu\\) denote parameters, gradient, velocity, momentum respectively. contrast Sutskever et. al. frameworks employ update form $$   \\begin{array}{ll} v_{t+1} & = \\mu * v_{t} + \\mbox{lr} * g_{t+1}, \\\\ p_{t+1} & = p_{t} - v_{t+1}. \\end{array} $$ Nesterov version analogously modified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"SGD optimizer — optim_sgd","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SGD optimizer — optim_sgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { optimizer <- optim_sgd(model$parameters(), lr = 0.1, momentum = 0.9) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() }  }"},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a custom optimizer — optimizer","title":"Creates a custom optimizer — optimizer","text":"implementing custom optimizers usually need implement initialize step methods. See example section full example.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a custom optimizer — optimizer","text":"","code":"optimizer(   name = NULL,   inherit = Optimizer,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a custom optimizer — optimizer","text":"name (optional) name optimizer inherit (optional) can inherit optimizers re-use methods. ... Pass number fields methods. least define initialize step methods. See examples section. private (optional) list private methods optimizer. active (optional) list active methods optimizer. parent_env used capture right environment define class. default fine situations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Creates a custom optimizer — optimizer","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a custom optimizer — optimizer","text":"","code":"if (torch_is_installed()) {  # In this example we will create a custom optimizer # that's just a simplified version of the `optim_sgd` function.  optim_sgd2 <- optimizer(   initialize = function(params, learning_rate) {     defaults <- list(       learning_rate = learning_rate     )     super$initialize(params, defaults)   },   step = function() {     with_no_grad({       for (g in seq_along(self$param_groups)) {         group <- self$param_groups[[g]]         for (p in seq_along(group$params)) {           param <- group$params[[p]]            if (is.null(param$grad) || is_undefined_tensor(param$grad)) {             next           }            param$add_(param$grad, alpha = -group$learning_rate)         }       }     })   } )  x <- torch_randn(1, requires_grad = TRUE) opt <- optim_sgd2(x, learning_rate = 0.1) for (i in 1:100) {   opt$zero_grad()   y <- x^2   y$backward()   opt$step() } all.equal(x$item(), 0, tolerance = 1e-9) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://torch.mlverse.org/docs/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-exporting the as_iterator function. — reexports","title":"Re-exporting the as_iterator function. — reexports","text":"objects imported packages. Follow links see documentation. coro as_iterator, loop, yield","code":""},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a new Sampler — sampler","title":"Creates a new Sampler — sampler","text":"Samplers can used dataloader() creating batches torch dataset().","code":""},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a new Sampler — sampler","text":"","code":"sampler(   name = NULL,   inherit = Sampler,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a new Sampler — sampler","text":"name (optional) name sampler inherit (optional) can inherit samplers re-use methods. ... Pass number fields methods. least define initialize step methods. See examples section. private (optional) list private methods sampler active (optional) list active methods sampler. parent_env used capture right environment define class. default fine situations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a new Sampler — sampler","text":"sampler must implement .iter .length() methods. initialize takes data_source. general dataset(). .iter returns function returns dataset index everytime called. .length returns maximum number samples can retrieved sampler.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a slice — slc","title":"Creates a slice — slc","text":"Creates slice object can used indexing torch tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a slice — slc","text":"","code":"slc(start, end, step = 1)"},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a slice — slc","text":"start (integer) starting index. end (integer) last selected index. step (integer) step indexes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a slice — slc","text":"","code":"if (torch_is_installed()) { x <- torch_randn(10) x[slc(start = 1, end = 5, step = 2)]  } #> torch_tensor #> -1.1161 #>  0.1016 #> -1.2852 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Dataset wrapping tensors. — tensor_dataset","title":"Dataset wrapping tensors. — tensor_dataset","text":"sample retrieved indexing tensors along first dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dataset wrapping tensors. — tensor_dataset","text":"","code":"tensor_dataset(...)"},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dataset wrapping tensors. — tensor_dataset","text":"... tensors size first dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of threads — threads","title":"Number of threads — threads","text":"Get set numbers used torch computations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of threads — threads","text":"","code":"torch_set_num_threads(num_threads)  torch_set_num_interop_threads(num_threads)  torch_get_num_interop_threads()  torch_get_num_threads()"},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of threads — threads","text":"num_threads number threads set.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of threads — threads","text":"details see CPU threading article PyTorch documentation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Number of threads — threads","text":"torch_set_threads work macOS system must 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Abs — torch_abs","title":"Abs — torch_abs","text":"Abs","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abs — torch_abs","text":"","code":"torch_abs(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abs — torch_abs","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"abs-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"abs(input) -> Tensor","title":"Abs — torch_abs","text":"Computes element-wise absolute value given input tensor. $$     \\mbox{}_{} = |\\mbox{input}_{}| $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abs — torch_abs","text":"","code":"if (torch_is_installed()) {  torch_abs(torch_tensor(c(-1, -2, 3))) } #> torch_tensor #>  1 #>  2 #>  3 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute — torch_absolute","title":"Absolute — torch_absolute","text":"Absolute","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute — torch_absolute","text":"","code":"torch_absolute(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute — torch_absolute","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"absolute-input-out-none-gt-tensor","dir":"Reference","previous_headings":"","what":"absolute(input, *, out=None) -> Tensor","title":"Absolute — torch_absolute","text":"Alias torch_abs()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":null,"dir":"Reference","previous_headings":"","what":"Acos — torch_acos","title":"Acos — torch_acos","text":"Acos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acos — torch_acos","text":"","code":"torch_acos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acos — torch_acos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"acos-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"acos(input) -> Tensor","title":"Acos — torch_acos","text":"Returns new tensor arccosine  elements input. $$     \\mbox{}_{} = \\cos^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acos — torch_acos","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_acos(a) } #> torch_tensor #>  1.9000 #>     nan #>  2.5559 #>     nan #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Acosh — torch_acosh","title":"Acosh — torch_acosh","text":"Acosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acosh — torch_acosh","text":"","code":"torch_acosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acosh — torch_acosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acosh — torch_acosh","text":"domain inverse hyperbolic cosine [1, inf) values outside range mapped NaN, except + INF output mapped + INF. $$     \\mbox{}_{} = \\cosh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"acosh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"acosh(input, *, out=None) -> Tensor","title":"Acosh — torch_acosh","text":"Returns new tensor inverse hyperbolic cosine elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acosh — torch_acosh","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4))$uniform_(1, 2) a torch_acosh(a) } #> torch_tensor #>  1.0694 #>  0.5761 #>  0.8503 #>  0.6869 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"Adaptive_avg_pool1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"","code":"torch_adaptive_avg_pool1d(self, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"self input tensor output_size target output size (single integer)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"adaptive-avg-pool-d-input-output-size-gt-tensor-","dir":"Reference","previous_headings":"","what":"adaptive_avg_pool1d(input, output_size) -> Tensor","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"Applies 1D adaptive average pooling input signal composed several input planes. See nn_adaptive_avg_pool1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add — torch_add","title":"Add — torch_add","text":"Add","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add — torch_add","text":"","code":"torch_add(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add — torch_add","text":"self (Tensor) input tensor. (Tensor/Number) second input tensor/number. alpha (Number) scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"add-input-other-out-null-","dir":"Reference","previous_headings":"","what":"add(input, other, out=NULL)","title":"Add — torch_add","text":"Adds scalar element input input returns new resulting tensor. $$     \\mbox{} = \\mbox{input} + \\mbox{} $$ input type FloatTensor DoubleTensor, must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"add-input-other-alpha-out-null-","dir":"Reference","previous_headings":"","what":"add(input, other, *, alpha=1, out=NULL)","title":"Add — torch_add","text":"element tensor multiplied scalar alpha added element tensor input. resulting tensor returned. shapes input must broadcastable . $$     \\mbox{} = \\mbox{input} + \\mbox{alpha} \\times \\mbox{} $$ type FloatTensor DoubleTensor, alpha must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add — torch_add","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_add(a, 20)   a = torch_randn(c(4)) a b = torch_randn(c(4, 1)) b torch_add(a, b) } #> torch_tensor #> -1.9521 -1.7848 -1.0599 -1.0725 #>  0.3564  0.5238  1.2487  1.2360 #> -0.8335 -0.6662  0.0587  0.0461 #> -0.5819 -0.4146  0.3103  0.2977 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Addbmm — torch_addbmm","title":"Addbmm — torch_addbmm","text":"Addbmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addbmm — torch_addbmm","text":"","code":"torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addbmm — torch_addbmm","text":"self (Tensor) matrix added batch1 (Tensor) first batch matrices multiplied batch2 (Tensor) second batch matrices multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier batch1 @ batch2 (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addbmm — torch_addbmm","text":"Performs batch matrix-matrix product matrices stored batch1 batch2, reduced add step (matrix multiplications get accumulated along first dimension). input added final result. batch1 batch2 must 3-D tensors containing number matrices. batch1 \\((b \\times n \\times m)\\) tensor, batch2 \\((b \\times m \\times p)\\) tensor, input must broadcastable  \\((n \\times p)\\) tensor \\((n \\times p)\\) tensor. $$     = \\beta\\ \\mbox{input} + \\alpha\\ (\\sum_{=0}^{b-1} \\mbox{batch1}_i \\mathbin{@} \\mbox{batch2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addbmm — torch_addbmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(3, 5)) batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_addbmm(M, batch1, batch2) } #> torch_tensor #> -7.4262 -4.7504 -1.8705  7.8178 -5.9200 #>  7.8648 -0.8338  1.4864 -9.0308 -8.3291 #>  0.4138 -3.4227  3.5947 -0.8622 -5.4645 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":null,"dir":"Reference","previous_headings":"","what":"Addcdiv — torch_addcdiv","title":"Addcdiv — torch_addcdiv","text":"Addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addcdiv — torch_addcdiv","text":"","code":"torch_addcdiv(self, tensor1, tensor2, value = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addcdiv — torch_addcdiv","text":"self (Tensor) tensor added tensor1 (Tensor) numerator tensor tensor2 (Tensor) denominator tensor value (Number, optional) multiplier \\(\\mbox{tensor1} / \\mbox{tensor2}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"addcdiv-input-tensor-tensor-value-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor","title":"Addcdiv — torch_addcdiv","text":"Performs element-wise division tensor1 tensor2, multiply result scalar value add input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Addcdiv — torch_addcdiv","text":"Integer division addcdiv deprecated, future release addcdiv perform true division tensor1 tensor2. current addcdiv behavior can replicated using torch_floor_divide() integral inputs (input + value * tensor1 // tensor2) torch_div() float inputs (input + value * tensor1 / tensor2). new addcdiv behavior can implemented torch_true_divide() (input + value * torch.true_divide(tensor1, tensor2). $$     \\mbox{}_i = \\mbox{input}_i + \\mbox{value} \\times \\frac{\\mbox{tensor1}_i}{\\mbox{tensor2}_i} $$ shapes input, tensor1, tensor2 must broadcastable . inputs type FloatTensor DoubleTensor, value must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addcdiv — torch_addcdiv","text":"","code":"if (torch_is_installed()) {  t = torch_randn(c(1, 3)) t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcdiv(t, t1, t2, 0.1) } #> torch_tensor #>  0.4138 -3.3908 -0.0976 #>  0.1676 -0.6541 -0.6481 #>  0.3376 -2.5442 -0.2679 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Addcmul — torch_addcmul","title":"Addcmul — torch_addcmul","text":"Addcmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addcmul — torch_addcmul","text":"","code":"torch_addcmul(self, tensor1, tensor2, value = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addcmul — torch_addcmul","text":"self (Tensor) tensor added tensor1 (Tensor) tensor multiplied tensor2 (Tensor) tensor multiplied value (Number, optional) multiplier \\(tensor1 .* tensor2\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"addcmul-input-tensor-tensor-value-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor","title":"Addcmul — torch_addcmul","text":"Performs element-wise multiplication tensor1 tensor2, multiply result scalar value add input. $$     \\mbox{}_i = \\mbox{input}_i + \\mbox{value} \\times \\mbox{tensor1}_i \\times \\mbox{tensor2}_i $$ shapes tensor, tensor1, tensor2 must broadcastable . inputs type FloatTensor DoubleTensor, value must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addcmul — torch_addcmul","text":"","code":"if (torch_is_installed()) {  t = torch_randn(c(1, 3)) t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcmul(t, t1, t2, 0.1) } #> torch_tensor #> -1.0627  2.9080 -0.3882 #> -0.6749  2.6368 -0.3886 #> -0.7420  2.6837 -0.3886 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Addmm — torch_addmm","title":"Addmm — torch_addmm","text":"Addmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addmm — torch_addmm","text":"","code":"torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addmm — torch_addmm","text":"self (Tensor) matrix added mat1 (Tensor) first matrix multiplied mat2 (Tensor) second matrix multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(mat1 @ mat2\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addmm — torch_addmm","text":"Performs matrix multiplication matrices mat1 mat2. matrix input added final result. mat1 \\((n \\times m)\\) tensor, mat2 \\((m \\times p)\\) tensor, input must broadcastable  \\((n \\times p)\\) tensor \\((n \\times p)\\) tensor. alpha beta scaling factors matrix-vector product mat1 mat2 added matrix input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{mat1}_i \\mathbin{@} \\mbox{mat2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addmm — torch_addmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(2, 3)) mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_addmm(M, mat1, mat2) } #> torch_tensor #> -0.8938 -1.2562 -0.4730 #> -3.5750  0.6293  1.7723 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":null,"dir":"Reference","previous_headings":"","what":"Addmv — torch_addmv","title":"Addmv — torch_addmv","text":"Addmv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addmv — torch_addmv","text":"","code":"torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addmv — torch_addmv","text":"self (Tensor) vector added mat (Tensor) matrix multiplied vec (Tensor) vector multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(mat @ vec\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addmv — torch_addmv","text":"Performs matrix-vector product matrix mat vector vec. vector input added final result. mat \\((n \\times m)\\) tensor, vec 1-D tensor size m, input must broadcastable  1-D tensor size n 1-D tensor size n. alpha beta scaling factors matrix-vector product mat vec added tensor input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{mat} \\mathbin{@} \\mbox{vec}) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addmv — torch_addmv","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(2)) mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_addmv(M, mat, vec) } #> torch_tensor #>  0.4158 #>  2.1916 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":null,"dir":"Reference","previous_headings":"","what":"Addr — torch_addr","title":"Addr — torch_addr","text":"Addr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addr — torch_addr","text":"","code":"torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addr — torch_addr","text":"self (Tensor) matrix added vec1 (Tensor) first vector outer product vec2 (Tensor) second vector outer product beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(\\mbox{vec1} \\otimes \\mbox{vec2}\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"addr-input-vec-vec-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addr — torch_addr","text":"Performs outer-product vectors vec1 vec2 adds matrix input. Optional values beta alpha scaling factors outer product vec1 vec2 added matrix input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{vec1} \\otimes \\mbox{vec2}) $$ vec1 vector size n vec2 vector size m, input must broadcastable  matrix size \\((n \\times m)\\) matrix size \\((n \\times m)\\). inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addr — torch_addr","text":"","code":"if (torch_is_installed()) {  vec1 = torch_arange(1, 3) vec2 = torch_arange(1, 2) M = torch_zeros(c(3, 2)) torch_addr(M, vec1, vec2) } #> torch_tensor #>  1  2 #>  2  4 #>  3  6 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Allclose — torch_allclose","title":"Allclose — torch_allclose","text":"Allclose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allclose — torch_allclose","text":"","code":"torch_allclose(self, other, rtol = 1e-05, atol = 1e-08, equal_nan = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allclose — torch_allclose","text":"self (Tensor) first tensor compare (Tensor) second tensor compare rtol (float, optional) relative tolerance. Default: 1e-05 atol (float, optional) absolute tolerance. Default: 1e-08 equal_nan (bool, optional) TRUE, two NaN s compared equal. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-","dir":"Reference","previous_headings":"","what":"allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool","title":"Allclose — torch_allclose","text":"function checks input satisfy condition: $$     \\vert \\mbox{input} - \\mbox{} \\vert \\leq \\mbox{atol} + \\mbox{rtol} \\times \\vert \\mbox{} \\vert $$ elementwise, elements input . behaviour function analogous numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Allclose — torch_allclose","text":"","code":"if (torch_is_installed()) {  torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08))) torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09))) torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN))) torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":null,"dir":"Reference","previous_headings":"","what":"Amax — torch_amax","title":"Amax — torch_amax","text":"Amax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Amax — torch_amax","text":"","code":"torch_amax(self, dim = list(), keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Amax — torch_amax","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Amax — torch_amax","text":"difference max/min amax/amin : amax/amin supports reducing multiple dimensions, amax/amin return indices, amax/amin evenly distributes gradient equal values, max(dim)/min(dim) propagates gradient single index source tensor. keepdim TRUE, output tensors size inputexcept dimension(s)dimwhere size 1. Otherwise,dims squeezed (see [torch_squeeze()]), resulting output tensors fewer dimension input`.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"amax-input-dim-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"amax(input, dim, keepdim=FALSE, *, out=None) -> Tensor","title":"Amax — torch_amax","text":"Returns maximum value slice input tensor given dimension(s) dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Amax — torch_amax","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4, 4)) a torch_amax(a, 1) } #> torch_tensor #>  0.7694 #>  1.3002 #>  0.8770 #> -0.3539 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":null,"dir":"Reference","previous_headings":"","what":"Amin — torch_amin","title":"Amin — torch_amin","text":"Amin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Amin — torch_amin","text":"","code":"torch_amin(self, dim = list(), keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Amin — torch_amin","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Amin — torch_amin","text":"difference max/min amax/amin : amax/amin supports reducing multiple dimensions, amax/amin return indices, amax/amin evenly distributes gradient equal values, max(dim)/min(dim) propagates gradient single index source tensor. keepdim TRUE, output tensors size input except dimension(s) dim size 1. Otherwise, dims squeezed (see torch_squeeze()), resulting output tensors fewer dimensions input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"amin-input-dim-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"amin(input, dim, keepdim=FALSE, *, out=None) -> Tensor","title":"Amin — torch_amin","text":"Returns minimum value slice input tensor given dimension(s) dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Amin — torch_amin","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4, 4)) a torch_amin(a, 1) } #> torch_tensor #> -0.8406 #> -0.5179 #> -0.9084 #> -2.1076 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":null,"dir":"Reference","previous_headings":"","what":"Angle — torch_angle","title":"Angle — torch_angle","text":"Angle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Angle — torch_angle","text":"","code":"torch_angle(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Angle — torch_angle","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"angle-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"angle(input) -> Tensor","title":"Angle — torch_angle","text":"Computes element-wise angle (radians) given input tensor. $$     \\mbox{}_{} = angle(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Angle — torch_angle","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159 }  }"},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Arange — torch_arange","title":"Arange — torch_arange","text":"Arange","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arange — torch_arange","text":"","code":"torch_arange(   start,   end,   step = 1L,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arange — torch_arange","text":"start (Number) starting value set points. Default: 0. end (Number) ending value set points step (Number) gap pair adjacent points. Default: 1. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Arange — torch_arange","text":"Returns 1-D tensor size \\(\\left\\lceil \\frac{\\mbox{end} - \\mbox{start}}{\\mbox{step}} \\right\\rceil\\) values interval [start, end) taken common difference step beginning start. Note non-integer step subject floating point rounding errors comparing end; avoid inconsistency, advise adding small epsilon end cases. $$     \\mbox{}_{{+1}} = \\mbox{}_{} + \\mbox{step} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arange — torch_arange","text":"","code":"if (torch_is_installed()) {  torch_arange(start = 0, end = 5) torch_arange(1, 4) torch_arange(1, 2.5, 0.5) } #> torch_tensor #>  1.0000 #>  1.5000 #>  2.0000 #>  2.5000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Arccos — torch_arccos","title":"Arccos — torch_arccos","text":"Arccos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arccos — torch_arccos","text":"","code":"torch_arccos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arccos — torch_arccos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"arccos-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arccos(input, *, out=None) -> Tensor","title":"Arccos — torch_arccos","text":"Alias torch_acos().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arccosh — torch_arccosh","title":"Arccosh — torch_arccosh","text":"Arccosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arccosh — torch_arccosh","text":"","code":"torch_arccosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arccosh — torch_arccosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"arccosh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arccosh(input, *, out=None) -> Tensor","title":"Arccosh — torch_arccosh","text":"Alias torch_acosh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Arcsin — torch_arcsin","title":"Arcsin — torch_arcsin","text":"Arcsin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arcsin — torch_arcsin","text":"","code":"torch_arcsin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arcsin — torch_arcsin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"arcsin-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arcsin(input, *, out=None) -> Tensor","title":"Arcsin — torch_arcsin","text":"Alias torch_asin().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arcsinh — torch_arcsinh","title":"Arcsinh — torch_arcsinh","text":"Arcsinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arcsinh — torch_arcsinh","text":"","code":"torch_arcsinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arcsinh — torch_arcsinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"arcsinh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arcsinh(input, *, out=None) -> Tensor","title":"Arcsinh — torch_arcsinh","text":"Alias torch_asinh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctan — torch_arctan","title":"Arctan — torch_arctan","text":"Arctan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctan — torch_arctan","text":"","code":"torch_arctan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arctan — torch_arctan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"arctan-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arctan(input, *, out=None) -> Tensor","title":"Arctan — torch_arctan","text":"Alias torch_atan().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctanh — torch_arctanh","title":"Arctanh — torch_arctanh","text":"Arctanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctanh — torch_arctanh","text":"","code":"torch_arctanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arctanh — torch_arctanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"arctanh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arctanh(input, *, out=None) -> Tensor","title":"Arctanh — torch_arctanh","text":"Alias torch_atanh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Argmax — torch_argmax","title":"Argmax — torch_argmax","text":"Argmax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argmax — torch_argmax","text":"self (Tensor) input tensor. dim (int) dimension reduce. NULL, argmax flattened input returned. keepdim (bool) whether output tensor dim retained . Ignored dim=NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"argmax-input-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmax(input) -> LongTensor","title":"Argmax — torch_argmax","text":"Returns indices maximum value elements input tensor. second value returned torch_max. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"argmax-input-dim-keepdim-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmax(input, dim, keepdim=False) -> LongTensor","title":"Argmax — torch_argmax","text":"Returns indices maximum values tensor across dimension. second value returned torch_max. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argmax — torch_argmax","text":"","code":"if (torch_is_installed()) {  if (FALSE) { a = torch_randn(c(4, 4)) a torch_argmax(a) }   a = torch_randn(c(4, 4)) a torch_argmax(a, dim=1) } #> torch_tensor #>  1 #>  3 #>  4 #>  1 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Argmin — torch_argmin","title":"Argmin — torch_argmin","text":"Argmin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argmin — torch_argmin","text":"self (Tensor) input tensor. dim (int) dimension reduce. NULL, argmin flattened input returned. keepdim (bool) whether output tensor dim retained . Ignored dim=NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"argmin-input-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmin(input) -> LongTensor","title":"Argmin — torch_argmin","text":"Returns indices minimum value elements input tensor. second value returned torch_min. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"argmin-input-dim-keepdim-false-out-null-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmin(input, dim, keepdim=False, out=NULL) -> LongTensor","title":"Argmin — torch_argmin","text":"Returns indices minimum values tensor across dimension. second value returned torch_min. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argmin — torch_argmin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 4)) a torch_argmin(a)   a = torch_randn(c(4, 4)) a torch_argmin(a, dim=1) } #> torch_tensor #>  2 #>  2 #>  4 #>  4 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Argsort — torch_argsort","title":"Argsort — torch_argsort","text":"Argsort","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Argsort — torch_argsort","text":"","code":"torch_argsort(self, dim = -1L, descending = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argsort — torch_argsort","text":"self (Tensor) input tensor. dim (int, optional) dimension sort along descending (bool, optional) controls sorting order (ascending descending)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"argsort-input-dim-descending-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argsort(input, dim=-1, descending=False) -> LongTensor","title":"Argsort — torch_argsort","text":"Returns indices sort tensor along given dimension ascending order value. second value returned torch_sort.  See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argsort — torch_argsort","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 4)) a torch_argsort(a, dim=1) } #> torch_tensor #>  1  2  1  2 #>  2  4  2  1 #>  3  3  4  4 #>  4  1  3  3 #> [ CPULongType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":null,"dir":"Reference","previous_headings":"","what":"As_strided — torch_as_strided","title":"As_strided — torch_as_strided","text":"As_strided","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"As_strided — torch_as_strided","text":"","code":"torch_as_strided(self, size, stride, storage_offset = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"As_strided — torch_as_strided","text":"self (Tensor) input tensor. size (tuple ints) shape output tensor stride (tuple ints) stride output tensor storage_offset (int, optional) offset underlying storage output tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"as-strided-input-size-stride-storage-offset-gt-tensor-","dir":"Reference","previous_headings":"","what":"as_strided(input, size, stride, storage_offset=0) -> Tensor","title":"As_strided — torch_as_strided","text":"Create view existing torch_Tensor input specified size, stride storage_offset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"As_strided — torch_as_strided","text":"one element created tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":"Many PyTorch functions, which return a view of a tensor, are internally implemented with this function. Those functions, like `torch_Tensor.expand`, are easier to read and are therefore more advisable to use."},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"As_strided — torch_as_strided","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 3)) x t = torch_as_strided(x, list(2, 2), list(1, 2)) t t = torch_as_strided(x, list(2, 2), list(1, 2), 1) t } #> torch_tensor #> -1.0386  0.9525 #> -0.9742 -0.9887 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":null,"dir":"Reference","previous_headings":"","what":"Asin — torch_asin","title":"Asin — torch_asin","text":"Asin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asin — torch_asin","text":"","code":"torch_asin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asin — torch_asin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"asin-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"asin(input, out=NULL) -> Tensor","title":"Asin — torch_asin","text":"Returns new tensor arcsine  elements input. $$     \\mbox{}_{} = \\sin^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asin — torch_asin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_asin(a) } #> torch_tensor #>  0.9477 #> -0.2969 #>     nan #>  0.0865 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Asinh — torch_asinh","title":"Asinh — torch_asinh","text":"Asinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asinh — torch_asinh","text":"","code":"torch_asinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asinh — torch_asinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"asinh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"asinh(input, *, out=None) -> Tensor","title":"Asinh — torch_asinh","text":"Returns new tensor inverse hyperbolic sine elements input. $$     \\mbox{}_{} = \\sinh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asinh — torch_asinh","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4)) a torch_asinh(a) } #> torch_tensor #> -0.6463 #> -1.0318 #>  1.3634 #> -0.6190 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":null,"dir":"Reference","previous_headings":"","what":"Atan — torch_atan","title":"Atan — torch_atan","text":"Atan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atan — torch_atan","text":"","code":"torch_atan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atan — torch_atan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"atan-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"atan(input, out=NULL) -> Tensor","title":"Atan — torch_atan","text":"Returns new tensor arctangent  elements input. $$     \\mbox{}_{} = \\tan^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atan — torch_atan","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_atan(a) } #> torch_tensor #>  0.3500 #> -1.0859 #> -1.1293 #> -0.2667 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Atan2 — torch_atan2","title":"Atan2 — torch_atan2","text":"Atan2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atan2 — torch_atan2","text":"","code":"torch_atan2(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atan2 — torch_atan2","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"atan-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"atan2(input, other, out=NULL) -> Tensor","title":"Atan2 — torch_atan2","text":"Element-wise arctangent \\(\\mbox{input}_{} / \\mbox{}_{}\\) consideration quadrant. Returns new tensor signed angles radians vector \\((\\mbox{}_{}, \\mbox{input}_{})\\) vector \\((1, 0)\\). (Note \\(\\mbox{}_{}\\), second parameter, x-coordinate, \\(\\mbox{input}_{}\\), first parameter, y-coordinate.) shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atan2 — torch_atan2","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_atan2(a, torch_randn(c(4))) } #> torch_tensor #> -0.0465 #> -2.7011 #>  2.7868 #>  0.5454 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Atanh — torch_atanh","title":"Atanh — torch_atanh","text":"Atanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atanh — torch_atanh","text":"","code":"torch_atanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atanh — torch_atanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Atanh — torch_atanh","text":"domain inverse hyperbolic tangent (-1, 1) values outside range mapped NaN, except values 1 -1 output mapped +/-INF respectively. $$     \\mbox{}_{} = \\tanh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"atanh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"atanh(input, *, out=None) -> Tensor","title":"Atanh — torch_atanh","text":"Returns new tensor inverse hyperbolic tangent elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atanh — torch_atanh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4))$uniform_(-1, 1) a torch_atanh(a) } #> torch_tensor #> -0.0134 #> -0.1849 #> -0.0527 #>  0.9015 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_1d — torch_atleast_1d","title":"Atleast_1d — torch_atleast_1d","text":"Returns 1-dimensional view input tensor zero dimensions. Input tensors one dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_1d — torch_atleast_1d","text":"","code":"torch_atleast_1d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_1d — torch_atleast_1d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atleast_1d — torch_atleast_1d","text":"","code":"if (torch_is_installed()) {  x <- torch_randn(c(2)) x torch_atleast_1d(x) x <- torch_tensor(1.) x torch_atleast_1d(x) x <- torch_tensor(0.5) y <- torch_tensor(1.) torch_atleast_1d(list(x,y)) } #> [[1]] #> torch_tensor #>  0.5000 #> [ CPUFloatType{1} ] #>  #> [[2]] #> torch_tensor #>  1 #> [ CPUFloatType{1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_2d — torch_atleast_2d","title":"Atleast_2d — torch_atleast_2d","text":"Returns 2-dimensional view input tensor zero dimensions. Input tensors two dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_2d — torch_atleast_2d","text":"","code":"torch_atleast_2d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_2d — torch_atleast_2d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atleast_2d — torch_atleast_2d","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(1.) x torch_atleast_2d(x) x <- torch_randn(c(2,2)) x torch_atleast_2d(x) x <- torch_tensor(0.5) y <- torch_tensor(1.) torch_atleast_2d(list(x,y)) } #> [[1]] #> torch_tensor #>  0.5000 #> [ CPUFloatType{1,1} ] #>  #> [[2]] #> torch_tensor #>  1 #> [ CPUFloatType{1,1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_3d — torch_atleast_3d","title":"Atleast_3d — torch_atleast_3d","text":"Returns 3-dimensional view input tensor zero dimensions. Input tensors three dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_3d — torch_atleast_3d","text":"","code":"torch_atleast_3d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_3d — torch_atleast_3d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool1d — torch_avg_pool1d","title":"Avg_pool1d — torch_avg_pool1d","text":"Avg_pool1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool1d — torch_avg_pool1d","text":"","code":"torch_avg_pool1d(   self,   kernel_size,   stride = list(),   padding = 0L,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool1d — torch_avg_pool1d","text":"self input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) kernel_size size window. Can single number tuple (kW,) stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 ceil_mode TRUE, use ceil instead floor compute output shape. Default: FALSE count_include_pad TRUE, include zero-padding averaging calculation. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor","title":"Avg_pool1d — torch_avg_pool1d","text":"Applies 1D average pooling input signal composed several input planes. See nn_avg_pool1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Baddbmm — torch_baddbmm","title":"Baddbmm — torch_baddbmm","text":"Baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baddbmm — torch_baddbmm","text":"","code":"torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baddbmm — torch_baddbmm","text":"self (Tensor) tensor added batch1 (Tensor) first batch matrices multiplied batch2 (Tensor) second batch matrices multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(\\mbox{batch1} \\mathbin{@} \\mbox{batch2}\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Baddbmm — torch_baddbmm","text":"Performs batch matrix-matrix product matrices batch1 batch2. input added final result. batch1 batch2 must 3-D tensors containing number matrices. batch1 \\((b \\times n \\times m)\\) tensor, batch2 \\((b \\times m \\times p)\\) tensor, input must broadcastable  \\((b \\times n \\times p)\\) tensor \\((b \\times n \\times p)\\) tensor. alpha beta mean scaling factors used torch_addbmm. $$     \\mbox{}_i = \\beta\\ \\mbox{input}_i + \\alpha\\ (\\mbox{batch1}_i \\mathbin{@} \\mbox{batch2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Baddbmm — torch_baddbmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(10, 3, 5)) batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_baddbmm(M, batch1, batch2) } #> torch_tensor #> (1,.,.) =  #>   0.5111 -3.1852  3.7126 -1.3524  3.9963 #>   4.7965 -0.0369 -0.3263 -1.2753  0.0556 #>   2.3793  0.1486  0.1915 -0.4350  1.2126 #>  #> (2,.,.) =  #>   1.5858 -1.5813 -0.6729 -1.2245 -1.2957 #>   2.2479  0.2300  0.6143  1.7530  0.5099 #>  -2.0699  2.4970  0.9025 -0.7765  1.3482 #>  #> (3,.,.) =  #>   1.8821  0.1650  2.2522 -0.3192 -0.3841 #>  -3.8732 -1.4290  0.6768 -1.7308  3.1746 #>   0.3767 -0.8012 -0.3291  0.9759 -1.7650 #>  #> (4,.,.) =  #>   0.0757  1.7507  2.5247 -2.5280  0.1248 #>   2.0840  1.4858  0.4577 -1.2264  1.5897 #>   2.7945  0.7971 -0.6055 -0.2607  0.7605 #>  #> (5,.,.) =  #>   0.7051  0.2898  1.5222 -0.2621 -2.5265 #>  -0.4269 -1.8950 -2.1202  1.3455  0.5589 #>   3.2963  5.4668  4.0805 -4.1564 -2.0503 #>  #> (6,.,.) =  #>   0.5190 -0.5352 -1.4169 -0.1021  2.8163 #>  -0.1018  0.0950  1.8032 -1.3999 -1.6487 #>  -0.2259  2.6865  4.0377  1.6081  1.1882 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Bartlett_window — torch_bartlett_window","title":"Bartlett_window — torch_bartlett_window","text":"Bartlett_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bartlett_window — torch_bartlett_window","text":"","code":"torch_bartlett_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bartlett_window — torch_bartlett_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bartlett_window — torch_bartlett_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Bartlett_window — torch_bartlett_window","text":"Bartlett window function. $$     w[n] = 1 - \\left| \\frac{2n}{N-1} - 1 \\right| = \\left\\{ \\begin{array}{ll}         \\frac{2n}{N - 1} & \\mbox{} 0 \\leq n \\leq \\frac{N - 1}{2} \\\\         2 - \\frac{2n}{N - 1} & \\mbox{} \\frac{N - 1}{2} < n < N \\\\     \\end{array}     \\right. , $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_bartlett_window(L, periodic=TRUE) equal torch_bartlett_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Bernoulli — torch_bernoulli","title":"Bernoulli — torch_bernoulli","text":"Bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bernoulli — torch_bernoulli","text":"","code":"torch_bernoulli(self, p, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bernoulli — torch_bernoulli","text":"self (Tensor) input tensor probability values Bernoulli distribution p (Number) probability value. p passed used instead values self tensor. generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"bernoulli-input-generator-null-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bernoulli(input, *, generator=NULL, out=NULL) -> Tensor","title":"Bernoulli — torch_bernoulli","text":"Draws binary random numbers (0 1) Bernoulli distribution. input tensor tensor containing probabilities used drawing binary random number. Hence, values input range: \\(0 \\leq \\mbox{input}_i \\leq 1\\). \\(\\mbox{}^{th}\\) element output tensor draw value \\(1\\) according \\(\\mbox{}^{th}\\) probability value given input. $$     \\mbox{}_{} \\sim \\mathrm{Bernoulli}(p = \\mbox{input}_{}) $$ returned tensor values 0 1 shape input. can integral dtype, input must floating point dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bernoulli — torch_bernoulli","text":"","code":"if (torch_is_installed()) {  a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1) a torch_bernoulli(a) a = torch_ones(c(3, 3)) # probability of drawing \"1\" is 1 torch_bernoulli(a) a = torch_zeros(c(3, 3)) # probability of drawing \"1\" is 0 torch_bernoulli(a) } #> torch_tensor #>  0  0  0 #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Bincount — torch_bincount","title":"Bincount — torch_bincount","text":"Bincount","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bincount — torch_bincount","text":"self (Tensor) 1-d int tensor weights (Tensor) optional, weight value input tensor.        size input tensor. minlength (int) optional, minimum number bins. non-negative.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"bincount-input-weights-null-minlength-gt-tensor-","dir":"Reference","previous_headings":"","what":"bincount(input, weights=NULL, minlength=0) -> Tensor","title":"Bincount — torch_bincount","text":"Count frequency value array non-negative ints. number bins (size 1) one larger largest value input unless input empty, case result tensor size 0. minlength specified, number bins least minlength input empty, result tensor size minlength filled zeros. n value position , [n] += weights[] weights specified else [n] += 1. .. include:: cuda_deterministic.rst","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bincount — torch_bincount","text":"","code":"if (torch_is_installed()) {  input = torch_randint(1, 8, list(5), dtype=torch_int64()) weights = torch_linspace(0, 1, steps=5) input weights torch_bincount(input, weights) input$bincount(weights) } #> torch_tensor #>  0.0000 #>  0.5000 #>  0.0000 #>  0.0000 #>  1.0000 #>  1.0000 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_and — torch_bitwise_and","title":"Bitwise_and — torch_bitwise_and","text":"Bitwise_and","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_and — torch_bitwise_and","text":"","code":"torch_bitwise_and(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_and — torch_bitwise_and","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"bitwise-and-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_and(input, other, out=NULL) -> Tensor","title":"Bitwise_and — torch_bitwise_and","text":"Computes bitwise input . input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_not — torch_bitwise_not","title":"Bitwise_not — torch_bitwise_not","text":"Bitwise_not","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_not — torch_bitwise_not","text":"","code":"torch_bitwise_not(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_not — torch_bitwise_not","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"bitwise-not-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_not(input, out=NULL) -> Tensor","title":"Bitwise_not — torch_bitwise_not","text":"Computes bitwise given input tensor. input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_or — torch_bitwise_or","title":"Bitwise_or — torch_bitwise_or","text":"Bitwise_or","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_or — torch_bitwise_or","text":"","code":"torch_bitwise_or(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_or — torch_bitwise_or","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"bitwise-or-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_or(input, other, out=NULL) -> Tensor","title":"Bitwise_or — torch_bitwise_or","text":"Computes bitwise input . input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_xor — torch_bitwise_xor","title":"Bitwise_xor — torch_bitwise_xor","text":"Bitwise_xor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_xor — torch_bitwise_xor","text":"","code":"torch_bitwise_xor(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_xor — torch_bitwise_xor","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"bitwise-xor-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_xor(input, other, out=NULL) -> Tensor","title":"Bitwise_xor — torch_bitwise_xor","text":"Computes bitwise XOR input . input tensor must integral Boolean types. bool tensors, computes logical XOR.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Blackman_window — torch_blackman_window","title":"Blackman_window — torch_blackman_window","text":"Blackman_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blackman_window — torch_blackman_window","text":"","code":"torch_blackman_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blackman_window — torch_blackman_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Blackman_window — torch_blackman_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Blackman_window — torch_blackman_window","text":"Blackman window function. $$     w[n] = 0.42 - 0.5 \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right) + 0.08 \\cos \\left( \\frac{4 \\pi n}{N - 1} \\right) $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_blackman_window(L, periodic=TRUE) equal torch_blackman_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Block_diag — torch_block_diag","title":"Block_diag — torch_block_diag","text":"Create block diagonal matrix provided tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Block_diag — torch_block_diag","text":"","code":"torch_block_diag(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Block_diag — torch_block_diag","text":"tensors (list tensors) One tensors 0, 1, 2 dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Block_diag — torch_block_diag","text":"","code":"if (torch_is_installed()) {  A <- torch_tensor(rbind(c(0, 1), c(1, 0))) B <- torch_tensor(rbind(c(3, 4, 5), c(6, 7, 8))) C <- torch_tensor(7) D <- torch_tensor(c(1, 2, 3)) E <- torch_tensor(rbind(4, 5, 6)) torch_block_diag(list(A, B, C, D, E)) } #> torch_tensor #>  0  1  0  0  0  0  0  0  0  0 #>  1  0  0  0  0  0  0  0  0  0 #>  0  0  3  4  5  0  0  0  0  0 #>  0  0  6  7  8  0  0  0  0  0 #>  0  0  0  0  0  7  0  0  0  0 #>  0  0  0  0  0  0  1  2  3  0 #>  0  0  0  0  0  0  0  0  0  4 #>  0  0  0  0  0  0  0  0  0  5 #>  0  0  0  0  0  0  0  0  0  6 #> [ CPUFloatType{9,10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Bmm — torch_bmm","title":"Bmm — torch_bmm","text":"Bmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bmm — torch_bmm","text":"","code":"torch_bmm(self, mat2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bmm — torch_bmm","text":"self (Tensor) first batch matrices multiplied mat2 (Tensor) second batch matrices multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bmm — torch_bmm","text":"function broadcast . broadcasting matrix products, see torch_matmul.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"bmm-input-mat-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bmm(input, mat2, out=NULL) -> Tensor","title":"Bmm — torch_bmm","text":"Performs batch matrix-matrix product matrices stored input mat2. input mat2 must 3-D tensors containing number matrices. input \\((b \\times n \\times m)\\) tensor, mat2 \\((b \\times m \\times p)\\) tensor, \\((b \\times n \\times p)\\) tensor. $$     \\mbox{}_i = \\mbox{input}_i \\mathbin{@} \\mbox{mat2}_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bmm — torch_bmm","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(10, 3, 4)) mat2 = torch_randn(c(10, 4, 5)) res = torch_bmm(input, mat2) res } #> torch_tensor #> (1,.,.) =  #>  -1.7765 -0.1350  0.8466  0.8478 -0.7594 #>   1.6370 -0.2408  0.3351  2.2860 -0.6668 #>   1.9089  0.2050 -1.6219 -2.5549  1.7960 #>  #> (2,.,.) =  #>  -1.8824  2.0253 -2.1206 -1.3713  0.0871 #>   2.8014 -0.8317 -0.6866 -1.2809  2.3431 #>   0.1755 -3.8833  0.2988  7.5040 -3.6713 #>  #> (3,.,.) =  #>   0.8480 -0.0850  1.4366  2.9513 -0.3949 #>  -0.9064 -0.8385  1.3575 -7.3166 -7.2062 #>   0.5430 -0.3684  0.4283  0.9606  2.1168 #>  #> (4,.,.) =  #>  -2.3839  1.7347  1.1756 -1.6514  0.6408 #>   1.6447 -1.2290  1.8483  1.1605 -3.7812 #>   1.8861  0.1105  0.4390  1.3200 -0.8549 #>  #> (5,.,.) =  #>   0.4684  1.1384  1.3886  1.1070 -1.7678 #>   3.1111  0.6010  1.0678 -0.0603 -2.4754 #>   1.0265 -0.3575  0.2944 -1.0106 -2.2023 #>  #> (6,.,.) =  #>   0.3164 -1.3392  0.5580 -1.6123  5.0994 #>   1.6859 -1.7957 -0.2757  0.7590  0.4473 #>  -0.4207  0.6214  0.0980  0.1298 -0.7558 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast_tensors — torch_broadcast_tensors","title":"Broadcast_tensors — torch_broadcast_tensors","text":"Broadcast_tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast_tensors — torch_broadcast_tensors","text":"","code":"torch_broadcast_tensors(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast_tensors — torch_broadcast_tensors","text":"tensors list containing number tensors type","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"broadcast-tensors-tensors-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"broadcast_tensors(tensors) -> List of Tensors","title":"Broadcast_tensors — torch_broadcast_tensors","text":"Broadcasts given tensors according broadcasting-semantics.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast_tensors — torch_broadcast_tensors","text":"","code":"if (torch_is_installed()) {  x = torch_arange(0, 3)$view(c(1, 4)) y = torch_arange(0, 2)$view(c(3, 1)) out = torch_broadcast_tensors(list(x, y)) out[[1]] } #> torch_tensor #>  0  1  2  3 #>  0  1  2  3 #>  0  1  2  3 #> [ CPUFloatType{3,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucketize — torch_bucketize","title":"Bucketize — torch_bucketize","text":"Bucketize","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucketize — torch_bucketize","text":"","code":"torch_bucketize(self, boundaries, out_int32 = FALSE, right = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucketize — torch_bucketize","text":"self (Tensor Scalar) N-D tensor Scalar containing search value(s). boundaries (Tensor) 1-D tensor, must contain monotonically increasing sequence. out_int32 (bool, optional) – indicate output data type. torch_int32() True, torch_int64() otherwise. Default value FALSE, .e. default output data type torch_int64(). right (bool, optional) – False, return first suitable location found. True, return last index. suitable index found, return 0 non-numerical value (eg. nan, inf) size boundaries (one pass last index). words, False, gets lower bound index value input boundaries. True, gets upper bound index instead. Default value False.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor","title":"Bucketize — torch_bucketize","text":"Returns indices buckets value input belongs, boundaries buckets set boundaries. Return new tensor size input. right FALSE (default), left boundary closed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucketize — torch_bucketize","text":"","code":"if (torch_is_installed()) {  boundaries <- torch_tensor(c(1, 3, 5, 7, 9)) boundaries v <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9))) v torch_bucketize(v, boundaries) torch_bucketize(v, boundaries, right=TRUE) } #> torch_tensor #>  2  3  5 #>  2  3  5 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Can_cast — torch_can_cast","title":"Can_cast — torch_can_cast","text":"Can_cast","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can_cast — torch_can_cast","text":"","code":"torch_can_cast(from, to)"},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Can_cast — torch_can_cast","text":"(dtype) original torch_dtype. (dtype) target torch_dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"can-cast-from-to-gt-bool-","dir":"Reference","previous_headings":"","what":"can_cast(from, to) -> bool","title":"Can_cast — torch_can_cast","text":"Determines type conversion allowed PyTorch casting rules described type promotion documentation .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Can_cast — torch_can_cast","text":"","code":"if (torch_is_installed()) {  torch_can_cast(torch_double(), torch_float()) torch_can_cast(torch_float(), torch_int()) } #> [1] FALSE"},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Cartesian_prod — torch_cartesian_prod","title":"Cartesian_prod — torch_cartesian_prod","text":"cartesian product given sequence tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cartesian_prod — torch_cartesian_prod","text":"","code":"torch_cartesian_prod(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cartesian_prod — torch_cartesian_prod","text":"tensors list containing number 1 dimensional tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cartesian_prod — torch_cartesian_prod","text":"","code":"if (torch_is_installed()) {  a = c(1, 2, 3) b = c(4, 5) tensor_a = torch_tensor(a) tensor_b = torch_tensor(b) torch_cartesian_prod(list(tensor_a, tensor_b)) } #> torch_tensor #>  1  4 #>  1  5 #>  2  4 #>  2  5 #>  3  4 #>  3  5 #> [ CPUFloatType{6,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":null,"dir":"Reference","previous_headings":"","what":"Cat — torch_cat","title":"Cat — torch_cat","text":"Cat","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cat — torch_cat","text":"","code":"torch_cat(tensors, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cat — torch_cat","text":"tensors (sequence Tensors) python sequence tensors type.        Non-empty tensors provided must shape, except        cat dimension. dim (int, optional) dimension tensors concatenated","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"cat-tensors-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cat(tensors, dim=0, out=NULL) -> Tensor","title":"Cat — torch_cat","text":"Concatenates given sequence seq tensors given dimension. tensors must either shape (except concatenating dimension) empty. torch_cat can seen inverse operation torch_split() torch_chunk. torch_cat can best understood via examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cat — torch_cat","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2, 3)) x torch_cat(list(x, x, x), 1) torch_cat(list(x, x, x), 2) } #> torch_tensor #>  0.3147 -0.6072 -1.1446  0.3147 -0.6072 -1.1446  0.3147 -0.6072 -1.1446 #> -0.2502  0.6148  0.3265 -0.2502  0.6148  0.3265 -0.2502  0.6148  0.3265 #> [ CPUFloatType{2,9} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cdist — torch_cdist","title":"Cdist — torch_cdist","text":"Cdist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cdist — torch_cdist","text":"","code":"torch_cdist(x1, x2, p = 2L, compute_mode = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cdist — torch_cdist","text":"x1 (Tensor) input tensor shape \\(B \\times P \\times M\\). x2 (Tensor) input tensor shape \\(B \\times R \\times M\\). p NA p value p-norm distance calculate vector pair        \\(\\[0, \\infty]\\). compute_mode NA 'use_mm_for_euclid_dist_if_necessary' - use matrix multiplication approach calculate        euclidean distance (p = 2) P > 25 R > 25        'use_mm_for_euclid_dist' - always use matrix multiplication approach calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - never use matrix multiplication approach calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Cdist — torch_cdist","text":"Computes batched p-norm distance pair two collections row vectors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Ceil — torch_ceil","title":"Ceil — torch_ceil","text":"Ceil","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ceil — torch_ceil","text":"","code":"torch_ceil(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ceil — torch_ceil","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ceil-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ceil(input, out=NULL) -> Tensor","title":"Ceil — torch_ceil","text":"Returns new tensor ceil elements input, smallest integer greater equal element. $$     \\mbox{}_{} = \\left\\lceil \\mbox{input}_{} \\right\\rceil = \\left\\lfloor \\mbox{input}_{} \\right\\rfloor + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ceil — torch_ceil","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_ceil(a) } #> torch_tensor #> -1 #>  1 #> -1 #>  1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu — torch_celu","title":"Celu — torch_celu","text":"Celu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu — torch_celu","text":"","code":"torch_celu(self, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu — torch_celu","text":"self input tensor alpha alpha value CELU formulation. Default: 1.0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"celu-input-alpha-gt-tensor-","dir":"Reference","previous_headings":"","what":"celu(input, alpha=1.) -> Tensor","title":"Celu — torch_celu","text":"See nnf_celu() info.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu_ — torch_celu_","title":"Celu_ — torch_celu_","text":"Celu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu_ — torch_celu_","text":"","code":"torch_celu_(self, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu_ — torch_celu_","text":"self input tensor alpha alpha value CELU formulation. Default: 1.0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"celu-input-alpha-gt-tensor-","dir":"Reference","previous_headings":"","what":"celu_(input, alpha=1.) -> Tensor","title":"Celu_ — torch_celu_","text":"-place version torch_celu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Chain_matmul — torch_chain_matmul","title":"Chain_matmul — torch_chain_matmul","text":"Chain_matmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chain_matmul — torch_chain_matmul","text":"","code":"torch_chain_matmul(matrices)"},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chain_matmul — torch_chain_matmul","text":"matrices (Tensors...) sequence 2 2-D tensors whose product determined.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Chain_matmul — torch_chain_matmul","text":"Returns matrix product \\(N\\) 2-D tensors. product efficiently computed using matrix chain order algorithm selects order incurs lowest cost terms arithmetic operations ([CLRS]_). Note since function compute product, \\(N\\) needs greater equal 2; equal 2 trivial matrix-matrix product returned. \\(N\\) 1, -op - original matrix returned .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Chain_matmul — torch_chain_matmul","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 4)) b = torch_randn(c(4, 5)) c = torch_randn(c(5, 6)) d = torch_randn(c(6, 7)) torch_chain_matmul(list(a, b, c, d)) } #> torch_tensor #>  -4.9114  11.1095  -3.5044  14.9431  17.5463  -4.6954  -8.2067 #>  -4.2924   2.9680  -1.0878   3.3883   7.5599  -2.4557  -3.2995 #>   5.2357  -3.2431   5.0502  -2.0051  -8.0577  -1.0972   1.1601 #> [ CPUFloatType{3,7} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Channel_shuffle — torch_channel_shuffle","title":"Channel_shuffle — torch_channel_shuffle","text":"Channel_shuffle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Channel_shuffle — torch_channel_shuffle","text":"","code":"torch_channel_shuffle(self, groups)"},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Channel_shuffle — torch_channel_shuffle","text":"self (Tensor) input tensor groups (int) number groups divide channels rearrange.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"divide-the-channels-in-a-tensor-of-shape-","dir":"Reference","previous_headings":"","what":"Divide the channels in a tensor of shape","title":"Channel_shuffle — torch_channel_shuffle","text":"math:(*, C , H, W) : Divide channels tensor shape \\((*, C , H, W)\\) g groups rearrange \\((*, C \\frac g, g, H, W)\\), keeping original tensor shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Channel_shuffle — torch_channel_shuffle","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(c(1, 4, 2, 2)) print(input) output <- torch_channel_shuffle(input, 2) print(output) } #> torch_tensor #> (1,1,.,.) =  #>  -1.7996 -0.4875 #>  -0.1402  0.6430 #>  #> (1,2,.,.) =  #>   1.6013 -2.8596 #>   0.6372  0.1704 #>  #> (1,3,.,.) =  #>  -0.8334  1.1602 #>   0.2836  0.8798 #>  #> (1,4,.,.) =  #>   0.5182 -1.2047 #>   0.9831  0.0413 #> [ CPUFloatType{1,4,2,2} ] #> torch_tensor #> (1,1,.,.) =  #>  -1.7996 -0.4875 #>  -0.1402  0.6430 #>  #> (1,2,.,.) =  #>  -0.8334  1.1602 #>   0.2836  0.8798 #>  #> (1,3,.,.) =  #>   1.6013 -2.8596 #>   0.6372  0.1704 #>  #> (1,4,.,.) =  #>   0.5182 -1.2047 #>   0.9831  0.0413 #> [ CPUFloatType{1,4,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky — torch_cholesky","title":"Cholesky — torch_cholesky","text":"Cholesky","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky — torch_cholesky","text":"","code":"torch_cholesky(self, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky — torch_cholesky","text":"self (Tensor) input tensor \\(\\) size \\((*, n, n)\\) * zero batch dimensions consisting symmetric positive-definite matrices. upper (bool, optional) flag indicates whether return upper lower triangular matrix. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"cholesky-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky(input, upper=False, out=NULL) -> Tensor","title":"Cholesky — torch_cholesky","text":"Computes Cholesky decomposition symmetric positive-definite matrix \\(\\) batches symmetric positive-definite matrices. upper TRUE, returned matrix U upper-triangular, decomposition form: $$   = U^TU $$ upper FALSE, returned matrix L lower-triangular, decomposition form: $$     = LL^T $$ upper TRUE, \\(\\) batch symmetric positive-definite matrices, returned tensor composed upper-triangular Cholesky factors individual matrices. Similarly, upper FALSE, returned tensor composed lower-triangular Cholesky factors individual matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky — torch_cholesky","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) # make symmetric positive-definite l = torch_cholesky(a) a l torch_mm(l, l$t()) a = torch_randn(c(3, 2, 2)) if (FALSE) { a = torch_matmul(a, a$transpose(-1, -2)) + 1e-03 # make symmetric positive-definite l = torch_cholesky(a) z = torch_matmul(l, l$transpose(-1, -2)) torch_max(torch_abs(z - a)) # Max non-zero } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky_inverse — torch_cholesky_inverse","title":"Cholesky_inverse — torch_cholesky_inverse","text":"Cholesky_inverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky_inverse — torch_cholesky_inverse","text":"","code":"torch_cholesky_inverse(self, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky_inverse — torch_cholesky_inverse","text":"self (Tensor) input 2-D tensor \\(u\\), upper lower triangular           Cholesky factor upper (bool, optional) whether return lower (default) upper triangular matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"cholesky-inverse-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky_inverse(input, upper=False, out=NULL) -> Tensor","title":"Cholesky_inverse — torch_cholesky_inverse","text":"Computes inverse symmetric positive-definite matrix \\(\\) using Cholesky factor \\(u\\): returns matrix inv. inverse computed using LAPACK routines dpotri spotri (corresponding MAGMA routines). upper FALSE, \\(u\\) lower triangular returned tensor $$     inv = (uu^{{T}})^{{-1}} $$ upper TRUE provided, \\(u\\) upper triangular returned tensor $$     inv = (u^T u)^{{-1}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky_inverse — torch_cholesky_inverse","text":"","code":"if (torch_is_installed()) {  if (FALSE) { a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) + 1e-05 * torch_eye(3) # make symmetric positive definite u = torch_cholesky(a) a torch_cholesky_inverse(u) a$inverse() } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky_solve — torch_cholesky_solve","title":"Cholesky_solve — torch_cholesky_solve","text":"Cholesky_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky_solve — torch_cholesky_solve","text":"","code":"torch_cholesky_solve(self, input2, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky_solve — torch_cholesky_solve","text":"self (Tensor) input matrix \\(b\\) size \\((*, m, k)\\),                \\(*\\) zero batch dimensions input2 (Tensor) input matrix \\(u\\) size \\((*, m, m)\\),                \\(*\\) zero batch dimensions composed                upper lower triangular Cholesky factor upper (bool, optional) whether consider Cholesky factor                            lower upper triangular matrix. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"cholesky-solve-input-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor","title":"Cholesky_solve — torch_cholesky_solve","text":"Solves linear system equations positive semidefinite matrix inverted given Cholesky factor matrix \\(u\\). upper FALSE, \\(u\\) lower triangular c returned : $$     c = (u u^T)^{{-1}} b $$ upper TRUE provided, \\(u\\) upper triangular c returned : $$     c = (u^T u)^{{-1}} b $$ torch_cholesky_solve(b, u) can take 2D inputs b, u inputs batches 2D matrices. inputs batches, returns batched outputs c","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky_solve — torch_cholesky_solve","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) # make symmetric positive definite u = torch_cholesky(a) a b = torch_randn(c(3, 2)) b torch_cholesky_solve(b, u) torch_mm(a$inverse(), b) } #> torch_tensor #>  5.7628  2.1661 #>  1.8217  0.5086 #> -6.3133 -1.1261 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":null,"dir":"Reference","previous_headings":"","what":"Chunk — torch_chunk","title":"Chunk — torch_chunk","text":"Chunk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chunk — torch_chunk","text":"","code":"torch_chunk(self, chunks, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chunk — torch_chunk","text":"self (Tensor) tensor split chunks (int) number chunks return dim (int) dimension along split tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"chunk-input-chunks-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"chunk(input, chunks, dim=0) -> List of Tensors","title":"Chunk — torch_chunk","text":"Splits tensor specific number chunks. chunk view input tensor. Last chunk smaller tensor size along given dimension dim divisible chunks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":null,"dir":"Reference","previous_headings":"","what":"Clamp — torch_clamp","title":"Clamp — torch_clamp","text":"Clamp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clamp — torch_clamp","text":"","code":"torch_clamp(self, min = NULL, max = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clamp — torch_clamp","text":"self (Tensor) input tensor. min (Number) lower-bound range clamped max (Number) upper-bound range clamped ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-min-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, min, max, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamp elements input range [ min, max ] return resulting tensor: $$     y_i = \\left\\{ \\begin{array}{ll}         \\mbox{min} & \\mbox{} x_i < \\mbox{min} \\\\         x_i & \\mbox{} \\mbox{min} \\leq x_i \\leq \\mbox{max} \\\\         \\mbox{max} & \\mbox{} x_i > \\mbox{max}     \\end{array}     \\right. $$ input type FloatTensor DoubleTensor, args min max must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-min-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, *, min, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamps elements input larger equal min. input type FloatTensor DoubleTensor, value real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, *, max, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamps elements input smaller equal max. input type FloatTensor DoubleTensor, value real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clamp — torch_clamp","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_clamp(a, min=-0.5, max=0.5)   a = torch_randn(c(4)) a torch_clamp(a, min=0.5)   a = torch_randn(c(4)) a torch_clamp(a, max=0.5) } #> torch_tensor #> -0.3931 #>  0.5000 #> -0.3415 #> -0.5366 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip — torch_clip","title":"Clip — torch_clip","text":"Clip","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip — torch_clip","text":"","code":"torch_clip(self, min = NULL, max = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip — torch_clip","text":"self (Tensor) input tensor. min (Number) lower-bound range clamped max (Number) upper-bound range clamped ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"clip-input-min-max-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"clip(input, min, max, *, out=None) -> Tensor","title":"Clip — torch_clip","text":"Alias torch_clamp().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone — torch_clone","title":"Clone — torch_clone","text":"Clone","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone — torch_clone","text":"","code":"torch_clone(self, memory_format = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone — torch_clone","text":"self (Tensor) input tensor. memory_format torch memory format. see torch_preserve_format().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Clone — torch_clone","text":"function differentiable, gradients flow back result operation input. create tensor without autograd relationship input see Tensor$detach.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"clone-input-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"clone(input, *, memory_format=torch.preserve_format) -> Tensor","title":"Clone — torch_clone","text":"Returns copy input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Combinations — torch_combinations","title":"Combinations — torch_combinations","text":"Combinations","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combinations — torch_combinations","text":"","code":"torch_combinations(self, r = 2L, with_replacement = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combinations — torch_combinations","text":"self (Tensor) 1D vector. r (int, optional) number elements combine with_replacement (boolean, optional) whether allow duplication combination","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"combinations-input-r-with-replacement-false-gt-seq-","dir":"Reference","previous_headings":"","what":"combinations(input, r=2, with_replacement=False) -> seq","title":"Combinations — torch_combinations","text":"Compute combinations length \\(r\\) given tensor. behavior similar python's itertools.combinations with_replacement set False, itertools.combinations_with_replacement with_replacement set TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combinations — torch_combinations","text":"","code":"if (torch_is_installed()) {  a = c(1, 2, 3) tensor_a = torch_tensor(a) torch_combinations(tensor_a) torch_combinations(tensor_a, r=3) torch_combinations(tensor_a, with_replacement=TRUE) } #> torch_tensor #>  1  1 #>  1  2 #>  1  3 #>  2  2 #>  2  3 #>  3  3 #> [ CPUFloatType{6,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"Complex — torch_complex","title":"Complex — torch_complex","text":"Complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Complex — torch_complex","text":"","code":"torch_complex(real, imag)"},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Complex — torch_complex","text":"real (Tensor) real part complex tensor. Must float double. imag (Tensor) imaginary part complex tensor. Must dtype real.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"complex-real-imag-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"complex(real, imag, *, out=None) -> Tensor","title":"Complex — torch_complex","text":"Constructs complex tensor real part equal real imaginary part equal imag.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Complex — torch_complex","text":"","code":"if (torch_is_installed()) {  real <- torch_tensor(c(1, 2), dtype=torch_float32()) imag <- torch_tensor(c(3, 4), dtype=torch_float32()) z <- torch_complex(real, imag) z z$dtype } #> torch_ComplexFloat"},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Conj — torch_conj","title":"Conj — torch_conj","text":"Conj","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conj — torch_conj","text":"","code":"torch_conj(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conj — torch_conj","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"conj-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"conj(input) -> Tensor","title":"Conj — torch_conj","text":"Computes element-wise conjugate given input tensor. $$     \\mbox{}_{} = conj(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conj — torch_conj","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1d — torch_conv1d","title":"Conv1d — torch_conv1d","text":"Conv1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1d — torch_conv1d","text":"","code":"torch_conv1d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1d — torch_conv1d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can      single number one-element tuple (padW,). Default: 0 dilation spacing kernel elements. Can single number      one-element tuple (dW,). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv1d — torch_conv1d","text":"Applies 1D convolution input signal composed several input planes. See nn_conv1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv1d — torch_conv1d","text":"","code":"if (torch_is_installed()) {  filters = torch_randn(c(33, 16, 3)) inputs = torch_randn(c(20, 16, 50)) nnf_conv1d(inputs, filters) } #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 6 -8.3169e+00 -2.1014e+00 -5.1744e+00  4.7219e+00 -7.2245e+00 -3.7793e+00 #>  -2.3550e+00  3.5967e-01 -5.9571e+00  1.9716e+00 -2.8001e+00  9.2921e+00 #>  -2.9458e+00  7.0693e+00 -2.3263e+00 -4.4576e+00 -3.0706e+00 -3.6137e+00 #>   7.6318e+00  7.6674e+00  6.7905e-01 -1.0191e+00  2.8939e+00  3.7213e+00 #>   3.5952e+00 -1.5817e+01  9.1722e+00  8.3184e-01  1.4853e+00 -1.3252e+00 #>   4.0351e+00  4.5588e-01  4.6643e+00 -5.1794e+00  2.4778e+00 -3.6634e+00 #>  -7.6635e+00  2.3136e+00  1.3166e+00  1.3124e+00 -3.7686e+00 -1.1524e+01 #>  -4.7977e+00  3.8666e+00  7.2739e+00 -1.3060e+01 -1.6958e+00  9.6115e+00 #>  -1.3698e+01 -6.0547e+00  1.0270e+01  1.7839e+01 -4.4191e+00 -5.6308e+00 #>   1.7233e+00 -2.1186e+00 -1.1272e+00 -4.6070e+00 -1.3363e+00  1.4993e+01 #>   4.1939e+00 -1.2392e+01  3.6945e+00  7.5041e+00  7.6516e+00 -7.6661e+00 #>   4.6416e+00 -8.4228e+00 -2.2767e+00  1.5158e+00  6.5058e+00 -7.3223e-01 #>   1.9778e+00 -2.0790e+00  1.6602e+00  9.3931e+00  2.3396e+00  1.5080e+01 #>   5.6369e+00 -4.9868e+00  8.2276e+00 -1.1036e+01  3.5321e+00  2.3121e+00 #>   1.8445e+00 -9.2048e-01 -4.2860e+00 -5.7858e+00 -2.1423e+00  1.3416e+01 #>  -8.5335e-01 -9.2716e+00 -4.5451e+00 -5.0947e+00  3.1179e-01  5.0687e-01 #>   2.1218e+00  3.3808e+00  6.7575e-02  1.9638e+00 -6.4383e-02  4.6061e+00 #>  -9.4345e+00 -1.2909e+01  4.5463e+00  5.7057e+00  7.9532e+00  1.0342e+00 #>   2.0564e+01  3.4541e+00  5.7038e-01 -1.0550e+01  6.2890e+00  2.9685e+00 #>   8.4836e+00  1.0381e+01 -4.9445e+00 -6.4267e-01 -8.5755e+00  3.4221e+00 #>   7.6088e-01  8.3236e+00 -9.5267e+00  4.5688e-01 -2.2607e+00  7.8716e+00 #>   7.3898e+00 -4.1470e+00  1.9449e+00 -1.8083e+00  1.9612e-01  1.6873e+00 #>  -4.0546e+00  3.4497e+00 -3.6739e+00  3.2975e+00 -4.9923e+00  6.4785e+00 #>   1.0131e+01  1.1753e+00 -9.1447e+00 -6.6979e+00  2.5119e+00  3.9920e+00 #>   2.9422e+00  1.0326e+01 -1.5198e+00 -1.6618e+00 -1.2997e+01 -1.6538e+01 #>  -4.5410e+00  1.7281e+00  2.8887e+00  7.1734e+00 -1.5699e-02  1.3118e+00 #>  -1.5259e+00 -5.7793e+00  1.5204e+00 -4.8937e-04 -1.0205e+00 -4.2960e+00 #>   2.3303e+00  6.4505e+00 -5.2870e+00  6.7430e-01 -1.5892e+00  1.6843e+01 #>   4.7452e+00 -3.9635e+00 -6.0927e+00  4.8368e+00 -1.0148e+00 -1.2588e+01 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{20,33,48} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2d — torch_conv2d","title":"Conv2d — torch_conv2d","text":"Conv2d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2d — torch_conv2d","text":"","code":"torch_conv2d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2d — torch_conv2d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kH , kW)\\) bias optional bias tensor shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can      single number tuple (padH, padW). Default: 0 dilation spacing kernel elements. Can single number      tuple (dH, dW). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv2d — torch_conv2d","text":"Applies 2D convolution input image composed several input planes. See nn_conv2d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv2d — torch_conv2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride filters = torch_randn(c(8,4,3,3)) inputs = torch_randn(c(1,4,5,5)) nnf_conv2d(inputs, filters, padding=1) } #> torch_tensor #> (1,1,.,.) =  #>  -6.2293  3.6368 -6.9950 -3.2092  4.0164 #>   4.4499  5.6002  5.9233  0.9858  4.2309 #>  -5.9799 -12.2515 -1.1408  5.2784  4.7481 #>   3.5944  0.3573  1.3755 -0.4545 -0.4302 #>   4.4743  3.5464  2.9807  3.5504  3.6378 #>  #> (1,2,.,.) =  #>  -14.1458  -2.4284  -1.6412  -4.7466  -0.5833 #>   -2.7556  -2.9063 -13.3437  -1.0216   0.0556 #>    8.1979  -9.5774  -2.3079  -9.8105  -6.1516 #>   -3.3297   1.0058   3.2910  -4.9661 -10.2056 #>   -2.9389   7.1504  -4.6618   3.7112   0.9689 #>  #> (1,3,.,.) =  #>   2.0213  4.8348 -0.0753 -5.5559  2.1641 #>   7.3397  5.3186 -0.5203 -10.4046 -2.4127 #>  -2.8926 -5.7183  7.3088  1.4437 -0.2859 #>  -0.1150  1.8809 -4.1334  4.5544  1.0229 #>  -4.4993  0.7790 -2.1356  1.6472 -3.0782 #>  #> (1,4,.,.) =  #>   -8.0488   0.3474   9.3235  -1.6900  -3.2958 #>   -8.0323  -9.7287  -6.8925   4.5854  10.4326 #>   10.2315 -12.7851  -6.9631 -11.5428  -1.7736 #>    1.9657   0.0843  -5.3749  -8.2050  -2.8523 #>   -1.6009   3.4971   2.5930   0.7100   5.0198 #>  #> (1,5,.,.) =  #>   -3.1366   2.1507  -1.2642   1.5051  -3.3853 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{1,8,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3d — torch_conv3d","title":"Conv3d — torch_conv3d","text":"Conv3d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3d — torch_conv3d","text":"","code":"torch_conv3d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3d — torch_conv3d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kT , kH , kW)\\) bias optional bias tensor shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can      single number tuple (padT, padH, padW). Default: 0 dilation spacing kernel elements. Can single number      tuple (dT, dH, dW). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv3d — torch_conv3d","text":"Applies 3D convolution input image composed several input planes. See nn_conv3d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv3d — torch_conv3d","text":"","code":"if (torch_is_installed()) {  # filters = torch_randn(c(33, 16, 3, 3, 3)) # inputs = torch_randn(c(20, 16, 50, 10, 20)) # nnf_conv3d(inputs, filters) } #> NULL"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_tbc — torch_conv_tbc","title":"Conv_tbc — torch_conv_tbc","text":"Conv_tbc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_tbc — torch_conv_tbc","text":"","code":"torch_conv_tbc(self, weight, bias, pad = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_tbc — torch_conv_tbc","text":"self NA input tensor shape \\((\\mbox{sequence length} \\times batch \\times \\mbox{\\_channels})\\) weight NA filter shape (\\(\\mbox{kernel width} \\times \\mbox{\\_channels} \\times \\mbox{\\_channels}\\)) bias NA bias shape (\\(\\mbox{\\_channels}\\)) pad NA number timesteps pad. Default: 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Conv_tbc — torch_conv_tbc","text":"Applies 1-dimensional sequence convolution input sequence. Input output dimensions (Time, Batch, Channels) - hence TBC.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose1d — torch_conv_transpose1d","title":"Conv_transpose1d — torch_conv_transpose1d","text":"Conv_transpose1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose1d — torch_conv_transpose1d","text":"","code":"torch_conv_transpose1d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose1d — torch_conv_transpose1d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sW,). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padW,). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple (out_padW). Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dW,). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose1d — torch_conv_transpose1d","text":"Applies 1D transposed convolution operator input signal composed several input planes, sometimes also called \"deconvolution\". See nn_conv_transpose1d()  details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose1d — torch_conv_transpose1d","text":"","code":"if (torch_is_installed()) {  inputs = torch_randn(c(20, 16, 50)) weights = torch_randn(c(16, 33, 5)) nnf_conv_transpose1d(inputs, weights) } #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 8  -4.1502   5.2662   1.2746  -3.9347   2.2134   7.8490  -1.4314   5.7363 #>   -5.8787  -3.6590  -5.1717  -6.2001  12.6397  13.7569   6.9527   0.8596 #>   -1.2104   0.7274   0.4443  -5.9108   7.5095  -6.3658  -6.4068  13.9326 #>   -5.9152  -6.9704   7.5327   3.7642  -1.7677  -5.2399  15.3008  -8.7244 #>    0.4521  -6.3098  -9.1746   6.6661  14.1740  15.3086  -8.1402   0.9600 #>    2.2716   0.8837  -0.1830  -4.7564  14.5371  14.8455  -4.3501  -9.0601 #>   -1.5410  11.1980   9.0446  -3.0310  -1.8131  15.7361 -12.3093  16.3265 #>   -3.2634   8.4169 -10.9854  -3.6416   5.5400   3.7850   5.8189  -1.2514 #>    1.1178  11.4405  -1.1431  -8.9672  -6.1387  14.0108  -1.0153   5.5063 #>    2.8625   4.9656   6.2678  -7.2279  -0.8833   5.9117   9.8635   0.9836 #>    2.9008   9.2096  -5.7410   4.0162   1.7477   0.0787  -0.9875  14.4767 #>    7.4777  -2.6968   7.8789  10.3786  -0.9959  15.2169  12.7871   1.1561 #>   -6.8383   8.0303   3.1514  11.9488  -1.3620  -9.9862   0.0746  -4.0376 #>   -2.2166   1.9659  -5.3781 -14.7103  -3.3162   4.7723   2.4020  -8.3331 #>    7.2071   7.2636  12.6062  -0.7760   9.4554  -7.9184 -11.9570   5.5830 #>    2.1695   6.4469   7.0870  -0.3204   2.3094  -6.7242  -1.6000   3.1596 #>    3.3520   4.1517  -2.1789   4.5343   2.5825   2.1492   1.1830  -4.4321 #>    0.0922  -1.3383  -4.0789   7.5349   0.3219   5.2433  -2.6360   3.7869 #>    4.7933   3.3512  -4.5051  -2.1901  22.3008  18.8490   7.5580  -5.6750 #>    7.3938 -11.1678  -2.0972  -5.7141 -11.4733  -7.5001   3.0933  -9.5548 #>   -9.2641 -12.2349   1.5176  -5.4936   4.9133  -1.5024 -10.7774 -11.2684 #>    3.2107   1.9521  11.6881  10.0457  12.0771   5.5904   2.8542 -15.3766 #>    3.3805  -0.6111  -3.0381   4.6090  -6.2199   7.1722  -3.4843  -3.2336 #>   -3.0108   6.0233   4.3482 -10.2952  -0.5250   1.0059   6.7320  12.2936 #>    6.2232   0.8173   2.2507   4.4085   6.0984 -12.9619  -7.8572   1.1984 #>    6.3426 -11.2770   4.4595  -5.8399  -1.1147  -5.2786 -11.4394  -7.1858 #>    4.6621  -2.3163   4.1256  -9.2621   0.6303   0.6374  11.0537   8.0226 #>   -4.4188   2.5024   7.9690 -10.1976  -2.7284  -1.7297 -14.5485  -1.5143 #>   -3.2679  -4.1840   6.4311   6.7153  11.8356   8.8752   0.9989   1.3909 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{20,33,54} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose2d — torch_conv_transpose2d","title":"Conv_transpose2d — torch_conv_transpose2d","text":"Conv_transpose2d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose2d — torch_conv_transpose2d","text":"","code":"torch_conv_transpose2d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose2d — torch_conv_transpose2d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kH , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sH, sW). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padH, padW). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple (out_padH, out_padW).      Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose2d — torch_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\". See nn_conv_transpose2d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose2d — torch_conv_transpose2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride inputs = torch_randn(c(1, 4, 5, 5)) weights = torch_randn(c(4, 8, 3, 3)) nnf_conv_transpose2d(inputs, weights, padding=1) } #> torch_tensor #> (1,1,.,.) =  #>  -0.9863 -2.1015 -0.7686  4.6569  4.3497 #>  -4.9922  3.4184  0.0460 -0.6890  7.4952 #>   5.5314 -1.1805 -0.0253  5.8615 -2.7114 #>   2.1113  3.0726  7.1237 -2.0755 -3.5216 #>   4.6665 -5.3079 -2.6928 -0.7921  1.6514 #>  #> (1,2,.,.) =  #>  -5.4506 -14.0902  0.0557  9.6182  1.5526 #>   0.7919  0.8676  4.5834  4.1394 -0.5979 #>  -2.8559  2.2970  4.3644 -14.7250 -1.2473 #>  -5.2405 -2.2707 -2.3420  0.0843  3.2389 #>   1.5088 -0.2471  2.9301  6.5253  4.5815 #>  #> (1,3,.,.) =  #>   -8.1272  -3.7910   1.7024  10.5755   1.1526 #>    5.3352   0.1813   7.7843   1.7031   2.8132 #>    5.1791  -4.5619   4.4660  -2.8693   7.4604 #>    2.2076   1.4190  -2.2263  -2.5487  -3.6389 #>    2.8728  -2.9609   4.4764  -3.8207   4.7798 #>  #> (1,4,.,.) =  #>    7.5384   5.1471   3.5030  -6.6308   2.5658 #>   -3.0810  -7.5815  -5.2492   8.1604  -2.4829 #>    4.6761  -3.1848   3.4074   0.4728  -0.2657 #>   -5.8688  13.5493   3.0594 -13.3597   0.1405 #>   -2.6346  -4.1050   1.9900   6.8615   1.5911 #>  #> (1,5,.,.) =  #>  -1.4595 -6.9167 -9.4588  4.4856  3.8772 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{1,8,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose3d — torch_conv_transpose3d","title":"Conv_transpose3d — torch_conv_transpose3d","text":"Conv_transpose3d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose3d — torch_conv_transpose3d","text":"","code":"torch_conv_transpose3d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose3d — torch_conv_transpose3d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kT , kH , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sT, sH, sW). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padT, padH, padW). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple      (out_padT, out_padH, out_padW). Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dT, dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose3d — torch_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\" See nn_conv_transpose3d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose3d — torch_conv_transpose3d","text":"","code":"if (torch_is_installed()) { if (FALSE) { inputs = torch_randn(c(20, 16, 50, 10, 20)) weights = torch_randn(c(16, 33, 3, 3, 3)) nnf_conv_transpose3d(inputs, weights) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cos — torch_cos","title":"Cos — torch_cos","text":"Cos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cos — torch_cos","text":"","code":"torch_cos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cos — torch_cos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"cos-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cos(input, out=NULL) -> Tensor","title":"Cos — torch_cos","text":"Returns new tensor cosine  elements input. $$     \\mbox{}_{} = \\cos(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cos — torch_cos","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_cos(a) } #> torch_tensor #>  0.8458 #>  0.5691 #>  0.9922 #>  0.6951 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosh — torch_cosh","title":"Cosh — torch_cosh","text":"Cosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosh — torch_cosh","text":"","code":"torch_cosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosh — torch_cosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"cosh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cosh(input, out=NULL) -> Tensor","title":"Cosh — torch_cosh","text":"Returns new tensor hyperbolic cosine  elements input. $$     \\mbox{}_{} = \\cosh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cosh — torch_cosh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_cosh(a) } #> torch_tensor #>  1.1358 #>  2.7650 #>  1.0465 #>  2.4691 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_similarity — torch_cosine_similarity","title":"Cosine_similarity — torch_cosine_similarity","text":"Cosine_similarity","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_similarity — torch_cosine_similarity","text":"","code":"torch_cosine_similarity(x1, x2, dim = 2L, eps = 1e-08)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_similarity — torch_cosine_similarity","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). dim (int, optional) Dimension vectors. Default: 1 eps (float, optional) Small value avoid division zero.        Default: 1e-8","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"cosine-similarity-x-x-dim-eps-e-gt-tensor-","dir":"Reference","previous_headings":"","what":"cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor","title":"Cosine_similarity — torch_cosine_similarity","text":"Returns cosine similarity x1 x2, computed along dim. $$     \\mbox{similarity} = \\frac{x_1 \\cdot x_2}{\\max(\\Vert x_1 \\Vert _2 \\cdot \\Vert x_2 \\Vert _2, \\epsilon)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cosine_similarity — torch_cosine_similarity","text":"","code":"if (torch_is_installed()) {  input1 = torch_randn(c(100, 128)) input2 = torch_randn(c(100, 128)) output = torch_cosine_similarity(input1, input2) output } #> torch_tensor #>  0.0305 #> -0.0182 #>  0.0420 #>  0.1624 #> -0.0298 #>  0.1531 #> -0.0605 #>  0.0000 #> -0.0160 #> -0.0241 #>  0.0530 #> -0.0665 #> -0.0217 #> -0.0156 #> -0.1495 #> -0.0518 #>  0.0479 #> -0.0701 #>  0.0126 #> -0.0778 #> -0.0188 #>  0.0288 #> -0.0369 #> -0.0133 #> -0.0499 #> -0.0711 #>  0.0695 #>  0.0328 #> -0.1419 #>  0.0098 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{100} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Count_nonzero — torch_count_nonzero","title":"Count_nonzero — torch_count_nonzero","text":"Count_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count_nonzero — torch_count_nonzero","text":"","code":"torch_count_nonzero(self, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count_nonzero — torch_count_nonzero","text":"self (Tensor) input tensor. dim (int tuple ints, optional) Dim tuple dims along count non-zeros.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"count-nonzero-input-dim-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"count_nonzero(input, dim=None) -> Tensor","title":"Count_nonzero — torch_count_nonzero","text":"Counts number non-zero values tensor input along given dim. dim specified non-zeros tensor counted.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count_nonzero — torch_count_nonzero","text":"","code":"if (torch_is_installed()) {  x <- torch_zeros(3,3) x[torch_randn(3,3) > 0.5] = 1 x torch_count_nonzero(x) torch_count_nonzero(x, dim=1) } #> torch_tensor #>  2 #>  1 #>  1 #> [ CPULongType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross — torch_cross","title":"Cross — torch_cross","text":"Cross","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross — torch_cross","text":"","code":"torch_cross(self, other, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross — torch_cross","text":"self (Tensor) input tensor. (Tensor) second input tensor dim (int, optional) dimension take cross-product .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"cross-input-other-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cross(input, other, dim=-1, out=NULL) -> Tensor","title":"Cross — torch_cross","text":"Returns cross product vectors dimension dim input . input must size, size dim dimension 3. dim given, defaults first dimension found size 3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross — torch_cross","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 3)) a b = torch_randn(c(4, 3)) b torch_cross(a, b, dim=2) torch_cross(a, b) } #> torch_tensor #> -0.0327  0.1124 -0.1158 #> -0.0779  0.3301 -0.3027 #> -1.3833 -0.0304 -0.8002 #> -0.6250  0.9997  0.1767 #> [ CPUFloatType{4,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":null,"dir":"Reference","previous_headings":"","what":"Cummax — torch_cummax","title":"Cummax — torch_cummax","text":"Cummax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cummax — torch_cummax","text":"","code":"torch_cummax(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cummax — torch_cummax","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"cummax-input-dim-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"cummax(input, dim) -> (Tensor, LongTensor)","title":"Cummax — torch_cummax","text":"Returns namedtuple (values, indices) values cumulative maximum elements input dimension dim. indices index location maximum value found dimension dim. $$     y_i = max(x_1, x_2, x_3, \\dots, x_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cummax — torch_cummax","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cummax(a, dim=1) } #> [[1]] #> torch_tensor #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #>  1.2996 #> [ CPUFloatType{10} ] #>  #> [[2]] #> torch_tensor #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #> [ CPULongType{10} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":null,"dir":"Reference","previous_headings":"","what":"Cummin — torch_cummin","title":"Cummin — torch_cummin","text":"Cummin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cummin — torch_cummin","text":"","code":"torch_cummin(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cummin — torch_cummin","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"cummin-input-dim-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"cummin(input, dim) -> (Tensor, LongTensor)","title":"Cummin — torch_cummin","text":"Returns namedtuple (values, indices) values cumulative minimum elements input dimension dim. indices index location maximum value found dimension dim. $$     y_i = min(x_1, x_2, x_3, \\dots, x_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cummin — torch_cummin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cummin(a, dim=1) } #> [[1]] #> torch_tensor #> 0.01 * #>  5.5115 #>  5.5115 #>  5.5115 #>  5.5115 #>  5.5115 #>  5.5115 #> -120.6017 #> -120.6017 #> -120.6017 #> -120.6017 #> [ CPUFloatType{10} ] #>  #> [[2]] #> torch_tensor #>  0 #>  0 #>  0 #>  0 #>  0 #>  0 #>  6 #>  6 #>  6 #>  6 #> [ CPULongType{10} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumprod — torch_cumprod","title":"Cumprod — torch_cumprod","text":"Cumprod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumprod — torch_cumprod","text":"","code":"torch_cumprod(self, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumprod — torch_cumprod","text":"self (Tensor) input tensor. dim (int) dimension operation dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"cumprod-input-dim-out-null-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor","title":"Cumprod — torch_cumprod","text":"Returns cumulative product elements input dimension dim. example, input vector size N, result also vector size N, elements. $$     y_i = x_1 \\times x_2\\times x_3\\times \\dots \\times x_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumprod — torch_cumprod","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cumprod(a, dim=1) } #> torch_tensor #> -0.7135 #> -0.2467 #> -0.4431 #>  0.2452 #>  0.2581 #> -0.3817 #>  0.2863 #> -0.4371 #>  0.5322 #> -0.1767 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumsum — torch_cumsum","title":"Cumsum — torch_cumsum","text":"Cumsum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumsum — torch_cumsum","text":"","code":"torch_cumsum(self, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumsum — torch_cumsum","text":"self (Tensor) input tensor. dim (int) dimension operation dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"cumsum-input-dim-out-null-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor","title":"Cumsum — torch_cumsum","text":"Returns cumulative sum elements input dimension dim. example, input vector size N, result also vector size N, elements. $$     y_i = x_1 + x_2 + x_3 + \\dots + x_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumsum — torch_cumsum","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cumsum(a, dim=1) } #> torch_tensor #>  0.7570 #>  0.2946 #> -0.2459 #> -0.1427 #> -0.1599 #>  0.7696 #>  1.3670 #>  1.9541 #>  2.5709 #>  2.4159 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":null,"dir":"Reference","previous_headings":"","what":"Deg2rad — torch_deg2rad","title":"Deg2rad — torch_deg2rad","text":"Deg2rad","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deg2rad — torch_deg2rad","text":"","code":"torch_deg2rad(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deg2rad — torch_deg2rad","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"deg-rad-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"deg2rad(input, *, out=None) -> Tensor","title":"Deg2rad — torch_deg2rad","text":"Returns new tensor elements input converted angles degrees radians.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deg2rad — torch_deg2rad","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(rbind(c(180.0, -180.0), c(360.0, -360.0), c(90.0, -90.0))) torch_deg2rad(a) } #> torch_tensor #>  3.1416 -3.1416 #>  6.2832 -6.2832 #>  1.5708 -1.5708 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":null,"dir":"Reference","previous_headings":"","what":"Dequantize — torch_dequantize","title":"Dequantize — torch_dequantize","text":"Dequantize","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dequantize — torch_dequantize","text":"","code":"torch_dequantize(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dequantize — torch_dequantize","text":"tensor (Tensor) quantized Tensor list oof quantized tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"dequantize-tensor-gt-tensor-","dir":"Reference","previous_headings":"","what":"dequantize(tensor) -> Tensor","title":"Dequantize — torch_dequantize","text":"Returns fp32 Tensor dequantizing quantized Tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"dequantize-tensors-gt-sequence-of-tensors-","dir":"Reference","previous_headings":"","what":"dequantize(tensors) -> sequence of Tensors","title":"Dequantize — torch_dequantize","text":"Given list quantized Tensors, dequantize return list fp32 Tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Det — torch_det","title":"Det — torch_det","text":"Det","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Det — torch_det","text":"","code":"torch_det(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Det — torch_det","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Det — torch_det","text":"","code":"Backward through `det` internally uses SVD results when `input` is not invertible. In this case, double backward through `det` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"det-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"det(input) -> Tensor","title":"Det — torch_det","text":"Calculates determinant square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Det — torch_det","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) torch_det(A) A = torch_randn(c(3, 2, 2)) A A$det() } #> torch_tensor #> -1.5133 #>  3.3801 #>  1.1298 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Device object — torch_device","title":"Create a Device object — torch_device","text":"torch_device  object representing device torch_tensor allocated.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Device object — torch_device","text":"","code":"torch_device(type, index = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Device object — torch_device","text":"type (character) device type \"cuda\" \"cpu\" index (integer) optional device ordinal device type.  device ordinal present, object always represent current device device type, even torch_cuda_set_device() called; e.g., torch_tensor constructed device 'cuda' equivalent 'cuda:X' X result torch_cuda_current_device(). torch_device can constructed via string via string device ordinal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Device object — torch_device","text":"","code":"if (torch_is_installed()) {  # Via string torch_device(\"cuda:1\") torch_device(\"cpu\") torch_device(\"cuda\") # current cuda device  # Via string and device ordinal torch_device(\"cuda\", 0) torch_device(\"cpu\", 0) } #> torch_device(type='cpu', index=0)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Diag — torch_diag","title":"Diag — torch_diag","text":"Diag","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diag — torch_diag","text":"","code":"torch_diag(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diag — torch_diag","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"diag-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"diag(input, diagonal=0, out=NULL) -> Tensor","title":"Diag — torch_diag","text":"input vector (1-D tensor), returns 2-D square tensor elements input diagonal. input matrix (2-D tensor), returns 1-D tensor diagonal elements input. argument diagonal controls diagonal consider: diagonal = 0, main diagonal. diagonal > 0, main diagonal. diagonal < 0, main diagonal.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":null,"dir":"Reference","previous_headings":"","what":"Diag_embed — torch_diag_embed","title":"Diag_embed — torch_diag_embed","text":"Diag_embed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diag_embed — torch_diag_embed","text":"","code":"torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diag_embed — torch_diag_embed","text":"self (Tensor) input tensor. Must least 1-dimensional. offset (int, optional) diagonal consider. Default: 0        (main diagonal). dim1 (int, optional) first dimension respect        take diagonal. Default: -2. dim2 (int, optional) second dimension respect        take diagonal. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"diag-embed-input-offset-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor","title":"Diag_embed — torch_diag_embed","text":"Creates tensor whose diagonals certain 2D planes (specified dim1 dim2) filled input. facilitate creating batched diagonal matrices, 2D planes formed last two dimensions returned tensor chosen default. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal. size new matrix calculated make specified diagonal size last input dimension. Note offset \\(0\\), order dim1 dim2 matters. Exchanging equivalent changing sign offset. Applying torch_diagonal output function arguments yields matrix identical input. However, torch_diagonal different default dimensions, need explicitly specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diag_embed — torch_diag_embed","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(2, 3)) torch_diag_embed(a) torch_diag_embed(a, offset=1, dim1=1, dim2=3) } #> torch_tensor #> (1,.,.) =  #>   0.0000  1.4648  0.0000  0.0000 #>   0.0000 -0.1176  0.0000  0.0000 #>  #> (2,.,.) =  #>   0.0000  0.0000 -0.0131  0.0000 #>   0.0000  0.0000 -0.6292  0.0000 #>  #> (3,.,.) =  #>   0.0000  0.0000  0.0000  0.2110 #>   0.0000  0.0000  0.0000 -0.2680 #>  #> (4,.,.) =  #>   0  0  0  0 #>   0  0  0  0 #> [ CPUFloatType{4,2,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagflat — torch_diagflat","title":"Diagflat — torch_diagflat","text":"Diagflat","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagflat — torch_diagflat","text":"","code":"torch_diagflat(self, offset = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagflat — torch_diagflat","text":"self (Tensor) input tensor. offset (int, optional) diagonal consider. Default: 0 (main        diagonal).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"diagflat-input-offset-gt-tensor-","dir":"Reference","previous_headings":"","what":"diagflat(input, offset=0) -> Tensor","title":"Diagflat — torch_diagflat","text":"input vector (1-D tensor), returns 2-D square tensor elements input diagonal. input tensor one dimension, returns 2-D tensor diagonal elements equal flattened input. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagflat — torch_diagflat","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3)) a torch_diagflat(a) torch_diagflat(a, 1) a = torch_randn(c(2, 2)) a torch_diagflat(a) } #> torch_tensor #>  0.1981  0.0000  0.0000  0.0000 #>  0.0000  0.3182  0.0000  0.0000 #>  0.0000  0.0000  0.0107  0.0000 #>  0.0000  0.0000  0.0000 -0.2125 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagonal — torch_diagonal","title":"Diagonal — torch_diagonal","text":"Diagonal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagonal — torch_diagonal","text":"","code":"torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagonal — torch_diagonal","text":"self (Tensor) input tensor. Must least 2-dimensional. outdim dimension name self named tensor. dim1 (int, optional) first dimension respect        take diagonal. Default: 0. dim2 (int, optional) second dimension respect        take diagonal. Default: 1. offset (int, optional) diagonal consider. Default: 0        (main diagonal).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"diagonal-input-offset-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor","title":"Diagonal — torch_diagonal","text":"Returns partial view input diagonal elements respect dim1 dim2 appended dimension end shape. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal. Applying torch_diag_embed output function arguments yields diagonal matrix diagonal entries input. However, torch_diag_embed different default dimensions, need explicitly specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagonal — torch_diagonal","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_diagonal(a, offset = 0) torch_diagonal(a, offset = 1) x = torch_randn(c(2, 5, 4, 2)) torch_diagonal(x, offset=-1, dim1=1, dim2=2) } #> torch_tensor #> (1,.,.) =  #>   0.4361 #>  -0.5322 #>  #> (2,.,.) =  #>  -1.4098 #>   1.1068 #>  #> (3,.,.) =  #>  -1.1374 #>  -0.0536 #>  #> (4,.,.) =  #>   0.3274 #>   0.4074 #> [ CPUFloatType{4,2,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the n-th forward difference along the given dimension. — torch_diff","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"first-order differences given [] = input[+ 1] - input[]. Higher-order differences calculated using torch_diff() recursively.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"","code":"torch_diff(self, n = 1L, dim = -1L, prepend = list(), append = list())"},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"self tensor compute differences n number times recursively compute difference dim dimension compute difference along. Default last dimension. prepend values prepend input along dim computing difference. dimensions must equivalent input, shapes must match input’s shape except dim. append values append input along dim computing difference. dimensions must equivalent input, shapes must match input’s shape except dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"n = 1 currently supported","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(c(1,2,3)) torch_diff(a)  b <- torch_tensor(c(4, 5)) torch_diff(a, append = b)  c <- torch_tensor(rbind(c(1,2,3), c(3,4,5))) torch_diff(c, dim = 1) torch_diff(c, dim = 2)   } #> torch_tensor #>  1  1 #>  1  1 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Digamma — torch_digamma","title":"Digamma — torch_digamma","text":"Digamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Digamma — torch_digamma","text":"","code":"torch_digamma(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Digamma — torch_digamma","text":"self (Tensor) tensor compute digamma function ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"digamma-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"digamma(input, out=NULL) -> Tensor","title":"Digamma — torch_digamma","text":"Computes logarithmic derivative gamma function input. $$     \\psi(x) = \\frac{d}{dx} \\ln\\left(\\Gamma\\left(x\\right)\\right) = \\frac{\\Gamma'(x)}{\\Gamma(x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Digamma — torch_digamma","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(1, 0.5)) torch_digamma(a) } #> torch_tensor #> -0.5772 #> -1.9635 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Dist — torch_dist","title":"Dist — torch_dist","text":"Dist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dist — torch_dist","text":"","code":"torch_dist(self, other, p = 2L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dist — torch_dist","text":"self (Tensor) input tensor. (Tensor) Right-hand-side input tensor p (float, optional) norm computed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"dist-input-other-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"dist(input, other, p=2) -> Tensor","title":"Dist — torch_dist","text":"Returns p-norm (input - ) shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dist — torch_dist","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(4)) x y = torch_randn(c(4)) y torch_dist(x, y, 3.5) torch_dist(x, y, 3) torch_dist(x, y, 0) torch_dist(x, y, 1) } #> torch_tensor #> 3.93348 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Div — torch_div","title":"Div — torch_div","text":"Div","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Div — torch_div","text":"","code":"torch_div(self, other, rounding_mode)"},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Div — torch_div","text":"self (Tensor) input tensor. (Number) number divided element input rounding_mode (str, optional) – Type rounding applied result: NULL - default behavior. Performs rounding , input integer types, promotes inputs default scalar type. Equivalent true division Python (/ operator) NumPy’s np.true_divide. \"trunc\" - rounds results division towards zero. Equivalent C-style integer division. \"floor\" - rounds results division . Equivalent floor division Python (// operator) NumPy’s np.floor_divide.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"div-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"div(input, other, out=NULL) -> Tensor","title":"Div — torch_div","text":"Divides element input input scalar returns new resulting tensor. element tensor input divided element tensor . resulting tensor returned. $$     \\mbox{}_i = \\frac{\\mbox{input}_i}{\\mbox{}_i} $$ shapes input must broadcastable . torch_dtype input differ, torch_dtype result tensor determined following rules described type promotion documentation . specified, result must castable  torch_dtype specified output tensor. Integral division zero leads undefined behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Div — torch_div","text":"Integer division using div deprecated, future release div perform true division like torch_true_divide(). Use torch_floor_divide() perform integer division, instead. $$     \\mbox{}_i = \\frac{\\mbox{input}_i}{\\mbox{}} $$ torch_dtype input differ, torch_dtype result tensor determined following rules described type promotion documentation . specified, result must castable torch_dtype specified output tensor. Integral division zero leads undefined behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Div — torch_div","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_div(a, 0.5)   a = torch_randn(c(4, 4)) a b = torch_randn(c(4)) b torch_div(a, b) } #> torch_tensor #>  2.1205  0.0517  0.4914 -1.1369 #>  0.4111  0.4443  0.3243 -3.1527 #>  2.1085 -1.0652  1.2698 -0.9235 #> -2.1766 -0.8320  0.8040 -4.1717 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide — torch_divide","title":"Divide — torch_divide","text":"Divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide — torch_divide","text":"","code":"torch_divide(self, other, rounding_mode)"},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide — torch_divide","text":"self (Tensor) input tensor. (Number) number divided element input rounding_mode (str, optional) – Type rounding applied result: NULL - default behavior. Performs rounding , input integer types, promotes inputs default scalar type. Equivalent true division Python (/ operator) NumPy’s np.true_divide. \"trunc\" - rounds results division towards zero. Equivalent C-style integer division. \"floor\" - rounds results division . Equivalent floor division Python (// operator) NumPy’s np.floor_divide.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"divide-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"divide(input, other, *, out=None) -> Tensor","title":"Divide — torch_divide","text":"Alias torch_div().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot — torch_dot","title":"Dot — torch_dot","text":"Dot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot — torch_dot","text":"","code":"torch_dot(self, tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot — torch_dot","text":"self input tensor tensor input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot — torch_dot","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"dot-input-tensor-gt-tensor-","dir":"Reference","previous_headings":"","what":"dot(input, tensor) -> Tensor","title":"Dot — torch_dot","text":"Computes dot product (inner product) two tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dot — torch_dot","text":"","code":"if (torch_is_installed()) {  torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1))) } #> torch_tensor #> 7 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Dstack — torch_dstack","title":"Dstack — torch_dstack","text":"Dstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dstack — torch_dstack","text":"","code":"torch_dstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dstack — torch_dstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"dstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"dstack(tensors, *, out=None) -> Tensor","title":"Dstack — torch_dstack","text":"Stack tensors sequence depthwise (along third axis). equivalent concatenation along third axis 1-D 2-D tensors reshaped torch_atleast_3d().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dstack — torch_dstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_dstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_dstack(list(a,b)) } #> torch_tensor #> (1,.,.) =  #>   1  4 #>  #> (2,.,.) =  #>   2  5 #>  #> (3,.,.) =  #>   3  6 #> [ CPUFloatType{3,1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch data types — torch_dtype","title":"Torch data types — torch_dtype","text":"Returns correspondent data type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch data types — torch_dtype","text":"","code":"torch_float32()  torch_float()  torch_float64()  torch_double()  torch_cfloat32()  torch_chalf()  torch_cfloat()  torch_cfloat64()  torch_cdouble()  torch_cfloat128()  torch_float16()  torch_half()  torch_uint8()  torch_int8()  torch_int16()  torch_short()  torch_int32()  torch_int()  torch_int64()  torch_long()  torch_bool()  torch_quint8()  torch_qint8()  torch_qint32()"},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Eig — torch_eig","title":"Eig — torch_eig","text":"Eig","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eig — torch_eig","text":"self (Tensor) square matrix shape \\((n \\times n)\\) eigenvalues eigenvectors        computed eigenvectors (bool) TRUE compute eigenvalues eigenvectors;        otherwise, eigenvalues computed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"eig-input-eigenvectors-false-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor)","title":"Eig — torch_eig","text":"Computes eigenvalues eigenvectors real square matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Einsum — torch_einsum","title":"Einsum — torch_einsum","text":"Einsum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Einsum — torch_einsum","text":"","code":"torch_einsum(equation, tensors, path = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Einsum — torch_einsum","text":"equation (string) equation given terms lower case letters (indices) associated           dimension operands result. left hand side lists operands           dimensions, separated commas. one index letter per tensor dimension.           right hand side follows -> gives indices output.           -> right hand side omitted, implicitly defined alphabetically           sorted list indices appearing exactly left hand side.           indices apprearing output summed multiplying operands           entries.           index appears several times operand, diagonal taken.           Ellipses ... represent fixed number dimensions. right hand side inferred,           ellipsis dimensions beginning output. tensors (Tensor) operands compute Einstein sum . path (int) function uses opt_einsum speed computation consume less memory optimizing contraction order. optimization occurs least three inputs, since order matter otherwise. Note finding optimal path NP-hard problem, thus, opt_einsum relies different heuristics achieve near-optimal results. opt_einsum available, default order contract left right. path argument used changed default, set advanced users.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"einsum-equation-operands-gt-tensor-","dir":"Reference","previous_headings":"","what":"einsum(equation, *operands) -> Tensor","title":"Einsum — torch_einsum","text":"function provides way computing multilinear expressions (.e. sums products) using Einstein summation convention.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Einsum — torch_einsum","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(5)) y = torch_randn(c(4)) torch_einsum('i,j->ij', list(x, y))  # outer product A = torch_randn(c(3,5,4)) l = torch_randn(c(2,5)) r = torch_randn(c(2,4)) torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear As = torch_randn(c(3,2,5)) Bs = torch_randn(c(3,5,4)) torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication A = torch_randn(c(3, 3)) torch_einsum('ii->i', list(A)) # diagonal A = torch_randn(c(4, 3, 3)) torch_einsum('...ii->...i', list(A)) # batch diagonal A = torch_randn(c(2, 3, 4, 5)) torch_einsum('...ij->...ji', list(A))$shape # batch permute  } #> [1] 2 3 5 4"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty — torch_empty","title":"Empty — torch_empty","text":"Empty","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty — torch_empty","text":"","code":"torch_empty(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty — torch_empty","text":"... sequence integers defining shape output tensor. names optional character vector naming dimension. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor","title":"Empty — torch_empty","text":"Returns tensor filled uninitialized data. shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty — torch_empty","text":"","code":"if (torch_is_installed()) {  torch_empty(c(2, 3)) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty_like — torch_empty_like","title":"Empty_like — torch_empty_like","text":"Empty_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty_like — torch_empty_like","text":"","code":"torch_empty_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty_like — torch_empty_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Empty_like — torch_empty_like","text":"Returns uninitialized tensor size input. torch_empty_like(input) equivalent torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty_like — torch_empty_like","text":"","code":"if (torch_is_installed()) {  torch_empty(list(2,3), dtype = torch_int64()) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty_strided — torch_empty_strided","title":"Empty_strided — torch_empty_strided","text":"Empty_strided","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty_strided — torch_empty_strided","text":"","code":"torch_empty_strided(   size,   stride,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   pin_memory = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty_strided — torch_empty_strided","text":"size (tuple ints) shape output tensor stride (tuple ints) strides output tensor dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. pin_memory (bool, optional) set, returned tensor allocated        pinned memory. Works CPU tensors. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor","title":"Empty_strided — torch_empty_strided","text":"Returns tensor filled uninitialized data. shape strides tensor defined variable argument size stride respectively. torch_empty_strided(size, stride) equivalent torch_empty(size).as_strided(size, stride).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Empty_strided — torch_empty_strided","text":"one element created tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty_strided — torch_empty_strided","text":"","code":"if (torch_is_installed()) {  a = torch_empty_strided(list(2, 3), list(1, 2)) a a$stride(1) a$size(1) } #> [1] 2"},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":null,"dir":"Reference","previous_headings":"","what":"Eq — torch_eq","title":"Eq — torch_eq","text":"Eq","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eq — torch_eq","text":"","code":"torch_eq(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eq — torch_eq","text":"self (Tensor) tensor compare (Tensor float) tensor value compare Must ByteTensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"eq-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"eq(input, other, out=NULL) -> Tensor","title":"Eq — torch_eq","text":"Computes element-wise equality second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eq — torch_eq","text":"","code":"if (torch_is_installed()) {  torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4))) } #> torch_tensor #>  1 #>  0 #>  0 #>  1 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal — torch_equal","title":"Equal — torch_equal","text":"Equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal — torch_equal","text":"","code":"torch_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal — torch_equal","text":"self input tensor input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"equal-input-other-gt-bool-","dir":"Reference","previous_headings":"","what":"equal(input, other) -> bool","title":"Equal — torch_equal","text":"TRUE two tensors size elements, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal — torch_equal","text":"","code":"if (torch_is_installed()) {  torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2))) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Erf — torch_erf","title":"Erf — torch_erf","text":"Erf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erf — torch_erf","text":"","code":"torch_erf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erf — torch_erf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"erf-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erf(input, out=NULL) -> Tensor","title":"Erf — torch_erf","text":"Computes error function element. error function defined follows: $$     \\mathrm{erf}(x) = \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x} e^{-t^2} dt $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erf — torch_erf","text":"","code":"if (torch_is_installed()) {  torch_erf(torch_tensor(c(0, -1., 10.))) } #> torch_tensor #>  0.0000 #> -0.8427 #>  1.0000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":null,"dir":"Reference","previous_headings":"","what":"Erfc — torch_erfc","title":"Erfc — torch_erfc","text":"Erfc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erfc — torch_erfc","text":"","code":"torch_erfc(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erfc — torch_erfc","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"erfc-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erfc(input, out=NULL) -> Tensor","title":"Erfc — torch_erfc","text":"Computes complementary error function element input. complementary error function defined follows: $$     \\mathrm{erfc}(x) = 1 - \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x} e^{-t^2} dt $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erfc — torch_erfc","text":"","code":"if (torch_is_installed()) {  torch_erfc(torch_tensor(c(0, -1., 10.))) } #> torch_tensor #>  1.0000e+00 #>  1.8427e+00 #>  2.8026e-45 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Erfinv — torch_erfinv","title":"Erfinv — torch_erfinv","text":"Erfinv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erfinv — torch_erfinv","text":"","code":"torch_erfinv(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erfinv — torch_erfinv","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"erfinv-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erfinv(input, out=NULL) -> Tensor","title":"Erfinv — torch_erfinv","text":"Computes inverse error function element input. inverse error function defined range \\((-1, 1)\\) : $$     \\mathrm{erfinv}(\\mathrm{erf}(x)) = x $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erfinv — torch_erfinv","text":"","code":"if (torch_is_installed()) {  torch_erfinv(torch_tensor(c(0, 0.5, -1.))) } #> torch_tensor #>  0.0000 #>  0.4769 #>    -inf #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Exp — torch_exp","title":"Exp — torch_exp","text":"Exp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exp — torch_exp","text":"","code":"torch_exp(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exp — torch_exp","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"exp-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"exp(input, out=NULL) -> Tensor","title":"Exp — torch_exp","text":"Returns new tensor exponential elements input tensor input. $$     y_{} = e^{x_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exp — torch_exp","text":"","code":"if (torch_is_installed()) {  torch_exp(torch_tensor(c(0, log(2)))) } #> torch_tensor #>  1 #>  2 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":null,"dir":"Reference","previous_headings":"","what":"Exp2 — torch_exp2","title":"Exp2 — torch_exp2","text":"Exp2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exp2 — torch_exp2","text":"","code":"torch_exp2(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exp2 — torch_exp2","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"exp-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"exp2(input, *, out=None) -> Tensor","title":"Exp2 — torch_exp2","text":"Computes base two exponential function input. $$     y_{} = 2^{x_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exp2 — torch_exp2","text":"","code":"if (torch_is_installed()) {  torch_exp2(torch_tensor(c(0, log2(2.), 3, 4))) } #> torch_tensor #>   1 #>   2 #>   8 #>  16 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Expm1 — torch_expm1","title":"Expm1 — torch_expm1","text":"Expm1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expm1 — torch_expm1","text":"","code":"torch_expm1(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expm1 — torch_expm1","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"expm-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"expm1(input, out=NULL) -> Tensor","title":"Expm1 — torch_expm1","text":"Returns new tensor exponential elements minus 1 input. $$     y_{} = e^{x_{}} - 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expm1 — torch_expm1","text":"","code":"if (torch_is_installed()) {  torch_expm1(torch_tensor(c(0, log(2)))) } #> torch_tensor #>  0 #>  1 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Eye — torch_eye","title":"Eye — torch_eye","text":"Eye","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eye — torch_eye","text":"","code":"torch_eye(   n,   m = n,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eye — torch_eye","text":"n (int) number rows m (int, optional) number columns default n dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Eye — torch_eye","text":"Returns 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eye — torch_eye","text":"","code":"if (torch_is_installed()) {  torch_eye(3) } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Fft — torch_fft_fft","title":"Fft — torch_fft_fft","text":"Computes one dimensional discrete Fourier transform input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fft — torch_fft_fft","text":"","code":"torch_fft_fft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fft — torch_fft_fft","text":"self (Tensor) input tensor n (int) Signal length. given, input either zero-padded trimmed length computing FFT. dim (int, optional) dimension along take one dimensional FFT. norm (str, optional) Normalization mode. forward transform, correspond : \"forward\" - normalize 1/n \"backward\" - normalization \"ortho\" - normalize 1/sqrt(n) (making FFT orthonormal) Calling backward transform (ifft()) normalization mode apply overall normalization 1/n two transforms. required make IFFT exact inverse. Default \"backward\" (normalization).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fft — torch_fft_fft","text":"Fourier domain representation real signal satisfies Hermitian property: X[] = conj(X[-]). function always returns positive negative frequency terms even though, real inputs, negative frequencies redundant. rfft() returns compact one-sided representation positive frequencies returned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fft — torch_fft_fft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) t torch_fft_fft(t, norm = \"backward\")  } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":null,"dir":"Reference","previous_headings":"","what":"fftfreq — torch_fft_fftfreq","title":"fftfreq — torch_fft_fftfreq","text":"Computes discrete Fourier Transform sample frequencies signal size n.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"fftfreq — torch_fft_fftfreq","text":"","code":"torch_fft_fftfreq(   n,   d = 1,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"fftfreq — torch_fft_fftfreq","text":"n (integer) – FFT length d (float, optional) – sampling length scale. spacing individual samples FFT input. default assumes unit spacing, dividing result actual spacing gives result physical frequency units. dtype (default: torch_get_default_dtype()) desired data type returned tensor. layout (default: torch_strided()) desired layout returned tensor. device (default: NULL) desired device returned tensor.  Default: NULL, uses current device default tensor type. requires_grad (default: FALSE)  autograd record operations returned tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"fftfreq — torch_fft_fftfreq","text":"convention, torch_fft_fft() returns positive frequency terms first, followed negative frequencies reverse order, f[-] 0 < <= n/2 gives negative frequency terms. FFT length n inputs spaced length unit d, frequencies : f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n) even lengths, Nyquist frequency f[n/2] can thought either negative positive. fftfreq() follows NumPy’s convention taking negative.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"fftfreq — torch_fft_fftfreq","text":"","code":"if (torch_is_installed()) { torch_fft_fftfreq(5) # Nyquist frequency at f[3] is positive torch_fft_fftfreq(4) # Nyquist frequency at f[3] is given as negative  } #> torch_tensor #>  0.0000 #>  0.2500 #> -0.5000 #> -0.2500 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":null,"dir":"Reference","previous_headings":"","what":"Ifft — torch_fft_ifft","title":"Ifft — torch_fft_ifft","text":"Computes one dimensional inverse discrete Fourier transform input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ifft — torch_fft_ifft","text":"","code":"torch_fft_ifft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ifft — torch_fft_ifft","text":"self (Tensor) input tensor n (int, optional) – Signal length. given, input either zero-padded trimmed length computing IFFT. dim (int, optional) – dimension along take one dimensional IFFT. norm (str, optional) – Normalization mode. backward transform, correspond : \"forward\" - normalization \"backward\" - normalize 1/n \"ortho\" - normalize 1/sqrt(n) (making IFFT orthonormal) Calling forward transform normalization mode apply overall normalization 1/n two transforms. required make ifft() exact inverse. Default \"backward\" (normalize 1/n).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ifft — torch_fft_ifft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) t x <- torch_fft_fft(t, norm = \"backward\") torch_fft_ifft(x)   } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Irfft — torch_fft_irfft","title":"Irfft — torch_fft_irfft","text":"Computes inverse torch_fft_rfft(). Input interpreted one-sided Hermitian signal Fourier domain, produced torch_fft_rfft(). Hermitian property, output real-valued.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Irfft — torch_fft_irfft","text":"","code":"torch_fft_irfft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Irfft — torch_fft_irfft","text":"self (Tensor) input tensor representing half-Hermitian signal n (int) Output signal length. determines length output signal. given, input either zero-padded trimmed length computing real IFFT. Defaults even output: n=2*(input.size(dim) - 1). dim (int, optional) – dimension along take one dimensional real IFFT. norm (str, optional) – Normalization mode. backward transform, correspond : \"forward\" - normalization \"backward\" - normalize 1/n \"ortho\" - normalize 1/sqrt(n) (making real IFFT orthonormal) Calling forward transform (torch_fft_rfft()) normalization mode apply overall normalization 1/n two transforms. required make irfft() exact inverse. Default \"backward\" (normalize 1/n).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Irfft — torch_fft_irfft","text":"input frequencies must real-valued satisfy Hermitian property. cases imaginary component ignored. example, imaginary component zero-frequency term represented real output always ignored. correct interpretation Hermitian input depends length original data, given n. input shape correspond either odd even length signal. default, signal assumed even length odd signals round-trip properly. , recommended always pass signal length n.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Irfft — torch_fft_irfft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 4) x <- torch_fft_rfft(t) torch_fft_irfft(x) torch_fft_irfft(x, n = t$numel())  } #> torch_tensor #>  0.0000 #>  1.0000 #>  2.0000 #>  3.0000 #>  4.0000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Rfft — torch_fft_rfft","title":"Rfft — torch_fft_rfft","text":"Computes one dimensional Fourier transform real-valued input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rfft — torch_fft_rfft","text":"","code":"torch_fft_rfft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rfft — torch_fft_rfft","text":"self (Tensor)  real input tensor n (int) Signal length. given, input either zero-padded trimmed length computing real FFT. dim (int, optional) – dimension along take one dimensional real FFT. norm norm (str, optional) – Normalization mode. forward transform, correspond : \"forward\" - normalize 1/n \"backward\" - normalization \"ortho\" - normalize 1/sqrt(n) (making FFT orthonormal) Calling backward transform (torch_fft_irfft()) normalization mode apply overall normalization 1/n two transforms. required make irfft() exact inverse. Default \"backward\" (normalization).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rfft — torch_fft_rfft","text":"FFT real signal Hermitian-symmetric, X[] = conj(X[-]) output contains positive frequencies Nyquist frequency. compute full output, use torch_fft_fft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rfft — torch_fft_rfft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) torch_fft_rfft(t)  } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Floating point type info — torch_finfo","title":"Floating point type info — torch_finfo","text":"list represents numerical properties floating point torch.dtype","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floating point type info — torch_finfo","text":"","code":"torch_finfo(dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floating point type info — torch_finfo","text":"dtype dtype check information","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":null,"dir":"Reference","previous_headings":"","what":"Fix — torch_fix","title":"Fix — torch_fix","text":"Fix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fix — torch_fix","text":"","code":"torch_fix(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fix — torch_fix","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"fix-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"fix(input, *, out=None) -> Tensor","title":"Fix — torch_fix","text":"Alias torch_trunc()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten — torch_flatten","title":"Flatten — torch_flatten","text":"Flatten","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten — torch_flatten","text":"","code":"torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten — torch_flatten","text":"self (Tensor) input tensor. dims tensor named can pass name dimensions flatten start_dim (int) first dim flatten end_dim (int) last dim flatten out_dim name resulting dimension named tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"flatten-input-start-dim-end-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"flatten(input, start_dim=0, end_dim=-1) -> Tensor","title":"Flatten — torch_flatten","text":"Flattens contiguous range dims tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flatten — torch_flatten","text":"","code":"if (torch_is_installed()) {  t = torch_tensor(matrix(c(1, 2), ncol = 2)) torch_flatten(t) torch_flatten(t, start_dim=2) } #> torch_tensor #>  1  2 #> [ CPUFloatType{1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Flip — torch_flip","title":"Flip — torch_flip","text":"Flip","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flip — torch_flip","text":"","code":"torch_flip(self, dims)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flip — torch_flip","text":"self (Tensor) input tensor. dims (list tuple) axis flip ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"flip-input-dims-gt-tensor-","dir":"Reference","previous_headings":"","what":"flip(input, dims) -> Tensor","title":"Flip — torch_flip","text":"Reverse order n-D tensor along given axis dims.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flip — torch_flip","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 8)$view(c(2, 2, 2)) x torch_flip(x, c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   7  8 #>   5  6 #>  #> (2,.,.) =  #>   3  4 #>   1  2 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":null,"dir":"Reference","previous_headings":"","what":"Fliplr — torch_fliplr","title":"Fliplr — torch_fliplr","text":"Fliplr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fliplr — torch_fliplr","text":"","code":"torch_fliplr(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fliplr — torch_fliplr","text":"self (Tensor) Must least 2-dimensional.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fliplr — torch_fliplr","text":"Equivalent input[,-1]. Requires array least 2-D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"fliplr-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"fliplr(input) -> Tensor","title":"Fliplr — torch_fliplr","text":"Flip array left/right direction, returning new tensor. Flip entries row left/right direction. Columns preserved, appear different order .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fliplr — torch_fliplr","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(start = 1, end = 4)$view(c(2, 2)) x torch_fliplr(x) } #> torch_tensor #>  2  1 #>  4  3 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":null,"dir":"Reference","previous_headings":"","what":"Flipud — torch_flipud","title":"Flipud — torch_flipud","text":"Flipud","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flipud — torch_flipud","text":"","code":"torch_flipud(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flipud — torch_flipud","text":"self (Tensor) Must least 1-dimensional.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flipud — torch_flipud","text":"Equivalent input[-1,]. Requires array least 1-D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"flipud-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"flipud(input) -> Tensor","title":"Flipud — torch_flipud","text":"Flip array /direction, returning new tensor. Flip entries column /direction. Rows preserved, appear different order .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flipud — torch_flipud","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(start = 1, end = 4)$view(c(2, 2)) x torch_flipud(x) } #> torch_tensor #>  3  4 #>  1  2 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Floor — torch_floor","title":"Floor — torch_floor","text":"Floor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floor — torch_floor","text":"","code":"torch_floor(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floor — torch_floor","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"floor-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"floor(input, out=NULL) -> Tensor","title":"Floor — torch_floor","text":"Returns new tensor floor elements input, largest integer less equal element. $$     \\mbox{}_{} = \\left\\lfloor \\mbox{input}_{} \\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Floor — torch_floor","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_floor(a) } #> torch_tensor #>  0 #> -1 #>  0 #>  0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Floor_divide — torch_floor_divide","title":"Floor_divide — torch_floor_divide","text":"Floor_divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floor_divide — torch_floor_divide","text":"","code":"torch_floor_divide(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floor_divide — torch_floor_divide","text":"self (Tensor) numerator tensor (Tensor Scalar) denominator","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"floor-divide-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"floor_divide(input, other, out=NULL) -> Tensor","title":"Floor_divide — torch_floor_divide","text":"Return division inputs rounded nearest integer. See torch_div type promotion broadcasting rules. $$     \\mbox{{}}_i = \\left\\lfloor \\frac{{\\mbox{{input}}_i}}{{\\mbox{{}}_i}} \\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Floor_divide — torch_floor_divide","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(4.0, 3.0)) b = torch_tensor(c(2.0, 2.0)) torch_floor_divide(a, b) torch_floor_divide(a, 1.4) } #> torch_tensor #>  2 #>  2 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":null,"dir":"Reference","previous_headings":"","what":"Fmod — torch_fmod","title":"Fmod — torch_fmod","text":"Fmod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fmod — torch_fmod","text":"","code":"torch_fmod(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fmod — torch_fmod","text":"self (Tensor) dividend (Tensor float) divisor, may either number tensor shape dividend","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"fmod-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"fmod(input, other, out=NULL) -> Tensor","title":"Fmod — torch_fmod","text":"Computes element-wise remainder division. dividend divisor may contain integer floating point numbers. remainder sign dividend input. tensor, shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fmod — torch_fmod","text":"","code":"if (torch_is_installed()) {  torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2) torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5) } #> torch_tensor #>  1.0000 #>  0.5000 #>  0.0000 #>  1.0000 #>  0.5000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":null,"dir":"Reference","previous_headings":"","what":"Frac — torch_frac","title":"Frac — torch_frac","text":"Frac","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frac — torch_frac","text":"","code":"torch_frac(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frac — torch_frac","text":"self input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"frac-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"frac(input, out=NULL) -> Tensor","title":"Frac — torch_frac","text":"Computes fractional portion element input. $$     \\mbox{}_{} = \\mbox{input}_{} - \\left\\lfloor |\\mbox{input}_{}| \\right\\rfloor * \\mbox{sgn}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frac — torch_frac","text":"","code":"if (torch_is_installed()) {  torch_frac(torch_tensor(c(1, 2.5, -3.2))) } #> torch_tensor #>  0.0000 #>  0.5000 #> -0.2000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Full — torch_full","title":"Full — torch_full","text":"Full","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Full — torch_full","text":"","code":"torch_full(   size,   fill_value,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Full — torch_full","text":"size (int...) list, tuple, torch_Size integers defining        shape output tensor. fill_value NA number fill output tensor . names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Full — torch_full","text":"Returns tensor size size filled fill_value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Full — torch_full","text":"PyTorch 1.5 bool integral fill_value produce warning dtype set. future PyTorch release, dtype set bool fill_value return tensor torch.bool dtype, integral fill_value return tensor torch.long dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Full — torch_full","text":"","code":"if (torch_is_installed()) {  torch_full(list(2, 3), 3.141592) } #> torch_tensor #>  3.1416  3.1416  3.1416 #>  3.1416  3.1416  3.1416 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Full_like — torch_full_like","title":"Full_like — torch_full_like","text":"Full_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Full_like — torch_full_like","text":"","code":"torch_full_like(   input,   fill_value,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Full_like — torch_full_like","text":"input (Tensor) size input determine size output tensor. fill_value number fill output tensor . dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-","dir":"Reference","previous_headings":"","what":"full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,","title":"Full_like — torch_full_like","text":"memory_format=torch.preserve_format) -> Tensor Returns tensor size input filled fill_value. torch_full_like(input, fill_value) equivalent torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":null,"dir":"Reference","previous_headings":"","what":"Gather — torch_gather","title":"Gather — torch_gather","text":"Gather","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gather — torch_gather","text":"","code":"torch_gather(self, dim, index, sparse_grad = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gather — torch_gather","text":"self (Tensor) source tensor dim (int) axis along index index (LongTensor) indices elements gather sparse_grad (bool,optional) TRUE, gradient w.r.t. input sparse tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"gather-input-dim-index-sparse-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"gather(input, dim, index, sparse_grad=FALSE) -> Tensor","title":"Gather — torch_gather","text":"Gathers values along axis specified dim. 3-D tensor output specified ::   input n-dimensional tensor size \\((x_0, x_1..., x_{-1}, x_i, x_{+1}, ..., x_{n-1})\\) dim = , index must \\(n\\)-dimensional tensor size \\((x_0, x_1, ..., x_{-1}, y, x_{+1}, ..., x_{n-1})\\) \\(y \\geq 1\\) size index.","code":"out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0 out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1 out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gather — torch_gather","text":"","code":"if (torch_is_installed()) {  t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE)) torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64())) } #> torch_tensor #>  1  1 #>  4  3 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gcd — torch_gcd","title":"Gcd — torch_gcd","text":"Gcd","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gcd — torch_gcd","text":"","code":"torch_gcd(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gcd — torch_gcd","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gcd — torch_gcd","text":"defines \\(gcd(0, 0) = 0\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"gcd-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"gcd(input, other, *, out=None) -> Tensor","title":"Gcd — torch_gcd","text":"Computes element-wise greatest common divisor (GCD) input . input must integer types.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gcd — torch_gcd","text":"","code":"if (torch_is_installed()) {  if (torch::cuda_is_available()) { a <- torch_tensor(c(5, 10, 15), dtype = torch_long(), device = \"cuda\") b <- torch_tensor(c(3, 4, 5), dtype = torch_long(), device = \"cuda\") torch_gcd(a, b) c <- torch_tensor(c(3L), device = \"cuda\") torch_gcd(a, c) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":null,"dir":"Reference","previous_headings":"","what":"Ge — torch_ge","title":"Ge — torch_ge","text":"Ge","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ge — torch_ge","text":"","code":"torch_ge(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ge — torch_ge","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ge-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ge(input, other, out=NULL) -> Tensor","title":"Ge — torch_ge","text":"Computes \\(\\mbox{input} \\geq \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ge — torch_ge","text":"","code":"if (torch_is_installed()) {  torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  1  1 #>  0  1 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Generator object — torch_generator","title":"Create a Generator object — torch_generator","text":"torch_generator  object manages state algorithm produces pseudo random numbers. Used keyword argument many -place random sampling functions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Generator object — torch_generator","text":"","code":"torch_generator()"},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Generator object — torch_generator","text":"","code":"if (torch_is_installed()) {  # Via string generator <- torch_generator() generator$current_seed() generator$set_current_seed(1234567L) generator$current_seed()  } #> integer64 #> [1] 1234567"},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":null,"dir":"Reference","previous_headings":"","what":"Geqrf — torch_geqrf","title":"Geqrf — torch_geqrf","text":"Geqrf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geqrf — torch_geqrf","text":"","code":"torch_geqrf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geqrf — torch_geqrf","text":"self (Tensor) input matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"geqrf-input-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"geqrf(input, out=NULL) -> (Tensor, Tensor)","title":"Geqrf — torch_geqrf","text":"low-level function calling LAPACK directly. function returns namedtuple (, tau) defined LAPACK documentation geqrf_ . generally want use torch_qr instead. Computes QR decomposition input, without constructing \\(Q\\) \\(R\\) explicit separate matrices. Rather, directly calls underlying LAPACK function ?geqrf produces sequence 'elementary reflectors'. See LAPACK documentation geqrf_ details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":null,"dir":"Reference","previous_headings":"","what":"Ger — torch_ger","title":"Ger — torch_ger","text":"Ger","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ger — torch_ger","text":"","code":"torch_ger(self, vec2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ger — torch_ger","text":"self (Tensor) 1-D input vector vec2 (Tensor) 1-D input vector","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Ger — torch_ger","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ger-input-vec-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ger(input, vec2, out=NULL) -> Tensor","title":"Ger — torch_ger","text":"Outer product input vec2. input vector size \\(n\\) vec2 vector size \\(m\\), must matrix size \\((n \\times m)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ger — torch_ger","text":"","code":"if (torch_is_installed()) {  v1 = torch_arange(1., 5.) v2 = torch_arange(1., 4.) torch_ger(v1, v2) } #> torch_tensor #>   1   2   3   4 #>   2   4   6   8 #>   3   6   9  12 #>   4   8  12  16 #>   5  10  15  20 #> [ CPUFloatType{5,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":null,"dir":"Reference","previous_headings":"","what":"RNG state management — torch_get_rng_state","title":"RNG state management — torch_get_rng_state","text":"Low level functionality set change RNG state. recommended use torch_manual_seed() cases.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RNG state management — torch_get_rng_state","text":"","code":"torch_get_rng_state()  torch_set_rng_state(state)  cuda_get_rng_state(device = NULL)  cuda_set_rng_state(state, device = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RNG state management — torch_get_rng_state","text":"state tensor current state list containing state device - (CUDA). device cuda device index get set state. NULL gets state available devices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"RNG state management — torch_get_rng_state","text":"torch_set_rng_state(): Sets RNG state CPU cuda_get_rng_state(): Gets RNG state CUDA. cuda_set_rng_state(): Sets RNG state CUDA.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Greater — torch_greater","title":"Greater — torch_greater","text":"Greater","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greater — torch_greater","text":"","code":"torch_greater(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greater — torch_greater","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"greater-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"greater(input, other, *, out=None) -> Tensor","title":"Greater — torch_greater","text":"Alias torch_gt().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Greater_equal — torch_greater_equal","title":"Greater_equal — torch_greater_equal","text":"Greater_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greater_equal — torch_greater_equal","text":"","code":"torch_greater_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greater_equal — torch_greater_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"greater-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"greater_equal(input, other, *, out=None) -> Tensor","title":"Greater_equal — torch_greater_equal","text":"Alias torch_ge().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":null,"dir":"Reference","previous_headings":"","what":"Gt — torch_gt","title":"Gt — torch_gt","text":"Gt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gt — torch_gt","text":"","code":"torch_gt(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gt — torch_gt","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"gt-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"gt(input, other, out=NULL) -> Tensor","title":"Gt — torch_gt","text":"Computes \\(\\mbox{input} > \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gt — torch_gt","text":"","code":"if (torch_is_installed()) {  torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  1 #>  0  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Hamming_window — torch_hamming_window","title":"Hamming_window — torch_hamming_window","text":"Hamming_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hamming_window — torch_hamming_window","text":"","code":"torch_hamming_window(   window_length,   periodic = TRUE,   alpha = 0.54,   beta = 0.46,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hamming_window — torch_hamming_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. alpha (float, optional) coefficient \\(\\alpha\\) equation beta (float, optional) coefficient \\(\\beta\\) equation dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Hamming_window — torch_hamming_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1. This is a generalized version of `torch_hann_window`."},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Hamming_window — torch_hamming_window","text":"Hamming window function. $$     w[n] = \\alpha - \\beta\\ \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right), $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_hamming_window(L, periodic=TRUE) equal torch_hamming_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Hann_window — torch_hann_window","title":"Hann_window — torch_hann_window","text":"Hann_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hann_window — torch_hann_window","text":"","code":"torch_hann_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hann_window — torch_hann_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Hann_window — torch_hann_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Hann_window — torch_hann_window","text":"Hann window function. $$     w[n] = \\frac{1}{2}\\ \\left[1 - \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right)\\right] =             \\sin^2 \\left( \\frac{\\pi n}{N - 1} \\right), $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_hann_window(L, periodic=TRUE) equal torch_hann_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":null,"dir":"Reference","previous_headings":"","what":"Heaviside — torch_heaviside","title":"Heaviside — torch_heaviside","text":"Heaviside","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heaviside — torch_heaviside","text":"","code":"torch_heaviside(self, values)"},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heaviside — torch_heaviside","text":"self (Tensor) input tensor. values (Tensor) values use input zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"heaviside-input-values-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"heaviside(input, values, *, out=None) -> Tensor","title":"Heaviside — torch_heaviside","text":"Computes Heaviside step function element input. Heaviside step function defined : $$ \\mbox{{heaviside}}(input, values) = \\begin{array}{ll}  0, & \\mbox{input < 0}\\\\  values, & \\mbox{input == 0}\\\\  1, & \\mbox{input > 0}  \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heaviside — torch_heaviside","text":"","code":"if (torch_is_installed()) {  input <- torch_tensor(c(-1.5, 0, 2.0)) values <- torch_tensor(c(0.5)) torch_heaviside(input, values) values <- torch_tensor(c(1.2, -2.0, 3.5)) torch_heaviside(input, values) } #> torch_tensor #>  0 #> -2 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":null,"dir":"Reference","previous_headings":"","what":"Histc — torch_histc","title":"Histc — torch_histc","text":"Histc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histc — torch_histc","text":"","code":"torch_histc(self, bins = 100L, min = 0L, max = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histc — torch_histc","text":"self (Tensor) input tensor. bins (int) number histogram bins min (int) lower end range (inclusive) max (int) upper end range (inclusive)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"histc-input-bins-min-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor","title":"Histc — torch_histc","text":"Computes histogram tensor. elements sorted equal width bins min max. min max zero, minimum maximum values data used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histc — torch_histc","text":"","code":"if (torch_is_installed()) {  torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3) } #> torch_tensor #>  0 #>  2 #>  1 #>  0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Hstack — torch_hstack","title":"Hstack — torch_hstack","text":"Hstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hstack — torch_hstack","text":"","code":"torch_hstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hstack — torch_hstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"hstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"hstack(tensors, *, out=None) -> Tensor","title":"Hstack — torch_hstack","text":"Stack tensors sequence horizontally (column wise). equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hstack — torch_hstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_hstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_hstack(list(a,b)) } #> torch_tensor #>  1  4 #>  2  5 #>  3  6 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":null,"dir":"Reference","previous_headings":"","what":"Hypot — torch_hypot","title":"Hypot — torch_hypot","text":"Hypot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hypot — torch_hypot","text":"","code":"torch_hypot(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hypot — torch_hypot","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"hypot-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"hypot(input, other, *, out=None) -> Tensor","title":"Hypot — torch_hypot","text":"Given legs right triangle, return hypotenuse. $$ \\mbox{}_{} = \\sqrt{\\mbox{input}_{}^{2} + \\mbox{}_{}^{2}} $$ shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hypot — torch_hypot","text":"","code":"if (torch_is_installed()) {  torch_hypot(torch_tensor(c(4.0)), torch_tensor(c(3.0, 4.0, 5.0))) } #> torch_tensor #>  5.0000 #>  5.6569 #>  6.4031 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":null,"dir":"Reference","previous_headings":"","what":"I0 — torch_i0","title":"I0 — torch_i0","text":"I0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"I0 — torch_i0","text":"","code":"torch_i0(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"I0 — torch_i0","text":"self (Tensor) input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"i-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"i0(input, *, out=None) -> Tensor","title":"I0 — torch_i0","text":"Computes zeroth order modified Bessel function first kind element input. $$ \\mbox{}_{} = I_0(\\mbox{input}_{}) = \\sum_{k=0}^{\\infty} \\frac{(\\mbox{input}_{}^2/4)^k}{(k!)^2} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"I0 — torch_i0","text":"","code":"if (torch_is_installed()) {  torch_i0(torch_arange(start = 0, end = 5, dtype=torch_float32())) } #> torch_tensor #>   1.0000 #>   1.2661 #>   2.2796 #>   4.8808 #>  11.3019 #>  27.2399 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Integer type info — torch_iinfo","title":"Integer type info — torch_iinfo","text":"list represents numerical properties integer type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Integer type info — torch_iinfo","text":"","code":"torch_iinfo(dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Integer type info — torch_iinfo","text":"dtype dtype get information .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Imag — torch_imag","title":"Imag — torch_imag","text":"Imag","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imag — torch_imag","text":"","code":"torch_imag(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imag — torch_imag","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"imag-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"imag(input) -> Tensor","title":"Imag — torch_imag","text":"Returns imaginary part input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Imag — torch_imag","text":"yet implemented. $$     \\mbox{}_{} = imag(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imag — torch_imag","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Index torch tensors — torch_index","title":"Index torch tensors — torch_index","text":"Helper functions index tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Index torch tensors — torch_index","text":"","code":"torch_index(self, indices)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Index torch tensors — torch_index","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify values selected by indices. — torch_index_put","title":"Modify values selected by indices. — torch_index_put","text":"Modify values selected indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify values selected by indices. — torch_index_put","text":"","code":"torch_index_put(self, indices, values, accumulate = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify values selected by indices. — torch_index_put","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype. values (Tensor) values replaced indexed location. Used torch_index_put torch_index_put_. accumulate (bool) Wether instead replacing current values values, want add .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":null,"dir":"Reference","previous_headings":"","what":"In-place version of torch_index_put. — torch_index_put_","title":"In-place version of torch_index_put. — torch_index_put_","text":"-place version torch_index_put.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"In-place version of torch_index_put. — torch_index_put_","text":"","code":"torch_index_put_(self, indices, values, accumulate = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"In-place version of torch_index_put. — torch_index_put_","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype. values (Tensor) values replaced indexed location. Used torch_index_put torch_index_put_. accumulate (bool) Wether instead replacing current values values, want add .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Index_select — torch_index_select","title":"Index_select — torch_index_select","text":"Index_select","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Index_select — torch_index_select","text":"","code":"torch_index_select(self, dim, index)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Index_select — torch_index_select","text":"self (Tensor) input tensor. dim (int) dimension index index (LongTensor) 1-D tensor containing indices index","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Index_select — torch_index_select","text":"returned tensor use storage original tensor.  different shape expected, silently change correct shape, reallocating underlying storage necessary.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"index-select-input-dim-index-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"index_select(input, dim, index, out=NULL) -> Tensor","title":"Index_select — torch_index_select","text":"Returns new tensor indexes input tensor along dimension dim using entries index LongTensor. returned tensor number dimensions original tensor (input).  dim\\ th dimension size length index; dimensions size original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Index_select — torch_index_select","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) x indices = torch_tensor(c(1, 3), dtype = torch_int64()) torch_index_select(x, 1, indices) torch_index_select(x, 2, indices) } #> torch_tensor #>  0.2459  2.5702 #> -1.0427  0.2300 #>  0.0728  0.7199 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_install_path.html","id":null,"dir":"Reference","previous_headings":"","what":"A simple exported version of install_path Returns the torch installation path. — torch_install_path","title":"A simple exported version of install_path Returns the torch installation path. — torch_install_path","text":"simple exported version install_path Returns torch installation path.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_install_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A simple exported version of install_path Returns the torch installation path. — torch_install_path","text":"","code":"torch_install_path()"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse — torch_inverse","title":"Inverse — torch_inverse","text":"Inverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse — torch_inverse","text":"","code":"torch_inverse(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse — torch_inverse","text":"self (Tensor) input tensor size \\((*, n, n)\\) * zero                    batch dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Inverse — torch_inverse","text":"","code":"Irrespective of the original strides, the returned tensors will be transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"inverse-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"inverse(input, out=NULL) -> Tensor","title":"Inverse — torch_inverse","text":"Takes inverse square matrix input. input can batches 2D square tensors, case function return tensor composed individual inverses.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse — torch_inverse","text":"","code":"if (torch_is_installed()) { if (FALSE) { x = torch_rand(c(4, 4)) y = torch_inverse(x) z = torch_mm(x, y) z torch_max(torch_abs(z - torch_eye(4))) # Max non-zero # Batched inverse example x = torch_randn(c(2, 3, 4, 4)) y = torch_inverse(x) z = torch_matmul(x, y) torch_max(torch_abs(z - torch_eye(4)$expand_as(x))) # Max non-zero } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_complex — torch_is_complex","title":"Is_complex — torch_is_complex","text":"Is_complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_complex — torch_is_complex","text":"","code":"torch_is_complex(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_complex — torch_is_complex","text":"self (Tensor) PyTorch tensor test","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"is-complex-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_complex(input) -> (bool)","title":"Is_complex — torch_is_complex","text":"Returns TRUE data type input complex data type .e., one torch_complex64, torch.complex128.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_floating_point — torch_is_floating_point","title":"Is_floating_point — torch_is_floating_point","text":"Is_floating_point","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_floating_point — torch_is_floating_point","text":"","code":"torch_is_floating_point(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_floating_point — torch_is_floating_point","text":"self (Tensor) PyTorch tensor test","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"is-floating-point-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_floating_point(input) -> (bool)","title":"Is_floating_point — torch_is_floating_point","text":"Returns TRUE data type input floating point data type .e., one torch_float64, torch.float32 torch.float16.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_installed.html","id":null,"dir":"Reference","previous_headings":"","what":"Verifies if torch is installed — torch_is_installed","title":"Verifies if torch is installed — torch_is_installed","text":"Verifies torch installed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_installed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Verifies if torch is installed — torch_is_installed","text":"","code":"torch_is_installed()"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_nonzero — torch_is_nonzero","title":"Is_nonzero — torch_is_nonzero","text":"Is_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_nonzero — torch_is_nonzero","text":"","code":"torch_is_nonzero(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_nonzero — torch_is_nonzero","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"is-nonzero-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_nonzero(input) -> (bool)","title":"Is_nonzero — torch_is_nonzero","text":"Returns TRUE input single element tensor equal zero type conversions. .e. equal torch_tensor(c(0)) torch_tensor(c(0)) torch_tensor(c(FALSE)). Throws RuntimeError torch_numel() != 1 (even case sparse tensors).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is_nonzero — torch_is_nonzero","text":"","code":"if (torch_is_installed()) {  torch_is_nonzero(torch_tensor(c(0.))) torch_is_nonzero(torch_tensor(c(1.5))) torch_is_nonzero(torch_tensor(c(FALSE))) torch_is_nonzero(torch_tensor(c(3))) if (FALSE) { torch_is_nonzero(torch_tensor(c(1, 3, 5))) torch_is_nonzero(torch_tensor(c())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Isclose — torch_isclose","title":"Isclose — torch_isclose","text":"Isclose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isclose — torch_isclose","text":"","code":"torch_isclose(self, other, rtol = 1e-05, atol = 1e-08, equal_nan = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isclose — torch_isclose","text":"self (Tensor) first tensor compare (Tensor) second tensor compare rtol (float, optional) relative tolerance. Default: 1e-05 atol (float, optional) absolute tolerance. Default: 1e-08 equal_nan (bool, optional) TRUE, two NaN s considered equal. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor","title":"Isclose — torch_isclose","text":"Returns new tensor boolean elements representing element input \"close\" corresponding element . Closeness defined : $$     \\vert \\mbox{input} - \\mbox{} \\vert \\leq \\mbox{atol} + \\mbox{rtol} \\times \\vert \\mbox{} \\vert $$ input finite. input /nonfinite close equal, NaNs considered equal equal_nan TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isclose — torch_isclose","text":"","code":"if (torch_is_installed()) {  torch_isclose(torch_tensor(c(1., 2, 3)), torch_tensor(c(1 + 1e-10, 3, 4))) torch_isclose(torch_tensor(c(Inf, 4)), torch_tensor(c(Inf, 6)), rtol=.5) } #> torch_tensor #>  1 #>  1 #> [ CPUBoolType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Isfinite — torch_isfinite","title":"Isfinite — torch_isfinite","text":"Isfinite","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isfinite — torch_isfinite","text":"","code":"torch_isfinite(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isfinite — torch_isfinite","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isfinite — torch_isfinite","text":"Returns new tensor boolean elements representing element Finite .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isfinite — torch_isfinite","text":"","code":"if (torch_is_installed()) {  torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN))) } #> torch_tensor #>  1 #>  0 #>  1 #>  0 #>  0 #> [ CPUBoolType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isinf — torch_isinf","title":"Isinf — torch_isinf","text":"Isinf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isinf — torch_isinf","text":"","code":"torch_isinf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isinf — torch_isinf","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isinf — torch_isinf","text":"Returns new tensor boolean elements representing element +/-INF .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isinf — torch_isinf","text":"","code":"if (torch_is_installed()) {  torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN))) } #> torch_tensor #>  0 #>  1 #>  0 #>  1 #>  0 #> [ CPUBoolType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Isnan — torch_isnan","title":"Isnan — torch_isnan","text":"Isnan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isnan — torch_isnan","text":"","code":"torch_isnan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isnan — torch_isnan","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isnan — torch_isnan","text":"Returns new tensor boolean elements representing element NaN .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isnan — torch_isnan","text":"","code":"if (torch_is_installed()) {  torch_isnan(torch_tensor(c(1, NaN, 2))) } #> torch_tensor #>  0 #>  1 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isneginf — torch_isneginf","title":"Isneginf — torch_isneginf","text":"Isneginf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isneginf — torch_isneginf","text":"","code":"torch_isneginf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isneginf — torch_isneginf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"isneginf-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"isneginf(input, *, out=None) -> Tensor","title":"Isneginf — torch_isneginf","text":"Tests element input negative infinity .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isneginf — torch_isneginf","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(-Inf, Inf, 1.2)) torch_isneginf(a) } #> torch_tensor #>  1 #>  0 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isposinf — torch_isposinf","title":"Isposinf — torch_isposinf","text":"Isposinf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isposinf — torch_isposinf","text":"","code":"torch_isposinf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isposinf — torch_isposinf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"isposinf-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"isposinf(input, *, out=None) -> Tensor","title":"Isposinf — torch_isposinf","text":"Tests element input positive infinity .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isposinf — torch_isposinf","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(-Inf, Inf, 1.2)) torch_isposinf(a) } #> torch_tensor #>  0 #>  1 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":null,"dir":"Reference","previous_headings":"","what":"Isreal — torch_isreal","title":"Isreal — torch_isreal","text":"Isreal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isreal — torch_isreal","text":"","code":"torch_isreal(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isreal — torch_isreal","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"isreal-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"isreal(input) -> Tensor","title":"Isreal — torch_isreal","text":"Returns new tensor boolean elements representing element input real-valued . real-valued types considered real. Complex values considered real imaginary part 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isreal — torch_isreal","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_isreal(torch_tensor(c(1, 1+1i, 2+0i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Istft — torch_istft","title":"Istft — torch_istft","text":"Inverse short time Fourier Transform. expected inverse torch_stft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Istft — torch_istft","text":"","code":"torch_istft(   self,   n_fft,   hop_length = NULL,   win_length = NULL,   window = list(),   center = TRUE,   normalized = FALSE,   onesided = NULL,   length = NULL,   return_complex = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Istft — torch_istft","text":"self (Tensor) input tensor. Expected output torch_stft(), can either complex (channel, fft_size, n_frame), real (channel, fft_size, n_frame, 2) channel dimension optional. n_fft (int) Size Fourier transform hop_length (Optional[int]) distance neighboring sliding window frames. (Default: n_fft %% 4) win_length (Optional[int]) size window frame STFT filter. (Default: n_fft) window (Optional(torch.Tensor)) optional window function. (Default: torch_ones(win_length)) center (bool) Whether input padded sides \\(t\\)-th frame centered time \\(t \\times \\mbox{hop\\_length}\\). (Default: TRUE) normalized (bool) Whether STFT normalized. (Default: FALSE) onesided (Optional(bool)) Whether STFT onesided. (Default: TRUE n_fft != fft_size input size) length (Optional(int)]) amount trim signal (.e. original signal length). (Default: whole signal) return_complex (Optional(bool)) Whether output complex, input assumed derive real signal window. Note incompatible onesided=TRUE. (Default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Istft — torch_istft","text":"parameters (+ additional optional parameter length) return least squares estimation original signal. algorithm check using NOLA condition (nonzero overlap). Important consideration parameters window center envelop created summation windows never zero certain point time. Specifically, \\(\\sum_{t=-\\infty}^{\\infty} |w|^2(n-t\\times hop_length) \\neq 0\\). Since torch_stft() discards elements end signal fit frame, istft may return shorter signal original signal (can occur center FALSE since signal padded). center TRUE, padding e.g. 'constant', 'reflect', etc. Left padding can trimmed exactly can calculated right padding calculated without additional information. Example: Suppose last window : [c(17, 18, 0, 0, 0) vs c(18, 0, 0, 0, 0) n_fft, hop_length, win_length prevents calculation right padding. additional values zeros reflection signal providing length useful. length None padding aggressively removed (loss signal). D. W. Griffin J. S. Lim, \"Signal estimation modified short-time Fourier transform,\" IEEE Trans. ASSP, vol.32, .2, pp.236-243, Apr. 1984.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiser_window — torch_kaiser_window","title":"Kaiser_window — torch_kaiser_window","text":"Kaiser_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiser_window — torch_kaiser_window","text":"","code":"torch_kaiser_window(   window_length,   periodic,   beta,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiser_window — torch_kaiser_window","text":"window_length (int) length window. periodic (bool, optional) TRUE, returns periodic window suitable use spectral analysis.        FALSE, returns symmetric window suitable use filter design. beta (float, optional) shape parameter window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Kaiser_window — torch_kaiser_window","text":"window_length one, returned window single element tensor containing one.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -> Tensor","title":"Kaiser_window — torch_kaiser_window","text":"Computes Kaiser window window length window_length shape parameter beta. Let I_0 zeroth order modified Bessel function first kind (see torch_i0()) N = L - 1 periodic FALSE L periodic TRUE, L window_length. function computes: $$     out_i = I_0 \\left( \\beta \\sqrt{1 - \\left( {\\frac{- N/2}{N/2}} \\right) ^2 } \\right) / I_0( \\beta ) $$ Calling torch_kaiser_window(L, B, periodic=TRUE) equivalent calling torch_kaiser_window(L + 1, B, periodic=FALSE)[:-1]). periodic argument intended helpful shorthand produce periodic window input functions like torch_stft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kron.html","id":null,"dir":"Reference","previous_headings":"","what":"Kronecker product — torch_kron","title":"Kronecker product — torch_kron","text":"Computes Kronecker product self .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kron.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kronecker product — torch_kron","text":"","code":"torch_kron(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_kron.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kronecker product — torch_kron","text":"self (Tensor) input Tensor (Tensor) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":null,"dir":"Reference","previous_headings":"","what":"Kthvalue — torch_kthvalue","title":"Kthvalue — torch_kthvalue","text":"Kthvalue","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kthvalue — torch_kthvalue","text":"","code":"torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kthvalue — torch_kthvalue","text":"self (Tensor) input tensor. k (int) k k-th smallest element dim (int, optional) dimension find kth value along keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Kthvalue — torch_kthvalue","text":"Returns namedtuple (values, indices) values k th smallest element row input tensor given dimension dim. indices index location element found. dim given, last dimension input chosen. keepdim TRUE, values indices tensors size input, except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting values indices tensors 1 fewer dimension input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kthvalue — torch_kthvalue","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 6) x torch_kthvalue(x, 4) x <- torch_arange(1,6)$resize_(c(2,3)) x torch_kthvalue(x, 2, 1, TRUE) } #> [[1]] #> torch_tensor #>  4  5  6 #> [ CPUFloatType{1,3} ] #>  #> [[2]] #> torch_tensor #>  1  1  1 #> [ CPULongType{1,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the corresponding layout — torch_layout","title":"Creates the corresponding layout — torch_layout","text":"Creates corresponding layout","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the corresponding layout — torch_layout","text":"","code":"torch_strided()  torch_sparse_coo()"},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":null,"dir":"Reference","previous_headings":"","what":"Lcm — torch_lcm","title":"Lcm — torch_lcm","text":"Lcm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lcm — torch_lcm","text":"","code":"torch_lcm(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lcm — torch_lcm","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Lcm — torch_lcm","text":"defines \\(lcm(0, 0) = 0\\) \\(lcm(0, ) = 0\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"lcm-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"lcm(input, other, *, out=None) -> Tensor","title":"Lcm — torch_lcm","text":"Computes element-wise least common multiple (LCM) input . input must integer types.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lcm — torch_lcm","text":"","code":"if (torch_is_installed()) {  if (torch::cuda_is_available()) { a <- torch_tensor(c(5, 10, 15), dtype = torch_long(), device = \"cuda\") b <- torch_tensor(c(3, 4, 5), dtype = torch_long(), device = \"cuda\") torch_lcm(a, b) c <- torch_tensor(c(3L), device = \"cuda\") torch_lcm(a, c) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":null,"dir":"Reference","previous_headings":"","what":"Le — torch_le","title":"Le — torch_le","text":"Le","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Le — torch_le","text":"","code":"torch_le(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Le — torch_le","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"le-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"le(input, other, out=NULL) -> Tensor","title":"Le — torch_le","text":"Computes \\(\\mbox{input} \\leq \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Le — torch_le","text":"","code":"if (torch_is_installed()) {  torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  1  0 #>  1  1 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":null,"dir":"Reference","previous_headings":"","what":"Lerp — torch_lerp","title":"Lerp — torch_lerp","text":"Lerp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lerp — torch_lerp","text":"","code":"torch_lerp(self, end, weight)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lerp — torch_lerp","text":"self (Tensor) tensor starting points end (Tensor) tensor ending points weight (float tensor) weight interpolation formula","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"lerp-input-end-weight-out-null-","dir":"Reference","previous_headings":"","what":"lerp(input, end, weight, out=NULL)","title":"Lerp — torch_lerp","text":"linear interpolation two tensors start (given input) end based scalar tensor weight returns resulting tensor. $$     \\mbox{}_i = \\mbox{start}_i + \\mbox{weight}_i \\times (\\mbox{end}_i - \\mbox{start}_i) $$ shapes start end must broadcastable . weight tensor, shapes weight, start, end must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lerp — torch_lerp","text":"","code":"if (torch_is_installed()) {  start = torch_arange(1, 4) end = torch_empty(4)$fill_(10) start end torch_lerp(start, end, 0.5) torch_lerp(start, end, torch_full_like(start, 0.5)) } #> torch_tensor #>  5.5000 #>  6.0000 #>  6.5000 #>  7.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Less — torch_less","title":"Less — torch_less","text":"Less","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Less — torch_less","text":"","code":"torch_less(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Less — torch_less","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"less-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"less(input, other, *, out=None) -> Tensor","title":"Less — torch_less","text":"Alias torch_lt().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Less_equal — torch_less_equal","title":"Less_equal — torch_less_equal","text":"Less_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Less_equal — torch_less_equal","text":"","code":"torch_less_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Less_equal — torch_less_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"less-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"less_equal(input, other, *, out=None) -> Tensor","title":"Less_equal — torch_less_equal","text":"Alias torch_le().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Lgamma — torch_lgamma","title":"Lgamma — torch_lgamma","text":"Lgamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lgamma — torch_lgamma","text":"","code":"torch_lgamma(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lgamma — torch_lgamma","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"lgamma-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lgamma(input, out=NULL) -> Tensor","title":"Lgamma — torch_lgamma","text":"Computes logarithm gamma function input. $$     \\mbox{}_{} = \\log \\Gamma(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lgamma — torch_lgamma","text":"","code":"if (torch_is_installed()) {  a = torch_arange(0.5, 2, 0.5) torch_lgamma(a) } #> torch_tensor #>  0.5724 #>  0.0000 #> -0.1208 #>  0.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Linspace — torch_linspace","title":"Linspace — torch_linspace","text":"Linspace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linspace — torch_linspace","text":"","code":"torch_linspace(   start,   end,   steps = 100,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linspace — torch_linspace","text":"start (float) starting value set points end (float) ending value set points steps (int) number points sample start        end. Default: 100. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Linspace — torch_linspace","text":"Returns one-dimensional tensor steps equally spaced points start end. output tensor 1-D size steps.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linspace — torch_linspace","text":"","code":"if (torch_is_installed()) {  torch_linspace(3, 10, steps=5) torch_linspace(-10, 10, steps=5) torch_linspace(start=-10, end=10, steps=5) torch_linspace(start=-10, end=10, steps=1) } #> torch_tensor #> -10 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a saved object — torch_load","title":"Loads a saved object — torch_load","text":"Loads saved object","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a saved object — torch_load","text":"","code":"torch_load(path, device = \"cpu\")"},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a saved object — torch_load","text":"path path saved object device device load tensors . default load cpu can also load cuda device. NULL device tensor saved reused.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Log — torch_log","title":"Log — torch_log","text":"Log","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log — torch_log","text":"","code":"torch_log(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log — torch_log","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log(input, out=NULL) -> Tensor","title":"Log — torch_log","text":"Returns new tensor natural logarithm elements input. $$     y_{} = \\log_{e} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log — torch_log","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_log(a) } #> torch_tensor #>     nan #>     nan #>     nan #>     nan #> -1.1847 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Log10 — torch_log10","title":"Log10 — torch_log10","text":"Log10","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log10 — torch_log10","text":"","code":"torch_log10(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log10 — torch_log10","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log10(input, out=NULL) -> Tensor","title":"Log10 — torch_log10","text":"Returns new tensor logarithm base 10 elements input. $$     y_{} = \\log_{10} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log10 — torch_log10","text":"","code":"if (torch_is_installed()) {  a = torch_rand(5) a torch_log10(a) } #> torch_tensor #> 0.01 * #> -8.2896 #> -100.2900 #> -3.4480 #> -46.5474 #> -13.5409 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Log1p — torch_log1p","title":"Log1p — torch_log1p","text":"Log1p","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log1p — torch_log1p","text":"","code":"torch_log1p(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log1p — torch_log1p","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log1p — torch_log1p","text":"function accurate torch_log small values input","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"log-p-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log1p(input, out=NULL) -> Tensor","title":"Log1p — torch_log1p","text":"Returns new tensor natural logarithm (1 + input). $$     y_i = \\log_{e} (x_i + 1) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log1p — torch_log1p","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_log1p(a) } #> torch_tensor #> -0.4804 #>  0.2434 #>  0.0869 #>  0.7760 #> -1.0442 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Log2 — torch_log2","title":"Log2 — torch_log2","text":"Log2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log2 — torch_log2","text":"","code":"torch_log2(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log2 — torch_log2","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log2(input, out=NULL) -> Tensor","title":"Log2 — torch_log2","text":"Returns new tensor logarithm base 2 elements input. $$     y_{} = \\log_{2} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log2 — torch_log2","text":"","code":"if (torch_is_installed()) {  a = torch_rand(5) a torch_log2(a) } #> torch_tensor #> -1.7491 #> -3.9394 #> -1.5573 #> -0.6504 #> -0.4162 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logaddexp — torch_logaddexp","title":"Logaddexp — torch_logaddexp","text":"Logaddexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logaddexp — torch_logaddexp","text":"","code":"torch_logaddexp(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logaddexp — torch_logaddexp","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"logaddexp-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logaddexp(input, other, *, out=None) -> Tensor","title":"Logaddexp — torch_logaddexp","text":"Logarithm sum exponentiations inputs. Calculates pointwise \\(\\log\\left(e^x + e^y\\right)\\). function useful statistics calculated probabilities events may small exceed range normal floating point numbers. cases logarithm calculated probability stored. function allows adding probabilities stored fashion. op disambiguated torch_logsumexp() performs reduction single tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logaddexp — torch_logaddexp","text":"","code":"if (torch_is_installed()) {  torch_logaddexp(torch_tensor(c(-1.0)), torch_tensor(c(-1.0, -2, -3))) torch_logaddexp(torch_tensor(c(-100.0, -200, -300)), torch_tensor(c(-1.0, -2, -3))) torch_logaddexp(torch_tensor(c(1.0, 2000, 30000)), torch_tensor(c(-1.0, -2, -3))) } #> torch_tensor #>      1.1269 #>   2000.0000 #>  30000.0000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":null,"dir":"Reference","previous_headings":"","what":"Logaddexp2 — torch_logaddexp2","title":"Logaddexp2 — torch_logaddexp2","text":"Logaddexp2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logaddexp2 — torch_logaddexp2","text":"","code":"torch_logaddexp2(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logaddexp2 — torch_logaddexp2","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"logaddexp-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logaddexp2(input, other, *, out=None) -> Tensor","title":"Logaddexp2 — torch_logaddexp2","text":"Logarithm sum exponentiations inputs base-2. Calculates pointwise \\(\\log_2\\left(2^x + 2^y\\right)\\). See torch_logaddexp() details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logcumsumexp — torch_logcumsumexp","title":"Logcumsumexp — torch_logcumsumexp","text":"Logcumsumexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logcumsumexp — torch_logcumsumexp","text":"","code":"torch_logcumsumexp(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logcumsumexp — torch_logcumsumexp","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"logcumsumexp-input-dim-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logcumsumexp(input, dim, *, out=None) -> Tensor","title":"Logcumsumexp — torch_logcumsumexp","text":"Returns logarithm cumulative summation exponentiation elements input dimension dim. summation index \\(j\\) given dim indices \\(\\), result $$         \\mbox{logcumsumexp}(x)_{ij} = \\log \\sum\\limits_{j=0}^{} \\exp(x_{ij}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logcumsumexp — torch_logcumsumexp","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(10)) torch_logcumsumexp(a, dim=1) } #> torch_tensor #> -1.8849 #> -0.8080 #>  0.6037 #>  1.6575 #>  2.0437 #>  2.1585 #>  2.2251 #>  2.3805 #>  2.5151 #>  2.5274 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Logdet — torch_logdet","title":"Logdet — torch_logdet","text":"Logdet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logdet — torch_logdet","text":"","code":"torch_logdet(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logdet — torch_logdet","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logdet — torch_logdet","text":"","code":"Result is `-inf` if `input` has zero log determinant, and is `NaN` if `input` has negative determinant. Backward through `logdet` internally uses SVD results when `input` is not invertible. In this case, double backward through `logdet` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"logdet-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"logdet(input) -> Tensor","title":"Logdet — torch_logdet","text":"Calculates log determinant square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logdet — torch_logdet","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) torch_det(A) torch_logdet(A) A A$det() A$det()$log() } #> torch_tensor #> nan #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_and — torch_logical_and","title":"Logical_and — torch_logical_and","text":"Logical_and","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_and — torch_logical_and","text":"","code":"torch_logical_and(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_and — torch_logical_and","text":"self (Tensor) input tensor. (Tensor) tensor compute ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"logical-and-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_and(input, other, out=NULL) -> Tensor","title":"Logical_and — torch_logical_and","text":"Computes element-wise logical given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_and — torch_logical_and","text":"","code":"if (torch_is_installed()) {  torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_and(a, b) if (FALSE) { torch_logical_and(a, b, out=torch_empty(4, dtype=torch_bool())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_not — torch_logical_not","title":"Logical_not — torch_logical_not","text":"Logical_not","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_not — torch_logical_not","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"logical-not-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_not(input, out=NULL) -> Tensor","title":"Logical_not — torch_logical_not","text":"Computes element-wise logical given input tensor. specified, output tensor bool dtype. input tensor bool tensor, zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_not — torch_logical_not","text":"","code":"if (torch_is_installed()) {  torch_logical_not(torch_tensor(c(TRUE, FALSE))) torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8())) torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double())) } #> torch_tensor #>  1 #>  0 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_or — torch_logical_or","title":"Logical_or — torch_logical_or","text":"Logical_or","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_or — torch_logical_or","text":"","code":"torch_logical_or(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_or — torch_logical_or","text":"self (Tensor) input tensor. (Tensor) tensor compute ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"logical-or-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_or(input, other, out=NULL) -> Tensor","title":"Logical_or — torch_logical_or","text":"Computes element-wise logical given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_or — torch_logical_or","text":"","code":"if (torch_is_installed()) {  torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_or(a, b) if (FALSE) { torch_logical_or(a$double(), b$double()) torch_logical_or(a$double(), b) torch_logical_or(a, b, out=torch_empty(4, dtype=torch_bool())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_xor — torch_logical_xor","title":"Logical_xor — torch_logical_xor","text":"Logical_xor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_xor — torch_logical_xor","text":"","code":"torch_logical_xor(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_xor — torch_logical_xor","text":"self (Tensor) input tensor. (Tensor) tensor compute XOR ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"logical-xor-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_xor(input, other, out=NULL) -> Tensor","title":"Logical_xor — torch_logical_xor","text":"Computes element-wise logical XOR given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_xor — torch_logical_xor","text":"","code":"if (torch_is_installed()) {  torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_xor(a, b) torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double())) torch_logical_xor(a$to(dtype=torch_double()), b) } #> torch_tensor #>  1 #>  1 #>  0 #>  0 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":null,"dir":"Reference","previous_headings":"","what":"Logit — torch_logit","title":"Logit — torch_logit","text":"Logit","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logit — torch_logit","text":"","code":"torch_logit(self, eps = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logit — torch_logit","text":"self (Tensor) input tensor. eps (float, optional) epsilon input clamp bound. Default: None","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"logit-input-eps-none-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logit(input, eps=None, *, out=None) -> Tensor","title":"Logit — torch_logit","text":"Returns new tensor logit elements input. input clamped [eps, 1 - eps] eps None. eps None input < 0 input > 1, function yields NaN. $$     y_{} = \\ln(\\frac{z_{}}{1 - z_{}}) \\\\     z_{} = \\begin{array}{ll}         x_{} & \\mbox{eps None} \\\\         \\mbox{eps} & \\mbox{} x_{} < \\mbox{eps} \\\\         x_{} & \\mbox{} \\mbox{eps} \\leq x_{} \\leq 1 - \\mbox{eps} \\\\         1 - \\mbox{eps} & \\mbox{} x_{} > 1 - \\mbox{eps}     \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logit — torch_logit","text":"","code":"if (torch_is_installed()) {  a <- torch_rand(5) a torch_logit(a, eps=1e-6) } #> torch_tensor #>  0.9320 #>  0.3958 #>  3.1804 #> -0.0449 #>  0.4256 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Logspace — torch_logspace","title":"Logspace — torch_logspace","text":"Logspace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logspace — torch_logspace","text":"","code":"torch_logspace(   start,   end,   steps = 100,   base = 10,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logspace — torch_logspace","text":"start (float) starting value set points end (float) ending value set points steps (int) number points sample start        end. Default: 100. base (float) base logarithm function. Default: 10.0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Logspace — torch_logspace","text":"Returns one-dimensional tensor steps points logarithmically spaced base base \\({\\mbox{base}}^{\\mbox{start}}\\) \\({\\mbox{base}}^{\\mbox{end}}\\). output tensor 1-D size steps.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logspace — torch_logspace","text":"","code":"if (torch_is_installed()) {  torch_logspace(start=-10, end=10, steps=5) torch_logspace(start=0.1, end=1.0, steps=5) torch_logspace(start=0.1, end=1.0, steps=1) torch_logspace(start=2, end=2, steps=1, base=2) } #> torch_tensor #>  4 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logsumexp — torch_logsumexp","title":"Logsumexp — torch_logsumexp","text":"Logsumexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logsumexp — torch_logsumexp","text":"","code":"torch_logsumexp(self, dim, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logsumexp — torch_logsumexp","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"logsumexp-input-dim-keepdim-false-out-null-","dir":"Reference","previous_headings":"","what":"logsumexp(input, dim, keepdim=False, out=NULL)","title":"Logsumexp — torch_logsumexp","text":"Returns log summed exponentials row input tensor given dimension dim. computation numerically stabilized. summation index \\(j\\) given dim indices \\(\\), result $$         \\mbox{logsumexp}(x)_{} = \\log \\sum_j \\exp(x_{ij}) $$ keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logsumexp — torch_logsumexp","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) torch_logsumexp(a, 1) } #> torch_tensor #>  1.8542 #>  1.3111 #>  2.1658 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Lstsq — torch_lstsq","title":"Lstsq — torch_lstsq","text":"Lstsq","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lstsq — torch_lstsq","text":"self (Tensor) matrix \\(B\\) (Tensor) \\(m\\) \\(n\\) matrix \\(\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Lstsq — torch_lstsq","text":"","code":"The case when \\eqn{m < n} is not supported on the GPU."},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"lstsq-input-a-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lstsq(input, A, out=NULL) -> Tensor","title":"Lstsq — torch_lstsq","text":"Computes solution least squares least norm problems full rank matrix \\(\\) size \\((m \\times n)\\) matrix \\(B\\) size \\((m \\times k)\\). \\(m \\geq n\\), torch_lstsq() solves least-squares problem: $$    \\begin{array}{ll}    \\min_X & \\|AX-B\\|_2.    \\end{array} $$ \\(m < n\\), torch_lstsq() solves least-norm problem: $$    \\begin{array}{llll}    \\min_X & \\|X\\|_2 & \\mbox{subject } & AX = B.    \\end{array} $$ Returned tensor \\(X\\) shape \\((\\mbox{max}(m, n) \\times k)\\). first \\(n\\) rows \\(X\\) contains solution. \\(m \\geq n\\), residual sum squares solution column given sum squares elements remaining \\(m - n\\) rows column.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":null,"dir":"Reference","previous_headings":"","what":"Lt — torch_lt","title":"Lt — torch_lt","text":"Lt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lt — torch_lt","text":"","code":"torch_lt(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lt — torch_lt","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"lt-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lt(input, other, out=NULL) -> Tensor","title":"Lt — torch_lt","text":"Computes \\(\\mbox{input} < \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lt — torch_lt","text":"","code":"if (torch_is_installed()) {  torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  0 #>  1  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":null,"dir":"Reference","previous_headings":"","what":"LU — torch_lu","title":"LU — torch_lu","text":"Computes LU factorization matrix batches matrices . Returns tuple containing LU factorization pivots . Pivoting done pivot set True.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LU — torch_lu","text":"","code":"torch_lu(A, pivot = TRUE, get_infos = FALSE, out = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LU — torch_lu","text":"(Tensor) tensor factor size (, m, n)(,m,n) pivot (bool, optional) – controls whether pivoting done. Default: TRUE get_infos (bool, optional) – set True, returns info IntTensor. Default: FALSE (tuple, optional) – optional output tuple. get_infos True, elements tuple Tensor, IntTensor, IntTensor. get_infos False, elements tuple Tensor, IntTensor. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LU — torch_lu","text":"","code":"if (torch_is_installed()) {  A <- torch_randn(c(2, 3, 3)) torch_lu(A) } #> [[1]] #> torch_tensor #> (1,.,.) =  #>   0.3834  0.6801 -1.2962 #>   0.1962 -0.7595  1.2475 #>  -0.8721 -0.6387  0.5538 #>  #> (2,.,.) =  #>  -2.8596 -0.3844  1.0102 #>   0.7906 -0.3574 -1.2487 #>  -0.4587  0.5223  1.4304 #> [ CPUFloatType{2,3,3} ] #>  #> [[2]] #> torch_tensor #>  2  3  3 #>  2  3  3 #> [ CPUIntType{2,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Lu_solve — torch_lu_solve","title":"Lu_solve — torch_lu_solve","text":"Lu_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lu_solve — torch_lu_solve","text":"","code":"torch_lu_solve(self, LU_data, LU_pivots)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lu_solve — torch_lu_solve","text":"self (Tensor) RHS tensor size \\((*, m, k)\\), \\(*\\)                zero batch dimensions. LU_data (Tensor) pivoted LU factorization torch_lu size \\((*, m, m)\\),                       \\(*\\) zero batch dimensions. LU_pivots (IntTensor) pivots LU factorization torch_lu size \\((*, m)\\),                           \\(*\\) zero batch dimensions.                           batch dimensions LU_pivots must equal batch dimensions                           LU_data.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor","title":"Lu_solve — torch_lu_solve","text":"Returns LU solve linear system \\(Ax = b\\) using partially pivoted LU factorization torch_lu.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lu_solve — torch_lu_solve","text":"","code":"if (torch_is_installed()) { A = torch_randn(c(2, 3, 3)) b = torch_randn(c(2, 3, 1)) out = torch_lu(A) x = torch_lu_solve(b, out[[1]], out[[2]]) torch_norm(torch_bmm(A, x) - b) } #> torch_tensor #> 2.14908e-07 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":null,"dir":"Reference","previous_headings":"","what":"Lu_unpack — torch_lu_unpack","title":"Lu_unpack — torch_lu_unpack","text":"Lu_unpack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lu_unpack — torch_lu_unpack","text":"","code":"torch_lu_unpack(LU_data, LU_pivots, unpack_data = TRUE, unpack_pivots = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lu_unpack — torch_lu_unpack","text":"LU_data (Tensor) – packed LU factorization data LU_pivots (Tensor) – packed LU factorization pivots unpack_data (logical) – flag indicating data unpacked. FALSE, returned L U NULL Default: TRUE unpack_pivots (logical) – flag indicating pivots unpacked permutation matrix P. FALSE, returned P None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":"lu-unpack-lu-data-lu-pivots-unpack-data-true-unpack-pivots-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"lu_unpack(LU_data, LU_pivots, unpack_data = TRUE, unpack_pivots=TRUE) -> Tensor","title":"Lu_unpack — torch_lu_unpack","text":"Unpacks data pivots LU factorization tensor tensors L U permutation tensor P LU_data_and_pivots <- torch_lu(P$matmul(L)$matmul(U)). Returns list tensors list(P tensor (permutation matrix), L tensor, U tensor)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the seed for generating random numbers. — torch_manual_seed","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"Sets seed generating random numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"","code":"torch_manual_seed(seed)  local_torch_manual_seed(seed, .env = parent.frame())  with_torch_manual_seed(code, ..., seed)"},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"seed integer seed. .env environment take modifications manual_seed. code expression run context seed ... unused currently.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"local_torch_manual_seed(): Modifies torch seed environment scope. with_torch_manual_seed(): context change seed function execution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"Currently local_torch_manual_seed with_torch_manual_seed work Tensors MPS device. can sample tensors CPU move MPS reproducibility required.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Masked_select — torch_masked_select","title":"Masked_select — torch_masked_select","text":"Masked_select","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masked_select — torch_masked_select","text":"","code":"torch_masked_select(self, mask)"},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masked_select — torch_masked_select","text":"self (Tensor) input tensor. mask (BoolTensor) tensor containing binary mask index ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masked_select — torch_masked_select","text":"returned tensor use storage original tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"masked-select-input-mask-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"masked_select(input, mask, out=NULL) -> Tensor","title":"Masked_select — torch_masked_select","text":"Returns new 1-D tensor indexes input tensor according boolean mask mask BoolTensor. shapes mask tensor input tensor need match, must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masked_select — torch_masked_select","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) x mask = x$ge(0.5) mask torch_masked_select(x, mask) } #> torch_tensor #>  1.6461 #>  1.4241 #>  1.1868 #>  0.5365 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matmul — torch_matmul","title":"Matmul — torch_matmul","text":"Matmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matmul — torch_matmul","text":"","code":"torch_matmul(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matmul — torch_matmul","text":"self (Tensor) first tensor multiplied (Tensor) second tensor multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matmul — torch_matmul","text":"","code":"The 1-dimensional dot product version of this function does not support an `out` parameter."},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"matmul-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"matmul(input, other, out=NULL) -> Tensor","title":"Matmul — torch_matmul","text":"Matrix product two tensors. behavior depends dimensionality tensors follows: tensors 1-dimensional, dot product (scalar) returned. arguments 2-dimensional, matrix-matrix product returned. first argument 1-dimensional second argument 2-dimensional, 1 prepended dimension purpose matrix multiply. matrix multiply, prepended dimension removed. first argument 2-dimensional second argument 1-dimensional, matrix-vector product returned. arguments least 1-dimensional least one argument N-dimensional (N > 2), batched matrix multiply returned.  first argument 1-dimensional, 1 prepended dimension purpose batched matrix multiply removed .  second argument 1-dimensional, 1 appended dimension purpose batched matrix multiple removed . non-matrix (.e. batch) dimensions broadcasted  (thus must broadcastable).  example, input \\((j \\times 1 \\times n \\times m)\\) tensor \\((k \\times m \\times p)\\) tensor, \\((j \\times k \\times n \\times p)\\) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matmul — torch_matmul","text":"","code":"if (torch_is_installed()) {  # vector x vector tensor1 = torch_randn(c(3)) tensor2 = torch_randn(c(3)) torch_matmul(tensor1, tensor2) # matrix x vector tensor1 = torch_randn(c(3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2) # batched matrix x broadcasted vector tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2) # batched matrix x batched matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(10, 4, 5)) torch_matmul(tensor1, tensor2) # batched matrix x broadcasted matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4, 5)) torch_matmul(tensor1, tensor2) } #> torch_tensor #> (1,.,.) =  #>  -0.6703  0.1748  0.1019  2.1782 -1.9164 #>   3.4863  0.7423 -0.8082  1.5387 -0.2886 #>   2.2476  0.9486  1.8962 -1.2999  1.4790 #>  #> (2,.,.) =  #>   0.9446  1.0571  1.4544 -0.2813  0.9670 #>   1.4007 -0.4533  0.5070 -0.2281 -0.8250 #>  -0.2017  0.3509  0.6494  0.0332  0.1461 #>  #> (3,.,.) =  #>  -3.4653 -1.6867 -0.8829 -0.4846 -1.2331 #>  -0.6890  0.2529  1.7228 -0.7841  0.2751 #>  -4.6621  0.6449  0.9571  2.8237 -2.0121 #>  #> (4,.,.) =  #>  -0.3645 -0.8249  0.9828 -2.5264  0.8061 #>  -5.7000 -0.6100 -0.2068  0.9414 -1.2617 #>   1.9504  1.7264  2.3723 -1.4703  2.5558 #>  #> (5,.,.) =  #>  -0.3427  0.8398  1.4759 -0.6493  1.1072 #>   2.5539 -1.2103 -1.4570 -0.5723 -0.6212 #>   2.2649 -0.9920 -2.4079  0.2779 -0.6104 #>  #> (6,.,.) =  #>  -1.8022  0.1544 -0.5616  0.9548 -0.2760 #>  -0.6957 -2.7108 -2.1812 -1.5212 -1.2216 #>   2.8840 -0.3510 -1.7105 -1.5380  1.7126 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_exp — torch_matrix_exp","title":"Matrix_exp — torch_matrix_exp","text":"Matrix_exp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_exp — torch_matrix_exp","text":"","code":"torch_matrix_exp(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_exp — torch_matrix_exp","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"matrix-power-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_power(input) -> Tensor","title":"Matrix_exp — torch_matrix_exp","text":"Returns matrix exponential. Supports batched input. matrix , matrix exponential defined $$     \\exp^= \\sum_{k=0}^\\infty ^k / k!. $$ implementation based : Bader, P.; Blanes, S.; Casas, F. Computing Matrix Exponential Optimized Taylor Polynomial Approximation. Mathematics 2019, 7, 1174.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_exp — torch_matrix_exp","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(2, 2, 2)) a[1, , ] <- torch_eye(2, 2) a[2, , ] <- 2 * torch_eye(2, 2) a torch_matrix_exp(a)  x <- torch_tensor(rbind(c(0, pi/3), c(-pi/3, 0))) x$matrix_exp() # should be [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]] } #> torch_tensor #>  0.5000  0.8660 #> -0.8660  0.5000 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_power — torch_matrix_power","title":"Matrix_power — torch_matrix_power","text":"Matrix_power","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_power — torch_matrix_power","text":"","code":"torch_matrix_power(self, n)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_power — torch_matrix_power","text":"self (Tensor) input tensor. n (int) power raise matrix ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"matrix-power-input-n-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_power(input, n) -> Tensor","title":"Matrix_power — torch_matrix_power","text":"Returns matrix raised power n square matrices. batch matrices, individual matrix raised power n. n negative, inverse matrix (invertible) raised power n.  batch matrices, batched inverse (invertible) raised power n. n 0, identity matrix returned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_power — torch_matrix_power","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(2, 2, 2)) a torch_matrix_power(a, 3) } #> torch_tensor #> (1,.,.) =  #>  -1.8053 -0.0262 #>   0.1418 -1.8350 #>  #> (2,.,.) =  #>   0.3966 -0.3756 #>  -0.3245  0.3026 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_rank — torch_matrix_rank","title":"Matrix_rank — torch_matrix_rank","text":"Matrix_rank","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_rank — torch_matrix_rank","text":"self (Tensor) input 2-D tensor tol (float, optional) tolerance value. Default: NULL symmetric (bool, optional) indicates whether input symmetric.                               Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"matrix-rank-input-tol-null-symmetric-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_rank(input, tol=NULL, symmetric=False) -> Tensor","title":"Matrix_rank — torch_matrix_rank","text":"Returns numerical rank 2-D tensor. method compute matrix rank done using SVD default. symmetric TRUE, input assumed symmetric, computation rank done obtaining eigenvalues. tol threshold singular values (eigenvalues symmetric TRUE) considered 0. tol specified, tol set S.max() * max(S.size()) * eps S singular values (eigenvalues symmetric TRUE), eps epsilon value datatype input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Max — torch_max","title":"Max — torch_max","text":"Max","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max — torch_max","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . Default: FALSE. (tuple, optional) result tuple two output tensors (max, max_indices) (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Max — torch_max","text":"shapes match, shape returned output tensor follows broadcasting rules .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"max(input) -> Tensor","title":"Max — torch_max","text":"Returns maximum value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Max — torch_max","text":"Returns namedtuple (values, indices) values maximum value row input tensor given dimension dim. indices index location maximum value found (argmax).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Max — torch_max","text":"indices necessarily contain first occurrence maximal value found, unless unique. exact implementation details device-specific. expect result run CPU GPU general. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"max(input, other, out=NULL) -> Tensor","title":"Max — torch_max","text":"element tensor input compared corresponding element tensor element-wise maximum taken. shapes input need match, must broadcastable . $$     \\mbox{}_i = \\max(\\mbox{tensor}_i, \\mbox{}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max — torch_max","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_max(a)   a = torch_randn(c(4, 4)) a torch_max(a, dim = 1)   a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_max(a, other = b) } #> torch_tensor #> -1.1791 #>  0.9259 #>  0.0413 #>  0.1109 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum — torch_maximum","title":"Maximum — torch_maximum","text":"Maximum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum — torch_maximum","text":"","code":"torch_maximum(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum — torch_maximum","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Maximum — torch_maximum","text":"one elements compared NaN, element returned. torch_maximum() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"maximum-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"maximum(input, other, *, out=None) -> Tensor","title":"Maximum — torch_maximum","text":"Computes element-wise maximum input .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum — torch_maximum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, -1)) b <- torch_tensor(c(3, 0, 4)) torch_maximum(a, b) } #> torch_tensor #>  3 #>  2 #>  4 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean — torch_mean","title":"Mean — torch_mean","text":"Mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean — torch_mean","text":"","code":"torch_mean(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean — torch_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype resulting data type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"mean-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"mean(input) -> Tensor","title":"Mean — torch_mean","text":"Returns mean value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"mean-input-dim-keepdim-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mean(input, dim, keepdim=False, out=NULL) -> Tensor","title":"Mean — torch_mean","text":"Returns mean value row input tensor given dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean — torch_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_mean(a)   a = torch_randn(c(4, 4)) a torch_mean(a, 1) torch_mean(a, 1, TRUE) } #> torch_tensor #> -1.0464  0.1152  0.6736 -0.3284 #> [ CPUFloatType{1,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Median — torch_median","title":"Median — torch_median","text":"Median","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Median — torch_median","text":"","code":"torch_median(self, dim, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Median — torch_median","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"median-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"median(input) -> Tensor","title":"Median — torch_median","text":"Returns median value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Median — torch_median","text":"Returns namedtuple (values, indices) values median value row input tensor given dimension dim. indices index location median value found. default, dim last dimension input tensor. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting outputs tensor 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Median — torch_median","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_median(a)   a = torch_randn(c(4, 5)) a torch_median(a, 1) } #> [[1]] #> torch_tensor #>  0.0628 #> -0.7662 #> -0.1937 #>  0.2784 #> -0.7597 #> [ CPUFloatType{5} ] #>  #> [[2]] #> torch_tensor #>  2 #>  2 #>  1 #>  0 #>  1 #> [ CPULongType{5} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_memory_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Memory format — torch_memory_format","title":"Memory format — torch_memory_format","text":"Returns correspondent memory format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_memory_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Memory format — torch_memory_format","text":"","code":"torch_contiguous_format()  torch_preserve_format()  torch_channels_last_format()"},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Meshgrid — torch_meshgrid","title":"Meshgrid — torch_meshgrid","text":"Take \\(N\\) tensors, can either scalar 1-dimensional vector, create \\(N\\) N-dimensional grids, \\(\\) th grid defined expanding \\(\\) th input dimensions defined inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meshgrid — torch_meshgrid","text":"","code":"torch_meshgrid(tensors, indexing)"},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meshgrid — torch_meshgrid","text":"tensors (list Tensor) list scalars 1 dimensional tensors. Scalars treated (1,). indexing (str, optional): indexing mode, either “xy” “ij”, defaults “ij”. See warning future changes. “xy” selected, first dimension corresponds cardinality second input second dimension corresponds cardinality first input. “ij” selected, dimensions order cardinality inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Meshgrid — torch_meshgrid","text":"future torch_meshgrid transition indexing=’xy’ default. issue tracks issue goal migrating NumPy’s behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Meshgrid — torch_meshgrid","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3)) y = torch_tensor(c(4, 5, 6)) out = torch_meshgrid(list(x, y)) out } #> [[1]] #> torch_tensor #>  1  1  1 #>  2  2  2 #>  3  3  3 #> [ CPUFloatType{3,3} ] #>  #> [[2]] #> torch_tensor #>  4  5  6 #>  4  5  6 #>  4  5  6 #> [ CPUFloatType{3,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Min — torch_min","title":"Min — torch_min","text":"Min","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Min — torch_min","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . (tuple, optional) tuple two output tensors (min, min_indices) (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Min — torch_min","text":"shapes match, shape returned output tensor follows broadcasting rules .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"min(input) -> Tensor","title":"Min — torch_min","text":"Returns minimum value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Min — torch_min","text":"Returns namedtuple (values, indices) values minimum value row input tensor given dimension dim. indices index location minimum value found (argmin).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Min — torch_min","text":"indices necessarily contain first occurrence minimal value found, unless unique. exact implementation details device-specific. expect result run CPU GPU general. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"min(input, other, out=NULL) -> Tensor","title":"Min — torch_min","text":"element tensor input compared corresponding element tensor element-wise minimum taken. resulting tensor returned. shapes input need match, must broadcastable . $$     \\mbox{}_i = \\min(\\mbox{tensor}_i, \\mbox{}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Min — torch_min","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_min(a)   a = torch_randn(c(4, 4)) a torch_min(a, dim = 1)   a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_min(a, other = b) } #> torch_tensor #> -0.3243 #>  0.2041 #> -1.7173 #>  0.4610 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum — torch_minimum","title":"Minimum — torch_minimum","text":"Minimum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum — torch_minimum","text":"","code":"torch_minimum(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum — torch_minimum","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Minimum — torch_minimum","text":"one elements compared NaN, element returned. torch_minimum() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"minimum-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"minimum(input, other, *, out=None) -> Tensor","title":"Minimum — torch_minimum","text":"Computes element-wise minimum input .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum — torch_minimum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, -1)) b <- torch_tensor(c(3, 0, 4)) torch_minimum(a, b) } #> torch_tensor #>  1 #>  0 #> -1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":null,"dir":"Reference","previous_headings":"","what":"Mm — torch_mm","title":"Mm — torch_mm","text":"Mm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mm — torch_mm","text":"","code":"torch_mm(self, mat2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mm — torch_mm","text":"self (Tensor) first matrix multiplied mat2 (Tensor) second matrix multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mm — torch_mm","text":"function broadcast . broadcasting matrix products, see torch_matmul.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"mm-input-mat-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mm(input, mat2, out=NULL) -> Tensor","title":"Mm — torch_mm","text":"Performs matrix multiplication matrices input mat2. input \\((n \\times m)\\) tensor, mat2 \\((m \\times p)\\) tensor, \\((n \\times p)\\) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mm — torch_mm","text":"","code":"if (torch_is_installed()) {  mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_mm(mat1, mat2) } #> torch_tensor #> -0.5435 -0.2961  1.1167 #>  0.8410 -0.8920  0.1800 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Mode — torch_mode","title":"Mode — torch_mode","text":"Mode","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mode — torch_mode","text":"","code":"torch_mode(self, dim = -1L, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mode — torch_mode","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mode — torch_mode","text":"function defined torch_cuda.Tensor yet.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Mode — torch_mode","text":"Returns namedtuple (values, indices) values mode value row input tensor given dimension dim, .e. value appears often row, indices index location mode value found. default, dim last dimension input tensor. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mode — torch_mode","text":"","code":"if (torch_is_installed()) {  a = torch_randint(0, 50, size = list(5)) a torch_mode(a, 1) } #> [[1]] #> torch_tensor #> 5 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 2 #> [ CPULongType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":null,"dir":"Reference","previous_headings":"","what":"Movedim — torch_movedim","title":"Movedim — torch_movedim","text":"Movedim","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Movedim — torch_movedim","text":"","code":"torch_movedim(self, source, destination)"},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Movedim — torch_movedim","text":"self (Tensor) input tensor. source (int tuple ints) Original positions dims move. must unique. destination (int tuple ints) Destination positions original dims. must also unique.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"movedim-input-source-destination-gt-tensor-","dir":"Reference","previous_headings":"","what":"movedim(input, source, destination) -> Tensor","title":"Movedim — torch_movedim","text":"Moves dimension(s) input position(s) source position(s) destination. dimensions input explicitly moved remain original order appear positions specified destination.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Movedim — torch_movedim","text":"","code":"if (torch_is_installed()) {  t <- torch_randn(c(3,2,1)) t torch_movedim(t, 2, 1)$shape torch_movedim(t, 2, 1) torch_movedim(t, c(2, 3), c(1, 2))$shape torch_movedim(t, c(2, 3), c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   1.4197 -0.3672 -1.5816 #>  #> (2,.,.) =  #>  -0.3044 -0.3522  1.1081 #> [ CPUFloatType{2,1,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":null,"dir":"Reference","previous_headings":"","what":"Mul — torch_mul","title":"Mul — torch_mul","text":"Mul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mul — torch_mul","text":"","code":"torch_mul(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mul — torch_mul","text":"self (Tensor) first multiplicand tensor (Tensor) second multiplicand tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"mul-input-other-out-null-","dir":"Reference","previous_headings":"","what":"mul(input, other, out=NULL)","title":"Mul — torch_mul","text":"Multiplies element input input scalar returns new resulting tensor. $$     \\mbox{}_i = \\mbox{} \\times \\mbox{input}_i $$ input type FloatTensor DoubleTensor, real number, otherwise integer element tensor input multiplied corresponding element Tensor . resulting tensor returned. shapes input must broadcastable . $$     \\mbox{}_i = \\mbox{input}_i \\times \\mbox{}_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mul — torch_mul","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3)) a torch_mul(a, 100)   a = torch_randn(c(4, 1)) a b = torch_randn(c(1, 4)) b torch_mul(a, b) } #> torch_tensor #>  1.1375 -1.3661  2.4762 -0.3392 #>  0.1289 -0.1549  0.2807 -0.0385 #>  0.1674 -0.2011  0.3645 -0.0499 #>  0.0937 -0.1126  0.2040 -0.0280 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Multinomial — torch_multinomial","title":"Multinomial — torch_multinomial","text":"Multinomial","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multinomial — torch_multinomial","text":"","code":"torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multinomial — torch_multinomial","text":"self (Tensor) input tensor containing probabilities num_samples (int) number samples draw replacement (bool, optional) whether draw replacement generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multinomial — torch_multinomial","text":"Indices ordered left right according sampled (first samples placed first column). input vector, vector size num_samples. input matrix m rows, matrix shape \\((m \\times \\mbox{num\\_samples})\\). replacement TRUE, samples drawn replacement. , drawn without replacement, means sample index drawn row, drawn row.","code":"The rows of `input` do not need to sum to one (in which case we use the values as weights), but must be non-negative, finite and have a non-zero sum. When drawn without replacement, `num_samples` must be lower than number of non-zero elements in `input` (or the min number of non-zero elements in each row of `input` if it is a matrix)."},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-","dir":"Reference","previous_headings":"","what":"multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor","title":"Multinomial — torch_multinomial","text":"Returns tensor row contains num_samples indices sampled multinomial probability distribution located corresponding row tensor input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multinomial — torch_multinomial","text":"","code":"if (torch_is_installed()) {  weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights torch_multinomial(weights, 2) torch_multinomial(weights, 4, replacement=TRUE) } #> torch_tensor #>  2 #>  2 #>  2 #>  2 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply — torch_multiply","title":"Multiply — torch_multiply","text":"Multiply","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply — torch_multiply","text":"","code":"torch_multiply(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply — torch_multiply","text":"self (Tensor) first multiplicand tensor (Tensor) second multiplicand tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"multiply-input-other-out-none-","dir":"Reference","previous_headings":"","what":"multiply(input, other, *, out=None)","title":"Multiply — torch_multiply","text":"Alias torch_mul().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":null,"dir":"Reference","previous_headings":"","what":"Mv — torch_mv","title":"Mv — torch_mv","text":"Mv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mv — torch_mv","text":"","code":"torch_mv(self, vec)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mv — torch_mv","text":"self (Tensor) matrix multiplied vec (Tensor) vector multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mv — torch_mv","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"mv-input-vec-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mv(input, vec, out=NULL) -> Tensor","title":"Mv — torch_mv","text":"Performs matrix-vector product matrix input vector vec. input \\((n \\times m)\\) tensor, vec 1-D tensor size \\(m\\), 1-D size \\(n\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mv — torch_mv","text":"","code":"if (torch_is_installed()) {  mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_mv(mat, vec) } #> torch_tensor #> -0.5089 #>  2.4542 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Mvlgamma — torch_mvlgamma","title":"Mvlgamma — torch_mvlgamma","text":"Mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mvlgamma — torch_mvlgamma","text":"","code":"torch_mvlgamma(self, p)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mvlgamma — torch_mvlgamma","text":"self (Tensor) tensor compute multivariate log-gamma function p (int) number dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"mvlgamma-input-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"mvlgamma(input, p) -> Tensor","title":"Mvlgamma — torch_mvlgamma","text":"Computes multivariate log-gamma function <https://en.wikipedia.org/wiki/Multivariate_gamma_function>_) dimension \\(p\\) element-wise, given $$     \\log(\\Gamma_{p}()) = C + \\displaystyle \\sum_{=1}^{p} \\log\\left(\\Gamma\\left(- \\frac{- 1}{2}\\right)\\right) $$ \\(C = \\log(\\pi) \\times \\frac{p (p - 1)}{4}\\) \\(\\Gamma(\\cdot)\\) Gamma function. elements must greater \\(\\frac{p - 1}{2}\\), otherwise error thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mvlgamma — torch_mvlgamma","text":"","code":"if (torch_is_installed()) {  a = torch_empty(c(2, 3))$uniform_(1, 2) a torch_mvlgamma(a, 2) } #> torch_tensor #>  0.3956  0.4255  0.3985 #>  0.4307  0.3940  0.4207 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Nanquantile — torch_nanquantile","title":"Nanquantile — torch_nanquantile","text":"Nanquantile","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nanquantile — torch_nanquantile","text":"","code":"torch_nanquantile(   self,   q,   dim = NULL,   keepdim = FALSE,   interpolation = \"linear\" )"},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nanquantile — torch_nanquantile","text":"self (Tensor) input tensor. q (float Tensor) scalar 1D tensor quantile values range [0, 1] dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . interpolation interpolation method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor","title":"Nanquantile — torch_nanquantile","text":"variant torch_quantile() \"ignores\" NaN values, computing quantiles q NaN values input exist. values reduced row NaN quantiles reduction NaN. See documentation torch_quantile().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nanquantile — torch_nanquantile","text":"","code":"if (torch_is_installed()) {  t <- torch_tensor(c(NaN, 1, 2)) t$quantile(0.5) t$nanquantile(0.5) t <- torch_tensor(rbind(c(NaN, NaN), c(1, 2))) t t$nanquantile(0.5, dim=1) t$nanquantile(0.5, dim=2) torch_nanquantile(t, 0.5, dim = 1) torch_nanquantile(t, 0.5, dim = 2) } #> torch_tensor #>     nan  1.5000 #> [ CPUFloatType{1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":null,"dir":"Reference","previous_headings":"","what":"Nansum — torch_nansum","title":"Nansum — torch_nansum","text":"Nansum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nansum — torch_nansum","text":"","code":"torch_nansum(self, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nansum — torch_nansum","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype desired data type returned tensor. specified, input tensor casted dtype operation performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"nansum-input-dtype-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nansum(input, *, dtype=None) -> Tensor","title":"Nansum — torch_nansum","text":"Returns sum elements, treating Numbers (NaNs) zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"nansum-input-dim-keepdim-false-dtype-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nansum(input, dim, keepdim=FALSE, *, dtype=None) -> Tensor","title":"Nansum — torch_nansum","text":"Returns sum row input tensor given dimension dim, treating Numbers (NaNs) zero. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nansum — torch_nansum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1., 2., NaN, 4.)) torch_nansum(a)   torch_nansum(torch_tensor(c(1., NaN))) a <- torch_tensor(rbind(c(1, 2), c(3., NaN))) torch_nansum(a) torch_nansum(a, dim=1) torch_nansum(a, dim=2) } #> torch_tensor #>  3 #>  3 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":null,"dir":"Reference","previous_headings":"","what":"Narrow — torch_narrow","title":"Narrow — torch_narrow","text":"Narrow","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Narrow — torch_narrow","text":"","code":"torch_narrow(self, dim, start, length)"},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Narrow — torch_narrow","text":"self (Tensor) tensor narrow dim (int) dimension along narrow start (int) starting dimension length (int) distance ending dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"narrow-input-dim-start-length-gt-tensor-","dir":"Reference","previous_headings":"","what":"narrow(input, dim, start, length) -> Tensor","title":"Narrow — torch_narrow","text":"Returns new tensor narrowed version input tensor. dimension dim input start start + length. returned tensor input tensor share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Narrow — torch_narrow","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE)) torch_narrow(x, 1, 1, 2) torch_narrow(x, 2, 2, 2) } #> torch_tensor #>  2  3 #>  5  6 #>  8  9 #> [ CPULongType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":null,"dir":"Reference","previous_headings":"","what":"Ne — torch_ne","title":"Ne — torch_ne","text":"Ne","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ne — torch_ne","text":"","code":"torch_ne(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ne — torch_ne","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ne-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ne(input, other, out=NULL) -> Tensor","title":"Ne — torch_ne","text":"Computes \\(input \\neq \\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ne — torch_ne","text":"","code":"if (torch_is_installed()) {  torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  1 #>  1  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":null,"dir":"Reference","previous_headings":"","what":"Neg — torch_neg","title":"Neg — torch_neg","text":"Neg","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Neg — torch_neg","text":"","code":"torch_neg(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Neg — torch_neg","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"neg-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"neg(input, out=NULL) -> Tensor","title":"Neg — torch_neg","text":"Returns new tensor negative elements input. $$     \\mbox{} = -1 \\times \\mbox{input} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Neg — torch_neg","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_neg(a) } #> torch_tensor #> -0.3203 #> -1.2317 #>  1.3018 #> -0.2356 #>  0.0700 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Negative — torch_negative","title":"Negative — torch_negative","text":"Negative","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negative — torch_negative","text":"","code":"torch_negative(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negative — torch_negative","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"negative-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"negative(input, *, out=None) -> Tensor","title":"Negative — torch_negative","text":"Alias torch_neg()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":null,"dir":"Reference","previous_headings":"","what":"Nextafter — torch_nextafter","title":"Nextafter — torch_nextafter","text":"Nextafter","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nextafter — torch_nextafter","text":"","code":"torch_nextafter(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nextafter — torch_nextafter","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"nextafter-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nextafter(input, other, *, out=None) -> Tensor","title":"Nextafter — torch_nextafter","text":"Return next floating-point value input towards , elementwise. shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nextafter — torch_nextafter","text":"","code":"if (torch_is_installed()) {  eps <- torch_finfo(torch_float32())$eps torch_nextafter(torch_tensor(c(1, 2)), torch_tensor(c(2, 1))) == torch_tensor(c(eps + 1, 2 - eps)) } #> torch_tensor #>  1 #>  1 #> [ CPUBoolType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonzero — torch_nonzero","title":"Nonzero — torch_nonzero","text":"Nonzero elements tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nonzero — torch_nonzero","text":"","code":"torch_nonzero(self, as_list = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nonzero — torch_nonzero","text":"self (Tensor) input tensor. as_list FALSE, output tensor containing indices. TRUE, one 1-D tensor dimension, containing indices nonzero element along dimension. as_list FALSE (default): Returns tensor containing indices non-zero elements input.  row result contains indices non-zero element input. result sorted lexicographically, last index changing fastest (C-style). input \\(n\\) dimensions, resulting indices tensor size \\((z \\times n)\\), \\(z\\) total number non-zero elements input tensor. as_list TRUE: Returns tuple 1-D tensors, one dimension input, containing indices (dimension) non-zero elements input . input \\(n\\) dimensions, resulting tuple contains \\(n\\) tensors size \\(z\\), \\(z\\) total number non-zero elements input tensor. special case, input zero dimensions nonzero scalar value, treated one-dimensional tensor one element.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonzero — torch_nonzero","text":"","code":"if (torch_is_installed()) {  torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1))) } #> torch_tensor #>  1 #>  2 #>  3 #>  5 #> [ CPULongType{4,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Norm — torch_norm","title":"Norm — torch_norm","text":"Norm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Norm — torch_norm","text":"","code":"torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Norm — torch_norm","text":"self (Tensor) input tensor p (int, float, inf, -inf, 'fro', 'nuc', optional) order norm. Default: 'fro'        following norms can calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --         vec norm dim NULL  sum(abs(x)ord)(1./ord)        =====  ============================  ========================== dim (int, 2-tuple ints, 2-list ints, optional) int,        vector norm calculated, 2-tuple ints, matrix norm        calculated. value NULL, matrix norm calculated        input tensor two dimensions, vector norm        calculated input tensor one dimension. input        tensor two dimensions, vector norm applied        last dimension. keepdim (bool, optional) whether output tensors dim        retained . Ignored dim = NULL        = NULL. Default: FALSE Ignored        dim = NULL = NULL. dtype (torch.dtype, optional) desired data type        returned tensor. specified, input tensor casted        'dtype' performing operation. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Norm — torch_norm","text":"Returns matrix norm vector norm given tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Norm — torch_norm","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(1, 9, dtype = torch_float()) b <- a$reshape(list(3, 3)) torch_norm(a) torch_norm(b) torch_norm(a, Inf) torch_norm(b, Inf)  } #> torch_tensor #> 9 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Normal — torch_normal","title":"Normal — torch_normal","text":"Normal Normal distributed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normal — torch_normal","text":"","code":"torch_normal(mean, std, size = NULL, generator = NULL, ...)"},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normal — torch_normal","text":"mean (tensor scalar double) Mean normal distribution. torch_tensor() output dim mean represents per-element mean. scalar value, reused elements. std (tensor scalar double) standard deviation normal distribution. torch_tensor() output size std represents per-element standard deviation. scalar value, reused elements. size (integers, optional) used mean std scalars. generator random number generator created torch_generator(). NULL default generator used. ... Tensor option parameters like dtype, layout, device. Can used mean std scalar numerics.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Normal — torch_normal","text":"shapes match, shape mean used shape returned output tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor-","dir":"Reference","previous_headings":"","what":"normal(mean, std, *) -> Tensor","title":"Normal — torch_normal","text":"Returns tensor random numbers drawn separate normal distributions whose mean standard deviation given. mean tensor mean output element's normal distribution std tensor standard deviation output element's normal distribution shapes mean std need match, total number elements tensor need .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor--1","dir":"Reference","previous_headings":"","what":"normal(mean=0.0, std) -> Tensor","title":"Normal — torch_normal","text":"Similar function , means shared among drawn elements.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor--2","dir":"Reference","previous_headings":"","what":"normal(mean, std=1.0) -> Tensor","title":"Normal — torch_normal","text":"Similar function , standard-deviations shared among drawn elements.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-size-gt-tensor-","dir":"Reference","previous_headings":"","what":"normal(mean, std, size, *) -> Tensor","title":"Normal — torch_normal","text":"Similar function , means standard deviations shared among drawn elements. resulting tensor size given size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normal — torch_normal","text":"","code":"if (torch_is_installed()) {  torch_normal(mean=0, std=torch_arange(1, 0, -0.1) + 1e-6) torch_normal(mean=0.5, std=torch_arange(1., 6.)) torch_normal(mean=torch_arange(1., 6.)) torch_normal(2, 3, size=c(1, 4))  } #> torch_tensor #>  0.0455  2.4500  3.9270  5.6783 #> [ CPUFloatType{1,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Not_equal — torch_not_equal","title":"Not_equal — torch_not_equal","text":"Not_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Not_equal — torch_not_equal","text":"","code":"torch_not_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Not_equal — torch_not_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"not-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"not_equal(input, other, *, out=None) -> Tensor","title":"Not_equal — torch_not_equal","text":"Alias torch_ne().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones — torch_ones","title":"Ones — torch_ones","text":"Ones","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones — torch_ones","text":"","code":"torch_ones(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones — torch_ones","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Ones — torch_ones","text":"Returns tensor filled scalar value 1, shape defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones — torch_ones","text":"","code":"if (torch_is_installed()) {  torch_ones(c(2, 3)) torch_ones(c(5)) } #> torch_tensor #>  1 #>  1 #>  1 #>  1 #>  1 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones_like — torch_ones_like","title":"Ones_like — torch_ones_like","text":"Ones_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones_like — torch_ones_like","text":"","code":"torch_ones_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones_like — torch_ones_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Ones_like — torch_ones_like","text":"Returns tensor filled scalar value 1, size input. torch_ones_like(input) equivalent torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Ones_like — torch_ones_like","text":"0.4, function support keyword. alternative, old torch_ones_like(input, =output) equivalent torch_ones(input.size(), =output).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones_like — torch_ones_like","text":"","code":"if (torch_is_installed()) {  input = torch_empty(c(2, 3)) torch_ones_like(input) } #> torch_tensor #>  1  1  1 #>  1  1  1 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":null,"dir":"Reference","previous_headings":"","what":"Orgqr — torch_orgqr","title":"Orgqr — torch_orgqr","text":"Orgqr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orgqr — torch_orgqr","text":"","code":"torch_orgqr(self, input2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orgqr — torch_orgqr","text":"self (Tensor) torch_geqrf. input2 (Tensor) tau torch_geqrf.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"orgqr-input-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"orgqr(input, input2) -> Tensor","title":"Orgqr — torch_orgqr","text":"Computes orthogonal matrix Q QR factorization, (input, input2) tuple returned torch_geqrf. directly calls underlying LAPACK function ?orgqr. See LAPACK documentation orgqr_ details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":null,"dir":"Reference","previous_headings":"","what":"Ormqr — torch_ormqr","title":"Ormqr — torch_ormqr","text":"Ormqr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ormqr — torch_ormqr","text":"","code":"torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ormqr — torch_ormqr","text":"self (Tensor) torch_geqrf. input2 (Tensor) tau torch_geqrf. input3 (Tensor) matrix multiplied. left see LAPACK documentation transpose see LAPACK documentation","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"ormqr-input-input-input-left-true-transpose-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor","title":"Ormqr — torch_ormqr","text":"Multiplies mat (given input3) orthogonal Q matrix QR factorization formed torch_geqrf() represented (, tau) (given (input, input2)). directly calls underlying LAPACK function ?ormqr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Outer — torch_outer","title":"Outer — torch_outer","text":"Outer","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outer — torch_outer","text":"","code":"torch_outer(self, vec2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outer — torch_outer","text":"self (Tensor) 1-D input vector vec2 (Tensor) 1-D input vector","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outer — torch_outer","text":"function broadcast.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"outer-input-vec-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"outer(input, vec2, *, out=None) -> Tensor","title":"Outer — torch_outer","text":"Outer product input vec2. input vector size \\(n\\) vec2 vector size \\(m\\), must matrix size \\((n \\times m)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outer — torch_outer","text":"","code":"if (torch_is_installed()) {  v1 <- torch_arange(1., 5.) v2 <- torch_arange(1., 4.) torch_outer(v1, v2) } #> torch_tensor #>   1   2   3   4 #>   2   4   6   8 #>   3   6   9  12 #>   4   8  12  16 #>   5  10  15  20 #> [ CPUFloatType{5,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Pdist — torch_pdist","title":"Pdist — torch_pdist","text":"Pdist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pdist — torch_pdist","text":"","code":"torch_pdist(self, p = 2L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pdist — torch_pdist","text":"self NA input tensor shape \\(N \\times M\\). p NA p value p-norm distance calculate vector pair        \\(\\[0, \\infty]\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"pdist-input-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"pdist(input, p=2) -> Tensor","title":"Pdist — torch_pdist","text":"Computes p-norm distance every pair row vectors input. identical upper triangular portion, excluding diagonal, torch_norm(input[:, NULL] - input, dim=2, p=p). function faster rows contiguous. input shape \\(N \\times M\\) output shape \\(\\frac{1}{2} N (N - 1)\\). function equivalent scipy.spatial.distance.pdist(input, 'minkowski', p=p) \\(p \\(0, \\infty)\\). \\(p = 0\\) equivalent scipy.spatial.distance.pdist(input, 'hamming') * M. \\(p = \\infty\\), closest scipy function scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max()).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Pinverse — torch_pinverse","title":"Pinverse — torch_pinverse","text":"Pinverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pinverse — torch_pinverse","text":"","code":"torch_pinverse(self, rcond = 1e-15)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pinverse — torch_pinverse","text":"self (Tensor) input tensor size \\((*, m, n)\\) \\(*\\) zero batch dimensions rcond (float) floating point value determine cutoff small singular values.                   Default: 1e-15","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pinverse — torch_pinverse","text":"","code":"This method is implemented using the Singular Value Decomposition. The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_. Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_. However, this method is backprop-able due to the implementation by using SVD results, and could be unstable. Double-backward will also be unstable due to the usage of SVD internally. See `~torch.svd` for more details."},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"pinverse-input-rcond-e-gt-tensor-","dir":"Reference","previous_headings":"","what":"pinverse(input, rcond=1e-15) -> Tensor","title":"Pinverse — torch_pinverse","text":"Calculates pseudo-inverse (also known Moore-Penrose inverse) 2D tensor. Please look Moore-Penrose inverse_ details","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pinverse — torch_pinverse","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(3, 5)) input torch_pinverse(input) # Batched pinverse example a = torch_randn(c(2,6,3)) b = torch_pinverse(a) torch_matmul(b, a) } #> torch_tensor #> (1,.,.) =  #>   1.0000e+00 -4.2841e-08 -8.9407e-08 #>  -8.3819e-09  1.0000e+00 -1.3411e-07 #>  -1.0245e-07 -9.6858e-08  1.0000e+00 #>  #> (2,.,.) =  #>   1.0000e+00 -3.4459e-07  9.6858e-08 #>  -1.7881e-07  1.0000e+00 -2.4587e-07 #>   1.4901e-07  1.7043e-07  1.0000e+00 #> [ CPUFloatType{2,3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Pixel_shuffle — torch_pixel_shuffle","title":"Pixel_shuffle — torch_pixel_shuffle","text":"Pixel_shuffle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pixel_shuffle — torch_pixel_shuffle","text":"","code":"torch_pixel_shuffle(self, upscale_factor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pixel_shuffle — torch_pixel_shuffle","text":"self (Tensor) input tensor upscale_factor (int) factor increase spatial resolution ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"rearranges-elements-in-a-tensor-of-shape-","dir":"Reference","previous_headings":"","what":"Rearranges elements in a tensor of shape","title":"Pixel_shuffle — torch_pixel_shuffle","text":"math:(*, C \\times r^2, H, W) : Rearranges elements tensor shape \\((*, C \\times r^2, H, W)\\) tensor shape \\((*, C, H \\times r, W \\times r)\\). See ~torch.nn.PixelShuffle details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pixel_shuffle — torch_pixel_shuffle","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(1, 9, 4, 4)) output = nnf_pixel_shuffle(input, 3) print(output$size()) } #> [1]  1  1 12 12"},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson — torch_poisson","title":"Poisson — torch_poisson","text":"Poisson","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson — torch_poisson","text":"","code":"torch_poisson(self, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson — torch_poisson","text":"self (Tensor) input tensor containing rates Poisson distribution generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"poisson-input-generator-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"poisson(input *, generator=NULL) -> Tensor","title":"Poisson — torch_poisson","text":"Returns tensor size input element sampled Poisson distribution rate parameter given corresponding element input .e., $$     \\mbox{}_i \\sim \\mbox{Poisson}(\\mbox{input}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Poisson — torch_poisson","text":"","code":"if (torch_is_installed()) {  rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5 torch_poisson(rates) } #> torch_tensor #>  3  0  2  2 #>  7  0  3  3 #>  3  4  4  0 #>  2  5  2  4 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":null,"dir":"Reference","previous_headings":"","what":"Polar — torch_polar","title":"Polar — torch_polar","text":"Polar","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polar — torch_polar","text":"","code":"torch_polar(abs, angle)"},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polar — torch_polar","text":"abs (Tensor) absolute value complex tensor. Must float double. angle (Tensor) angle complex tensor. Must dtype abs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"polar-abs-angle-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"polar(abs, angle, *, out=None) -> Tensor","title":"Polar — torch_polar","text":"Constructs complex tensor whose elements Cartesian coordinates corresponding polar coordinates absolute value abs angle angle. $$     \\mbox{} = \\mbox{abs} \\cdot \\cos(\\mbox{angle}) + \\mbox{abs} \\cdot \\sin(\\mbox{angle}) \\cdot j $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polar — torch_polar","text":"","code":"if (torch_is_installed()) {  abs <- torch_tensor(c(1, 2), dtype=torch_float64()) angle <- torch_tensor(c(pi / 2, 5 * pi / 4), dtype=torch_float64()) z <- torch_polar(abs, angle) z } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexDoubleType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygamma — torch_polygamma","title":"Polygamma — torch_polygamma","text":"Polygamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygamma — torch_polygamma","text":"","code":"torch_polygamma(n, input)"},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polygamma — torch_polygamma","text":"n (int) order polygamma function input (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Polygamma — torch_polygamma","text":"","code":"This function is not implemented for \\eqn{n \\geq 2}."},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"polygamma-n-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"polygamma(n, input, out=NULL) -> Tensor","title":"Polygamma — torch_polygamma","text":"Computes \\(n^{th}\\) derivative digamma function input. \\(n \\geq 0\\) called order polygamma function. $$     \\psi^{(n)}(x) = \\frac{d^{(n)}}{dx^{(n)}} \\psi(x) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polygamma — torch_polygamma","text":"","code":"if (torch_is_installed()) { if (FALSE) { a = torch_tensor(c(1, 0.5)) torch_polygamma(1, a) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":null,"dir":"Reference","previous_headings":"","what":"Pow — torch_pow","title":"Pow — torch_pow","text":"Pow","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pow — torch_pow","text":"","code":"torch_pow(self, exponent)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pow — torch_pow","text":"self (float) scalar base value power operation exponent (float tensor) exponent value","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"pow-input-exponent-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"pow(input, exponent, out=NULL) -> Tensor","title":"Pow — torch_pow","text":"Takes power element input exponent returns tensor result. exponent can either single float number Tensor number elements input. exponent scalar value, operation applied : $$     \\mbox{}_i = x_i^{\\mbox{exponent}} $$ exponent tensor, operation applied : $$     \\mbox{}_i = x_i^{\\mbox{exponent}_i} $$ exponent tensor, shapes input exponent must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"pow-self-exponent-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"pow(self, exponent, out=NULL) -> Tensor","title":"Pow — torch_pow","text":"self scalar float value, exponent tensor. returned tensor shape exponent operation applied : $$     \\mbox{}_i = \\mbox{self} ^ {\\mbox{exponent}_i} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pow — torch_pow","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_pow(a, 2) exp <- torch_arange(1, 5) a <- torch_arange(1, 5) a exp torch_pow(a, exp)   exp <- torch_arange(1, 5) base <- 2 torch_pow(base, exp) } #> torch_tensor #>   2 #>   4 #>   8 #>  16 #>  32 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Prod — torch_prod","title":"Prod — torch_prod","text":"Prod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prod — torch_prod","text":"","code":"torch_prod(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prod — torch_prod","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"prod-input-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"prod(input, dtype=NULL) -> Tensor","title":"Prod — torch_prod","text":"Returns product elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"prod-input-dim-keepdim-false-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"prod(input, dim, keepdim=False, dtype=NULL) -> Tensor","title":"Prod — torch_prod","text":"Returns product row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prod — torch_prod","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_prod(a)   a = torch_randn(c(4, 2)) a torch_prod(a, 1) } #> torch_tensor #>  1.0789 #>  0.6899 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":null,"dir":"Reference","previous_headings":"","what":"Promote_types — torch_promote_types","title":"Promote_types — torch_promote_types","text":"Promote_types","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Promote_types — torch_promote_types","text":"","code":"torch_promote_types(type1, type2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Promote_types — torch_promote_types","text":"type1 (torch.dtype) type2 (torch.dtype)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"promote-types-type-type-gt-dtype-","dir":"Reference","previous_headings":"","what":"promote_types(type1, type2) -> dtype","title":"Promote_types — torch_promote_types","text":"Returns torch_dtype smallest size scalar kind smaller lower kind either type1 type2. See type promotion documentation  information type promotion logic.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Promote_types — torch_promote_types","text":"","code":"if (torch_is_installed()) {  torch_promote_types(torch_int32(), torch_float32()) torch_promote_types(torch_uint8(), torch_long()) } #> torch_Long"},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Qr — torch_qr","title":"Qr — torch_qr","text":"Qr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Qr — torch_qr","text":"","code":"torch_qr(self, some = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Qr — torch_qr","text":"self (Tensor) input tensor size \\((*, m, n)\\) * zero                batch dimensions consisting matrices dimension \\(m \\times n\\). (bool, optional) Set TRUE reduced QR decomposition FALSE                complete QR decomposition.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Qr — torch_qr","text":"precision may lost magnitudes elements input large always give valid decomposition, may give one across platforms - depend LAPACK implementation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"qr-input-some-true-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor)","title":"Qr — torch_qr","text":"Computes QR decomposition matrix batch matrices input, returns namedtuple (Q, R) tensors \\(\\mbox{input} = Q R\\) \\(Q\\) orthogonal matrix batch orthogonal matrices \\(R\\) upper triangular matrix batch upper triangular matrices. TRUE, function returns thin (reduced) QR factorization. Otherwise, FALSE, function returns complete QR factorization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Qr — torch_qr","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE)) out = torch_qr(a) q = out[[1]] r = out[[2]] torch_mm(q, r)$round() torch_mm(q$t(), q)$round() } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_qscheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the corresponding Scheme object — torch_qscheme","title":"Creates the corresponding Scheme object — torch_qscheme","text":"Creates corresponding Scheme object","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qscheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the corresponding Scheme object — torch_qscheme","text":"","code":"torch_per_channel_affine()  torch_per_tensor_affine()  torch_per_channel_symmetric()  torch_per_tensor_symmetric()"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile — torch_quantile","title":"Quantile — torch_quantile","text":"Quantile","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile — torch_quantile","text":"","code":"torch_quantile(self, q, dim = NULL, keepdim = FALSE, interpolation = \"linear\")"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile — torch_quantile","text":"self (Tensor) input tensor. q (float Tensor) scalar 1D tensor quantile values range [0, 1] dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . interpolation interpolation method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"quantile-input-q-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantile(input, q) -> Tensor","title":"Quantile — torch_quantile","text":"Returns q-th quantiles elements input tensor, linear interpolation q-th quantile lies two data points.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor","title":"Quantile — torch_quantile","text":"Returns q-th quantiles row input tensor along dimension dim, linear interpolation q-th quantile lies two data points. default, dim None resulting input tensor flattened computation. keepdim TRUE, output dimensions size input except dimensions reduced (dim dim NULL) size 1. Otherwise, dimensions reduced squeezed (see torch_squeeze). q 1D tensor, extra dimension prepended output tensor size q represents quantiles.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile — torch_quantile","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(1, 3)) a q <- torch_tensor(c(0, 0.5, 1)) torch_quantile(a, q)   a <- torch_randn(c(2, 3)) a q <- torch_tensor(c(0.25, 0.5, 0.75)) torch_quantile(a, q, dim=1, keepdim=TRUE) torch_quantile(a, q, dim=1, keepdim=TRUE)$shape } #> [1] 3 1 3"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize_per_channel — torch_quantize_per_channel","title":"Quantize_per_channel — torch_quantize_per_channel","text":"Quantize_per_channel","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize_per_channel — torch_quantize_per_channel","text":"","code":"torch_quantize_per_channel(self, scales, zero_points, axis, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize_per_channel — torch_quantize_per_channel","text":"self (Tensor) float tensor quantize scales (Tensor) float 1D tensor scales use, size match input.size(axis) zero_points (int) integer 1D tensor offset use, size match input.size(axis) axis (int) dimension apply per-channel quantization dtype (torch.dtype) desired data type returned tensor.        one quantized dtypes: torch_quint8, torch.qint8, torch.qint32","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor","title":"Quantize_per_channel — torch_quantize_per_channel","text":"Converts float tensor per-channel quantized tensor given scales zero points.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantize_per_channel — torch_quantize_per_channel","text":"","code":"if (torch_is_installed()) { x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE)) torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),                             torch_tensor(c(10L, 0L)), 0, torch_quint8()) torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),                             torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr() } #> torch_tensor #>    0   10 #>  100  200 #> [ CPUByteType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize_per_tensor — torch_quantize_per_tensor","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"Quantize_per_tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"","code":"torch_quantize_per_tensor(self, scale, zero_point, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"self (Tensor) float tensor quantize scale (float) scale apply quantization formula zero_point (int) offset integer value maps float zero dtype (torch.dtype) desired data type returned tensor.        one quantized dtypes: torch_quint8, torch.qint8, torch.qint32","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"Converts float tensor quantized tensor given scale zero point.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"","code":"if (torch_is_installed()) { torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8()) torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr() } #> torch_tensor #>   0 #>  10 #>  20 #>  30 #> [ CPUByteType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":null,"dir":"Reference","previous_headings":"","what":"Rad2deg — torch_rad2deg","title":"Rad2deg — torch_rad2deg","text":"Rad2deg","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rad2deg — torch_rad2deg","text":"","code":"torch_rad2deg(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rad2deg — torch_rad2deg","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"rad-deg-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"rad2deg(input, *, out=None) -> Tensor","title":"Rad2deg — torch_rad2deg","text":"Returns new tensor elements input converted angles radians degrees.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rad2deg — torch_rad2deg","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(rbind(c(3.142, -3.142), c(6.283, -6.283), c(1.570, -1.570))) torch_rad2deg(a) } #> torch_tensor #>  180.0233 -180.0233 #>  359.9894 -359.9894 #>   89.9544  -89.9544 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":null,"dir":"Reference","previous_headings":"","what":"Rand — torch_rand","title":"Rand — torch_rand","text":"Rand","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rand — torch_rand","text":"","code":"torch_rand(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rand — torch_rand","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional dimension names dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Rand — torch_rand","text":"Returns tensor filled random numbers uniform distribution interval \\([0, 1)\\) shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rand — torch_rand","text":"","code":"if (torch_is_installed()) {  torch_rand(4) torch_rand(c(2, 3)) } #> torch_tensor #>  0.8802  0.0500  0.8957 #>  0.2329  0.6650  0.5580 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Rand_like — torch_rand_like","title":"Rand_like — torch_rand_like","text":"Rand_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rand_like — torch_rand_like","text":"","code":"torch_rand_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rand_like — torch_rand_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Rand_like — torch_rand_like","text":"Returns tensor size input filled random numbers uniform distribution interval \\([0, 1)\\). torch_rand_like(input) equivalent torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":null,"dir":"Reference","previous_headings":"","what":"Randint — torch_randint","title":"Randint — torch_randint","text":"Randint","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randint — torch_randint","text":"","code":"torch_randint(   low,   high,   size,   generator = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randint — torch_randint","text":"low (int, optional) Lowest integer drawn distribution. Default: 0. high (int) One highest integer drawn distribution. size (tuple) tuple defining shape output tensor. generator (torch.Generator, optional) pseudorandom number generator sampling dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format memory format resulting tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"randint-low-high-size-generator-null-out-null-","dir":"Reference","previous_headings":"","what":"randint(low=0, high, size, *, generator=NULL, out=NULL, \\","title":"Randint — torch_randint","text":"dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor Returns tensor filled random integers generated uniformly low (inclusive) high (exclusive). shape tensor defined variable argument size. .. note: global dtype default (torch_float32), function returns tensor dtype torch_int64.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randint — torch_randint","text":"","code":"if (torch_is_installed()) {  torch_randint(3, 5, list(3)) torch_randint(0, 10, size = list(2, 2)) torch_randint(3, 10, list(2, 2)) } #> torch_tensor #>  6  8 #>  5  7 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Randint_like — torch_randint_like","title":"Randint_like — torch_randint_like","text":"Randint_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randint_like — torch_randint_like","text":"","code":"torch_randint_like(   input,   low,   high,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randint_like — torch_randint_like","text":"input (Tensor) size input determine size output tensor. low (int, optional) Lowest integer drawn distribution. Default: 0. high (int) One highest integer drawn distribution. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-","dir":"Reference","previous_headings":"","what":"randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,","title":"Randint_like — torch_randint_like","text":"memory_format=torch.preserve_format) -> Tensor Returns tensor shape Tensor input filled random integers generated uniformly low (inclusive) high (exclusive). .. note: global dtype default (torch_float32), function returns tensor dtype torch_int64.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":null,"dir":"Reference","previous_headings":"","what":"Randn — torch_randn","title":"Randn — torch_randn","text":"Randn","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randn — torch_randn","text":"","code":"torch_randn(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randn — torch_randn","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Randn — torch_randn","text":"Returns tensor filled random numbers normal distribution mean 0 variance 1 (also called standard normal distribution). $$     \\mbox{}_{} \\sim \\mathcal{N}(0, 1) $$ shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randn — torch_randn","text":"","code":"if (torch_is_installed()) {  torch_randn(c(4)) torch_randn(c(2, 3)) } #> torch_tensor #> -0.8321  0.3774 -1.2515 #>  1.1037  2.6689  0.0240 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Randn_like — torch_randn_like","title":"Randn_like — torch_randn_like","text":"Randn_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randn_like — torch_randn_like","text":"","code":"torch_randn_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randn_like — torch_randn_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Randn_like — torch_randn_like","text":"Returns tensor size input filled random numbers normal distribution mean 0 variance 1. torch_randn_like(input) equivalent torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":null,"dir":"Reference","previous_headings":"","what":"Randperm — torch_randperm","title":"Randperm — torch_randperm","text":"Randperm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randperm — torch_randperm","text":"","code":"torch_randperm(   n,   dtype = torch_int64(),   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randperm — torch_randperm","text":"n (int) upper bound (exclusive) dtype (torch.dtype, optional) desired data type returned tensor.        Default: torch_int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor","title":"Randperm — torch_randperm","text":"Returns random permutation integers 0 n - 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randperm — torch_randperm","text":"","code":"if (torch_is_installed()) {  torch_randperm(4) } #> torch_tensor #>  1 #>  2 #>  0 #>  3 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Range — torch_range","title":"Range — torch_range","text":"Range","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Range — torch_range","text":"","code":"torch_range(   start,   end,   step = 1,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Range — torch_range","text":"start (float) starting value set points. Default: 0. end (float) ending value set points step (float) gap pair adjacent points. Default: 1. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Range — torch_range","text":"Returns 1-D tensor size \\(\\left\\lfloor \\frac{\\mbox{end} - \\mbox{start}}{\\mbox{step}} \\right\\rfloor + 1\\) values start end step step. Step gap two values tensor. $$     \\mbox{}_{+1} = \\mbox{}_i + \\mbox{step}. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Range — torch_range","text":"function deprecated favor torch_arange.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Range — torch_range","text":"","code":"if (torch_is_installed()) {  torch_range(1, 4) torch_range(1, 4, 0.5) } #> Warning: This function is deprecated in favor of torch_arange. #> Warning: This function is deprecated in favor of torch_arange. #> torch_tensor #>  1.0000 #>  1.5000 #>  2.0000 #>  2.5000 #>  3.0000 #>  3.5000 #>  4.0000 #> [ CPUFloatType{7} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Real — torch_real","title":"Real — torch_real","text":"Real","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real — torch_real","text":"","code":"torch_real(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real — torch_real","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"real-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"real(input) -> Tensor","title":"Real — torch_real","text":"Returns real part input tensor. input real (non-complex) tensor, function just returns .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Real — torch_real","text":"yet implemented complex tensors. $$     \\mbox{}_{} = real(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real — torch_real","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal — torch_reciprocal","title":"Reciprocal — torch_reciprocal","text":"Reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal — torch_reciprocal","text":"","code":"torch_reciprocal(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal — torch_reciprocal","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"reciprocal-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"reciprocal(input, out=NULL) -> Tensor","title":"Reciprocal — torch_reciprocal","text":"Returns new tensor reciprocal elements input $$     \\mbox{}_{} = \\frac{1}{\\mbox{input}_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal — torch_reciprocal","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_reciprocal(a) } #> torch_tensor #>  -12.5890 #>    1.9890 #>  162.2891 #>    1.2674 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_reduction.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the reduction objet — torch_reduction","title":"Creates the reduction objet — torch_reduction","text":"Creates reduction objet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reduction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the reduction objet — torch_reduction","text":"","code":"torch_reduction_sum()  torch_reduction_mean()  torch_reduction_none()"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu — torch_relu","title":"Relu — torch_relu","text":"Relu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu — torch_relu","text":"","code":"torch_relu(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu — torch_relu","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"relu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"relu(input) -> Tensor","title":"Relu — torch_relu","text":"Computes relu tranformation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu_ — torch_relu_","title":"Relu_ — torch_relu_","text":"Relu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu_ — torch_relu_","text":"","code":"torch_relu_(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu_ — torch_relu_","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"relu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"relu_(input) -> Tensor","title":"Relu_ — torch_relu_","text":"-place version torch_relu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":null,"dir":"Reference","previous_headings":"","what":"Remainder — torch_remainder","title":"Remainder — torch_remainder","text":"Remainder","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remainder — torch_remainder","text":"","code":"torch_remainder(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remainder — torch_remainder","text":"self (Tensor) dividend (Tensor float) divisor may either number                               Tensor shape dividend","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"remainder-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"remainder(input, other, out=NULL) -> Tensor","title":"Remainder — torch_remainder","text":"Computes element-wise remainder division. divisor dividend may contain integer floating point numbers. remainder sign divisor. tensor, shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remainder — torch_remainder","text":"","code":"if (torch_is_installed()) {  torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2) torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5) } #> torch_tensor #>  1.0000 #>  0.5000 #>  0.0000 #>  1.0000 #>  0.5000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Renorm — torch_renorm","title":"Renorm — torch_renorm","text":"Renorm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renorm — torch_renorm","text":"","code":"torch_renorm(self, p, dim, maxnorm)"},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renorm — torch_renorm","text":"self (Tensor) input tensor. p (float) power norm computation dim (int) dimension slice get sub-tensors maxnorm (float) maximum norm keep sub-tensor ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Renorm — torch_renorm","text":"norm row lower maxnorm, row unchanged","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"renorm-input-p-dim-maxnorm-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"renorm(input, p, dim, maxnorm, out=NULL) -> Tensor","title":"Renorm — torch_renorm","text":"Returns tensor sub-tensor input along dimension dim normalized p-norm sub-tensor lower value maxnorm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Renorm — torch_renorm","text":"","code":"if (torch_is_installed()) { x = torch_ones(c(3, 3)) x[2,]$fill_(2) x[3,]$fill_(3) x torch_renorm(x, 1, 1, 5) } #> torch_tensor #>  1.0000  1.0000  1.0000 #>  1.6667  1.6667  1.6667 #>  1.6667  1.6667  1.6667 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat_interleave — torch_repeat_interleave","title":"Repeat_interleave — torch_repeat_interleave","text":"Repeat_interleave","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"torch_repeat_interleave(self, repeats, dim = NULL, output_size = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat_interleave — torch_repeat_interleave","text":"self (Tensor) input tensor. repeats (Tensor int) number repetitions element.        repeats broadcasted fit shape given axis. dim (int, optional) dimension along repeat values.        default, use flattened input array, return flat output        array. output_size (int, optional) – Total output size given axis ( e.g. sum repeats). given, avoid stream syncronization needed calculate output shape tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"repeat-interleave-input-repeats-dim-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"repeat_interleave(input, repeats, dim=NULL) -> Tensor","title":"Repeat_interleave — torch_repeat_interleave","text":"Repeat elements tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`."},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"repeat-interleave-repeats-gt-tensor-","dir":"Reference","previous_headings":"","what":"repeat_interleave(repeats) -> Tensor","title":"Repeat_interleave — torch_repeat_interleave","text":"repeats tensor([n1, n2, n3, ...]), output tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...]) 0 appears n1 times, 1 appears n2 times, 2 appears n3 times, etc.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"if (torch_is_installed()) { if (FALSE) { x = torch_tensor(c(1, 2, 3)) x$repeat_interleave(2) y = torch_tensor(matrix(c(1, 2, 3, 4), ncol = 2, byrow=TRUE)) torch_repeat_interleave(y, 2) torch_repeat_interleave(y, 3, dim=1) torch_repeat_interleave(y, torch_tensor(c(1, 2)), dim=1) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape — torch_reshape","title":"Reshape — torch_reshape","text":"Reshape","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape — torch_reshape","text":"","code":"torch_reshape(self, shape)"},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape — torch_reshape","text":"self (Tensor) tensor reshaped shape (tuple ints) new shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"reshape-input-shape-gt-tensor-","dir":"Reference","previous_headings":"","what":"reshape(input, shape) -> Tensor","title":"Reshape — torch_reshape","text":"Returns tensor data number elements input, specified shape. possible, returned tensor view input. Otherwise, copy. Contiguous inputs inputs compatible strides can reshaped without copying, depend copying vs. viewing behavior. See torch_Tensor.view possible return view. single dimension may -1, case inferred remaining dimensions number elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape — torch_reshape","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(0, 3) torch_reshape(a, list(2, 2)) b <- torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE)) torch_reshape(b, list(-1)) } #> torch_tensor #>  0 #>  1 #>  2 #>  3 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Result_type — torch_result_type","title":"Result_type — torch_result_type","text":"Result_type","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result_type — torch_result_type","text":"","code":"torch_result_type(tensor1, tensor2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result_type — torch_result_type","text":"tensor1 (Tensor Number) input tensor number tensor2 (Tensor Number) input tensor number","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"result-type-tensor-tensor-gt-dtype-","dir":"Reference","previous_headings":"","what":"result_type(tensor1, tensor2) -> dtype","title":"Result_type — torch_result_type","text":"Returns torch_dtype result performing arithmetic operation provided input tensors. See type promotion documentation information type promotion logic.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result_type — torch_result_type","text":"","code":"if (torch_is_installed()) {  torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1) } #> torch_Float"},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll — torch_roll","title":"Roll — torch_roll","text":"Roll","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll — torch_roll","text":"","code":"torch_roll(self, shifts, dims = list())"},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll — torch_roll","text":"self (Tensor) input tensor. shifts (int tuple ints) number places elements        tensor shifted. shifts tuple, dims must tuple        size, dimension rolled corresponding        value dims (int tuple ints) Axis along roll","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"roll-input-shifts-dims-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"roll(input, shifts, dims=NULL) -> Tensor","title":"Roll — torch_roll","text":"Roll tensor along given dimension(s). Elements shifted beyond last position re-introduced first position. dimension specified, tensor flattened rolling restored original shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Roll — torch_roll","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2)) x torch_roll(x, 1, 1) torch_roll(x, -1, 1) torch_roll(x, shifts=list(2, 1), dims=list(1, 2)) } #> torch_tensor #>  6  5 #>  8  7 #>  2  1 #>  4  3 #> [ CPUFloatType{4,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":null,"dir":"Reference","previous_headings":"","what":"Rot90 — torch_rot90","title":"Rot90 — torch_rot90","text":"Rot90","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rot90 — torch_rot90","text":"","code":"torch_rot90(self, k = 1L, dims = c(0, 1))"},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rot90 — torch_rot90","text":"self (Tensor) input tensor. k (int) number times rotate dims (list tuple) axis rotate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"rot-input-k-dims-gt-tensor-","dir":"Reference","previous_headings":"","what":"rot90(input, k, dims) -> Tensor","title":"Rot90 — torch_rot90","text":"Rotate n-D tensor 90 degrees plane specified dims axis. Rotation direction first towards second axis k > 0, second towards first k < 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rot90 — torch_rot90","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 4)$view(c(2, 2)) x torch_rot90(x, 1, c(1, 2)) x <- torch_arange(1, 8)$view(c(2, 2, 2)) x torch_rot90(x, 1, c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   3  4 #>   7  8 #>  #> (2,.,.) =  #>   1  2 #>   5  6 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Round — torch_round","title":"Round — torch_round","text":"Round","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round — torch_round","text":"","code":"torch_round(self, decimals)"},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round — torch_round","text":"self (Tensor) input tensor. decimals Number decimal places round (default: 0). decimals negative, specifies number positions left decimal point.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"round-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"round(input, out=NULL) -> Tensor","title":"Round — torch_round","text":"Returns new tensor elements input rounded closest integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round — torch_round","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_round(a) } #> torch_tensor #> -1 #> -1 #>  0 #>  1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Rrelu_ — torch_rrelu_","title":"Rrelu_ — torch_rrelu_","text":"Rrelu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rrelu_ — torch_rrelu_","text":"","code":"torch_rrelu_(   self,   lower = 0.125,   upper = 0.333333333333333,   training = FALSE,   generator = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rrelu_ — torch_rrelu_","text":"self input tensor lower lower bound uniform distribution. Default: 1/8 upper upper bound uniform distribution. Default: 1/3 training bool wether training pass. DEfault: FALSE generator random number generator","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"rrelu-input-lower-upper-training-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor","title":"Rrelu_ — torch_rrelu_","text":"-place version torch_rrelu.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Rsqrt — torch_rsqrt","title":"Rsqrt — torch_rsqrt","text":"Rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rsqrt — torch_rsqrt","text":"","code":"torch_rsqrt(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rsqrt — torch_rsqrt","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"rsqrt-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"rsqrt(input, out=NULL) -> Tensor","title":"Rsqrt — torch_rsqrt","text":"Returns new tensor reciprocal square-root elements input. $$     \\mbox{}_{} = \\frac{1}{\\sqrt{\\mbox{input}_{}}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rsqrt — torch_rsqrt","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_rsqrt(a) } #> torch_tensor #>     nan #>  3.2777 #>  1.3045 #>     nan #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an object to a disk file. — torch_save","title":"Saves an object to a disk file. — torch_save","text":"function experimental, use long term storage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an object to a disk file. — torch_save","text":"","code":"torch_save(obj, path, ..., compress = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an object to a disk file. — torch_save","text":"obj saved object path connection name file save. ... currently used. compress logical specifying whether saving named file use \"gzip\" compression, one \"gzip\", \"bzip2\" \"xz\" indicate type compression used. Ignored file connection.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar tensor — torch_scalar_tensor","title":"Scalar tensor — torch_scalar_tensor","text":"Creates singleton dimension tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar tensor — torch_scalar_tensor","text":"","code":"torch_scalar_tensor(value, dtype = NULL, device = NULL, requires_grad = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar tensor — torch_scalar_tensor","text":"value value want use dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":null,"dir":"Reference","previous_headings":"","what":"Searchsorted — torch_searchsorted","title":"Searchsorted — torch_searchsorted","text":"Searchsorted","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Searchsorted — torch_searchsorted","text":"","code":"torch_searchsorted(   sorted_sequence,   self,   out_int32 = FALSE,   right = FALSE,   side = NULL,   sorter = list() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Searchsorted — torch_searchsorted","text":"sorted_sequence (Tensor) N-D 1-D tensor, containing monotonically increasing sequence innermost dimension. self (Tensor Scalar) N-D tensor Scalar containing search value(s). out_int32 (bool, optional) – indicate output data type. torch_int32() True, torch_int64() otherwise. Default value FALSE, .e. default output data type torch_int64(). right (bool, optional) – False, return first suitable location found. True, return last index. suitable index found, return 0 non-numerical value (eg. nan, inf) size boundaries (one pass last index). words, False, gets lower bound index value input boundaries. True, gets upper bound index instead. Default value False. side right preferred. “left” corresponds FALSE right “right” corresponds TRUE right. error set “left” right TRUE. sorter provided, tensor matching shape unsorted sorted_sequence containing sequence indices sort ascending order innermost dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor","title":"Searchsorted — torch_searchsorted","text":"Find indices innermost dimension sorted_sequence , corresponding values values inserted indices, order corresponding innermost dimension within sorted_sequence preserved. Return new tensor size values. right FALSE (default), left boundary sorted_sequence closed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Searchsorted — torch_searchsorted","text":"","code":"if (torch_is_installed()) {  sorted_sequence <- torch_tensor(rbind(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10))) sorted_sequence values <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9))) values torch_searchsorted(sorted_sequence, values) torch_searchsorted(sorted_sequence, values, right=TRUE) sorted_sequence_1d <- torch_tensor(c(1, 3, 5, 7, 9)) sorted_sequence_1d torch_searchsorted(sorted_sequence_1d, values) } #> torch_tensor #>  1  3  4 #>  1  3  4 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu — torch_selu","title":"Selu — torch_selu","text":"Selu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu — torch_selu","text":"","code":"torch_selu(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu — torch_selu","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"selu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"selu(input) -> Tensor","title":"Selu — torch_selu","text":"Computes selu transformation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu_ — torch_selu_","title":"Selu_ — torch_selu_","text":"Selu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu_ — torch_selu_","text":"","code":"torch_selu_(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu_ — torch_selu_","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"selu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"selu_(input) -> Tensor","title":"Selu_ — torch_selu_","text":"-place version torch_selu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":null,"dir":"Reference","previous_headings":"","what":"Serialize a torch object returning a raw object — torch_serialize","title":"Serialize a torch object returning a raw object — torch_serialize","text":"just wraper around torch_save().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Serialize a torch object returning a raw object — torch_serialize","text":"","code":"torch_serialize(obj, ...)"},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Serialize a torch object returning a raw object — torch_serialize","text":"obj saved object ... Additional arguments passed torch_save(). obj path accepted set torch_serialize().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Serialize a torch object returning a raw object — torch_serialize","text":"raw vector containing serialized object. Can reloaded using torch_load().","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":null,"dir":"Reference","previous_headings":"","what":"Sgn — torch_sgn","title":"Sgn — torch_sgn","text":"Sgn","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sgn — torch_sgn","text":"","code":"torch_sgn(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sgn — torch_sgn","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"sgn-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"sgn(input, *, out=None) -> Tensor","title":"Sgn — torch_sgn","text":"complex tensors, function returns new tensor whose elemants angle elements input absolute value 1. non-complex tensor, function returns signs elements input (see torch_sign). \\(\\mbox{}_{} = 0\\), \\(|{\\mbox{{input}}_i}| == 0\\) \\(\\mbox{}_{} = \\frac{{\\mbox{{input}}_i}}{|{\\mbox{{input}}_i}|}\\), otherwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sgn — torch_sgn","text":"","code":"if (torch_is_installed()) { if (FALSE) { x <- torch_tensor(c(3+4i, 7-24i, 0, 1+2i)) x$sgn() torch_sgn(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid — torch_sigmoid","title":"Sigmoid — torch_sigmoid","text":"Sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid — torch_sigmoid","text":"","code":"torch_sigmoid(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid — torch_sigmoid","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"sigmoid-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sigmoid(input, out=NULL) -> Tensor","title":"Sigmoid — torch_sigmoid","text":"Returns new tensor sigmoid elements input. $$     \\mbox{}_{} = \\frac{1}{1 + e^{-\\mbox{input}_{}}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid — torch_sigmoid","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sigmoid(a) } #> torch_tensor #>  0.3561 #>  0.6325 #>  0.5406 #>  0.2522 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Sign — torch_sign","title":"Sign — torch_sign","text":"Sign","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sign — torch_sign","text":"","code":"torch_sign(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sign — torch_sign","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"sign-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sign(input, out=NULL) -> Tensor","title":"Sign — torch_sign","text":"Returns new tensor signs elements input. $$     \\mbox{}_{} = \\mbox{sgn}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sign — torch_sign","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(0.7, -1.2, 0., 2.3)) a torch_sign(a) } #> torch_tensor #>  1 #> -1 #>  0 #>  1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":null,"dir":"Reference","previous_headings":"","what":"Signbit — torch_signbit","title":"Signbit — torch_signbit","text":"Signbit","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signbit — torch_signbit","text":"","code":"torch_signbit(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signbit — torch_signbit","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"signbit-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"signbit(input, *, out=None) -> Tensor","title":"Signbit — torch_signbit","text":"Tests element input sign bit set (less zero) .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signbit — torch_signbit","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(0.7, -1.2, 0., 2.3)) torch_signbit(a) } #> torch_tensor #>  0 #>  1 #>  0 #>  0 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Sin — torch_sin","title":"Sin — torch_sin","text":"Sin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sin — torch_sin","text":"","code":"torch_sin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sin — torch_sin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"sin-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sin(input, out=NULL) -> Tensor","title":"Sin — torch_sin","text":"Returns new tensor sine elements input. $$     \\mbox{}_{} = \\sin(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sin — torch_sin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sin(a) } #> torch_tensor #>  0.6204 #> -0.8166 #>  0.9796 #> -0.9412 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Sinh — torch_sinh","title":"Sinh — torch_sinh","text":"Sinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sinh — torch_sinh","text":"","code":"torch_sinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sinh — torch_sinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"sinh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sinh(input, out=NULL) -> Tensor","title":"Sinh — torch_sinh","text":"Returns new tensor hyperbolic sine elements input. $$     \\mbox{}_{} = \\sinh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sinh — torch_sinh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sinh(a) } #> torch_tensor #> -2.4683 #>  0.7364 #> -1.0255 #>  1.5774 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Slogdet — torch_slogdet","title":"Slogdet — torch_slogdet","text":"Slogdet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Slogdet — torch_slogdet","text":"","code":"torch_slogdet(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Slogdet — torch_slogdet","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Slogdet — torch_slogdet","text":"","code":"If `input` has zero determinant, this returns `(0, -inf)`. Backward through `slogdet` internally uses SVD results when `input` is not invertible. In this case, double backward through `slogdet` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"slogdet-input-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"slogdet(input) -> (Tensor, Tensor)","title":"Slogdet — torch_slogdet","text":"Calculates sign log absolute value determinant(s) square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slogdet — torch_slogdet","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) A torch_det(A) torch_logdet(A) torch_slogdet(A) } #> [[1]] #> torch_tensor #> 1 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 0.240458 #> [ CPUFloatType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort — torch_sort","title":"Sort — torch_sort","text":"Sort","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort — torch_sort","text":"self (Tensor) input tensor. dim (int, optional) dimension sort along descending (bool, optional) controls sorting order (ascending descending) stable (bool, optional) – makes sorting routine stable, guarantees order equivalent elements preserved.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"sort-input-dim-descending-false-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor)","title":"Sort — torch_sort","text":"Sorts elements input tensor along given dimension ascending order value. dim given, last dimension input chosen. descending TRUE elements sorted descending order value. namedtuple (values, indices) returned, values sorted values indices indices elements original input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort — torch_sort","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) out = torch_sort(x) out out = torch_sort(x, 1) out } #> [[1]] #> torch_tensor #> -0.7761 -2.0850 -0.6274 -0.7934 #> -0.5297 -1.2828 -0.2581  0.2248 #>  1.0347 -0.2183  0.0451  0.8375 #> [ CPUFloatType{3,4} ] #>  #> [[2]] #> torch_tensor #>  3  2  2  3 #>  1  1  1  1 #>  2  3  3  2 #> [ CPULongType{3,4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse_coo_tensor — torch_sparse_coo_tensor","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"Sparse_coo_tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"","code":"torch_sparse_coo_tensor(   indices,   values,   size = NULL,   dtype = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"indices (array_like) Initial data tensor. Can list, tuple,        NumPy ndarray, scalar, types. cast torch_LongTensor        internally. indices coordinates non-zero values matrix, thus        two-dimensional first dimension number tensor dimensions        second dimension number non-zero values. values (array_like) Initial values tensor. Can list, tuple,        NumPy ndarray, scalar, types. size (list, tuple, torch.Size, optional) Size sparse tensor.        provided size inferred minimum size big enough hold non-zero        elements. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, infers data type values. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"Constructs sparse tensors COO(rdinate) format non-zero elements given indices given values. sparse tensor can uncoalesced, case, duplicate coordinates indices, value index sum duplicate value entries: torch_sparse_.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"","code":"if (torch_is_installed()) {  i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64()) v = torch_tensor(c(3, 4, 5), dtype=torch_float32()) torch_sparse_coo_tensor(i, v) torch_sparse_coo_tensor(i, v, c(2, 4))  # create empty sparse tensors S = torch_sparse_coo_tensor(   torch_empty(c(1, 0), dtype = torch_int64()),    torch_tensor(numeric(), dtype = torch_float32()),    c(1) ) S = torch_sparse_coo_tensor(   torch_empty(c(1, 0), dtype = torch_int64()),    torch_empty(c(0, 2)),    c(1, 2) ) }"},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split — torch_split","title":"Split — torch_split","text":"Splits tensor chunks. chunk view original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split — torch_split","text":"","code":"torch_split(self, split_size, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split — torch_split","text":"self (Tensor) tensor split. split_size (int) size single chunk list sizes chunk dim (int) dimension along split tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split — torch_split","text":"split_size integer type, tensor split equally sized chunks (possible). Last chunk smaller tensor size along given dimension dim divisible split_size. split_size list, tensor split length(split_size) chunks sizes dim according split_size_or_sections.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Sqrt — torch_sqrt","title":"Sqrt — torch_sqrt","text":"Sqrt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sqrt — torch_sqrt","text":"","code":"torch_sqrt(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sqrt — torch_sqrt","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"sqrt-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sqrt(input, out=NULL) -> Tensor","title":"Sqrt — torch_sqrt","text":"Returns new tensor square-root elements input. $$     \\mbox{}_{} = \\sqrt{\\mbox{input}_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sqrt — torch_sqrt","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sqrt(a) } #> torch_tensor #>     nan #>  1.1726 #>  1.0350 #>  1.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Square — torch_square","title":"Square — torch_square","text":"Square","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Square — torch_square","text":"","code":"torch_square(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Square — torch_square","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"square-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"square(input, out=NULL) -> Tensor","title":"Square — torch_square","text":"Returns new tensor square elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Square — torch_square","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_square(a) } #> torch_tensor #>  0.1155 #>  0.0852 #>  6.8488 #>  0.0022 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Squeeze — torch_squeeze","title":"Squeeze — torch_squeeze","text":"Squeeze","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Squeeze — torch_squeeze","text":"","code":"torch_squeeze(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Squeeze — torch_squeeze","text":"self (Tensor) input tensor. dim (int, optional) given, input squeezed           dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Squeeze — torch_squeeze","text":"returned tensor shares storage input tensor, changing contents one change contents .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"squeeze-input-dim-null-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"squeeze(input, dim=NULL, out=NULL) -> Tensor","title":"Squeeze — torch_squeeze","text":"Returns tensor dimensions input size 1 removed. example, input shape: \\((\\times 1 \\times B \\times C \\times 1 \\times D)\\) tensor shape: \\((\\times B \\times C \\times D)\\). dim given, squeeze operation done given dimension. input shape: \\((\\times 1 \\times B)\\), squeeze(input, 0) leaves tensor unchanged, squeeze(input, 1) squeeze tensor shape \\((\\times B)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Squeeze — torch_squeeze","text":"","code":"if (torch_is_installed()) {  x = torch_zeros(c(2, 1, 2, 1, 2)) x y = torch_squeeze(x) y y = torch_squeeze(x, 1) y y = torch_squeeze(x, 2) y } #> torch_tensor #> (1,1,.,.) =  #>   0  0 #>  #> (2,1,.,.) =  #>   0  0 #>  #> (1,2,.,.) =  #>   0  0 #>  #> (2,2,.,.) =  #>   0  0 #> [ CPUFloatType{2,2,1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack — torch_stack","title":"Stack — torch_stack","text":"Stack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack — torch_stack","text":"","code":"torch_stack(tensors, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack — torch_stack","text":"tensors (sequence Tensors) sequence tensors concatenate dim (int) dimension insert. 0 number        dimensions concatenated tensors (inclusive)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"stack-tensors-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"stack(tensors, dim=0, out=NULL) -> Tensor","title":"Stack — torch_stack","text":"Concatenates sequence tensors along new dimension. tensors need size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Std — torch_std","title":"Std — torch_std","text":"Std","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Std — torch_std","text":"","code":"torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Std — torch_std","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"std-input-unbiased-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"std(input, unbiased=TRUE) -> Tensor","title":"Std — torch_std","text":"Returns standard-deviation elements input tensor. unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor","title":"Std — torch_std","text":"Returns standard-deviation row input tensor dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Std — torch_std","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_std(a)   a = torch_randn(c(4, 4)) a torch_std(a, dim=1) } #> torch_tensor #>  1.1563 #>  0.3994 #>  0.5251 #>  1.2504 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Std_mean — torch_std_mean","title":"Std_mean — torch_std_mean","text":"Std_mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Std_mean — torch_std_mean","text":"","code":"torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Std_mean — torch_std_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"std-mean-input-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"std_mean(input, unbiased=TRUE) -> (Tensor, Tensor)","title":"Std_mean — torch_std_mean","text":"Returns standard-deviation mean elements input tensor. unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor)","title":"Std_mean — torch_std_mean","text":"Returns standard-deviation mean row input tensor dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Std_mean — torch_std_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_std_mean(a)   a = torch_randn(c(4, 4)) a torch_std_mean(a, 1) } #> [[1]] #> torch_tensor #>  0.3698 #>  0.3130 #>  0.6728 #>  1.1034 #> [ CPUFloatType{4} ] #>  #> [[2]] #> torch_tensor #> -0.1066 #>  0.0862 #>  0.1781 #>  0.1767 #> [ CPUFloatType{4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Stft — torch_stft","title":"Stft — torch_stft","text":"Stft","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stft — torch_stft","text":"","code":"torch_stft(   input,   n_fft,   hop_length = NULL,   win_length = NULL,   window = NULL,   center = TRUE,   pad_mode = \"reflect\",   normalized = FALSE,   onesided = NULL,   return_complex = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stft — torch_stft","text":"input (Tensor) input tensor n_fft (int) size Fourier transform hop_length (int, optional) distance neighboring sliding window frames. Default: NULL (treated equal floor(n_fft / 4)) win_length (int, optional) size window frame STFT filter. Default: NULL  (treated equal n_fft) window (Tensor, optional) optional window function. Default: NULL (treated window \\(1\\) s) center (bool, optional) whether pad input sides \\(t\\)-th frame centered time \\(t \\times \\mbox{hop\\_length}\\). Default: TRUE pad_mode (string, optional) controls padding method used center TRUE. Default: \"reflect\" normalized (bool, optional) controls whether return normalized STFT results Default: FALSE onesided (bool, optional) controls whether return half results avoid redundancy Default: TRUE return_complex (bool, optional) controls whether return complex tensors .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"short-time-fourier-transform-stft-","dir":"Reference","previous_headings":"","what":"Short-time Fourier transform (STFT).","title":"Stft — torch_stft","text":"Short-time Fourier transform (STFT).   $$         X[m, \\omega] = \\sum_{k = 0}^{\\mbox{win\\_length-1}}%                             \\mbox{window}[k]\\ \\mbox{input}[m \\times \\mbox{hop\\_length} + k]\\ %                             \\exp\\left(- j \\frac{2 \\pi \\cdot \\omega k}{\\mbox{win\\_length}}\\right), $$ \\(m\\) index sliding window, \\(\\omega\\) frequency \\(0 \\leq \\omega < \\mbox{n\\_fft}\\). onesided default value TRUE,","code":"Ignoring the optional batch dimension, this method computes the following expression: * `input` must be either a 1-D time sequence or a 2-D batch of time   sequences.  * If `hop_length` is `NULL` (default), it is treated as equal to   `floor(n_fft / 4)`.  * If `win_length` is `NULL` (default), it is treated as equal to   `n_fft`.  * `window` can be a 1-D tensor of size `win_length`, e.g., from   `torch_hann_window`. If `window` is `NULL` (default), it is   treated as if having \\eqn{1} everywhere in the window. If   \\eqn{\\mbox{win\\_length} < \\mbox{n\\_fft}}, `window` will be padded on   both sides to length `n_fft` before being applied.  * If `center` is `TRUE` (default), `input` will be padded on   both sides so that the \\eqn{t}-th frame is centered at time   \\eqn{t \\times \\mbox{hop\\_length}}. Otherwise, the \\eqn{t}-th frame   begins at time  \\eqn{t \\times \\mbox{hop\\_length}}.  * `pad_mode` determines the padding method used on `input` when   `center` is `TRUE`. See `torch_nn.functional.pad` for   all available options. Default is `\"reflect\"`.  * If `onesided` is `TRUE` (default), only values for \\eqn{\\omega}   in \\eqn{\\left[0, 1, 2, \\dots, \\left\\lfloor \\frac{\\mbox{n\\_fft}}{2} \\right\\rfloor + 1\\right]}   are returned because the real-to-complex Fourier transform satisfies the   conjugate symmetry, i.e., \\eqn{X[m, \\omega] = X[m, \\mbox{n\\_fft} - \\omega]^*}.  * If `normalized` is `TRUE` (default is `FALSE`), the function   returns the normalized STFT results, i.e., multiplied by \\eqn{(\\mbox{frame\\_length})^{-0.5}}.  Returns the real and the imaginary parts together as one tensor of size \\eqn{(* \\times N \\times T \\times 2)}, where \\eqn{*} is the optional batch size of `input`, \\eqn{N} is the number of frequencies where STFT is applied, \\eqn{T} is the total number of frames used, and each pair in the last dimension represents a complex number as the real part and the imaginary part."},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Stft — torch_stft","text":"function changed signature version 0.4.1. Calling previous signature may cause error return incorrect result.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":null,"dir":"Reference","previous_headings":"","what":"Sub — torch_sub","title":"Sub — torch_sub","text":"Sub","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sub — torch_sub","text":"","code":"torch_sub(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sub — torch_sub","text":"self (Tensor) input tensor. (Tensor Scalar) tensor scalar subtract input alpha scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"sub-input-other-alpha-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"sub(input, other, *, alpha=1, out=None) -> Tensor","title":"Sub — torch_sub","text":"Subtracts , scaled alpha, input. $$     \\mbox{{}}_i = \\mbox{{input}}_i - \\mbox{{alpha}} \\times \\mbox{{}}_i $$ Supports broadcasting common shape , type promotion , integer, float, complex inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sub — torch_sub","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2)) b <- torch_tensor(c(0, 1)) torch_sub(a, b, alpha=2) } #> torch_tensor #>  1 #>  0 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract — torch_subtract","title":"Subtract — torch_subtract","text":"Subtract","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract — torch_subtract","text":"","code":"torch_subtract(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract — torch_subtract","text":"self (Tensor) input tensor. (Tensor Scalar) tensor scalar subtract input alpha scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"subtract-input-other-alpha-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"subtract(input, other, *, alpha=1, out=None) -> Tensor","title":"Subtract — torch_subtract","text":"Alias torch_sub().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum — torch_sum","title":"Sum — torch_sum","text":"Sum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum — torch_sum","text":"","code":"torch_sum(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum — torch_sum","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"sum-input-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sum(input, dtype=NULL) -> Tensor","title":"Sum — torch_sum","text":"Returns sum elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"sum-input-dim-keepdim-false-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sum(input, dim, keepdim=False, dtype=NULL) -> Tensor","title":"Sum — torch_sum","text":"Returns sum row input tensor given dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sum — torch_sum","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_sum(a)   a <- torch_randn(c(4, 4)) a torch_sum(a, 1) b <- torch_arange(1, 4 * 5 * 6)$view(c(4, 5, 6)) torch_sum(b, list(2, 1)) } #> torch_tensor #>  1160 #>  1180 #>  1200 #>  1220 #>  1240 #>  1260 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Svd — torch_svd","title":"Svd — torch_svd","text":"Svd","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Svd — torch_svd","text":"","code":"torch_svd(self, some = TRUE, compute_uv = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Svd — torch_svd","text":"self (Tensor) input tensor size \\((*, m, n)\\) * zero                    batch dimensions consisting \\(m \\times n\\) matrices. (bool, optional) controls shape returned U V compute_uv (bool, optional) option whether compute U V ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Svd — torch_svd","text":"singular values returned descending order. input batch matrices, singular values matrix batch returned descending order. implementation SVD CPU uses LAPACK routine ?gesdd (divide--conquer algorithm) instead ?gesvd speed. Analogously, SVD GPU uses MAGMA routine gesdd well. Irrespective original strides, returned matrix U transposed, .e. strides U.contiguous().transpose(-2, -1).stride() Extra care needs taken backward U V outputs. operation really stable input full rank distinct singular values. Otherwise, NaN can appear gradients properly defined. Also, notice double backward usually additional backward U V even original backward S. = FALSE, gradients U[..., :, min(m, n):] V[..., :, min(m, n):] ignored backward vectors can arbitrary bases subspaces. compute_uv = FALSE, backward performed since U V forward pass required backward operation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-","dir":"Reference","previous_headings":"","what":"svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)","title":"Svd — torch_svd","text":"function returns namedtuple (U, S, V) singular value decomposition input real matrix batches real matrices input \\(input = U \\times diag(S) \\times V^T\\). TRUE (default), method returns reduced singular value decomposition .e., last two dimensions input m n, returned U V matrices contain \\(min(n, m)\\) orthonormal columns. compute_uv FALSE, returned U V matrices zero matrices shape \\((m \\times m)\\) \\((n \\times n)\\) respectively. ignored .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Svd — torch_svd","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5, 3)) a out = torch_svd(a) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t())) a_big = torch_randn(c(7, 5, 3)) out = torch_svd(a_big) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1))) } #> torch_tensor #> 2.91169e-06 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":null,"dir":"Reference","previous_headings":"","what":"T — torch_t","title":"T — torch_t","text":"T","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"T — torch_t","text":"","code":"torch_t(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"T — torch_t","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"t-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"t(input) -> Tensor","title":"T — torch_t","text":"Expects input <= 2-D tensor transposes dimensions 0 1. 0-D 1-D tensors returned . input 2-D tensor equivalent transpose(input, 0, 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"T — torch_t","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2,3)) x torch_t(x) x = torch_randn(c(3)) x torch_t(x) x = torch_randn(c(2, 3)) x torch_t(x) } #> torch_tensor #> -0.8600  0.5891 #> -0.0482 -1.9269 #>  1.6084  0.3332 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take — torch_take","title":"Take — torch_take","text":"Take","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take — torch_take","text":"","code":"torch_take(self, index)"},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take — torch_take","text":"self (Tensor) input tensor. index (LongTensor) indices tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"take-input-index-gt-tensor-","dir":"Reference","previous_headings":"","what":"take(input, index) -> Tensor","title":"Take — torch_take","text":"Returns new tensor elements input given indices. input tensor treated viewed 1-D tensor. result takes shape indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take — torch_take","text":"","code":"if (torch_is_installed()) {  src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE)) torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64())) } #> torch_tensor #>  4 #>  3 #>  7 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Tan — torch_tan","title":"Tan — torch_tan","text":"Tan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tan — torch_tan","text":"","code":"torch_tan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tan — torch_tan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"tan-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tan(input, out=NULL) -> Tensor","title":"Tan — torch_tan","text":"Returns new tensor tangent elements input. $$     \\mbox{}_{} = \\tan(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tan — torch_tan","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_tan(a) } #> torch_tensor #>  0.6133 #>  0.6518 #>  0.1024 #>  0.6123 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanh — torch_tanh","title":"Tanh — torch_tanh","text":"Tanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanh — torch_tanh","text":"","code":"torch_tanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tanh — torch_tanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"tanh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tanh(input, out=NULL) -> Tensor","title":"Tanh — torch_tanh","text":"Returns new tensor hyperbolic tangent elements input. $$     \\mbox{}_{} = \\tanh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanh — torch_tanh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_tanh(a) } #> torch_tensor #> -0.2279 #>  0.1029 #>  0.8538 #>  0.8189 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts R objects to a torch tensor — torch_tensor","title":"Converts R objects to a torch tensor — torch_tensor","text":"Converts R objects torch tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts R objects to a torch tensor — torch_tensor","text":"","code":"torch_tensor(   data,   dtype = NULL,   device = NULL,   requires_grad = FALSE,   pin_memory = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts R objects to a torch tensor — torch_tensor","text":"data R atomic vector, matrix array dtype torch_dtype instance device device creted torch_device() requires_grad autograd record operations returned tensor. pin_memory set, returned tensor allocated pinned memory.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts R objects to a torch tensor — torch_tensor","text":"","code":"if (torch_is_installed()) { torch_tensor(c(1, 2, 3, 4)) torch_tensor(c(1, 2, 3, 4), dtype = torch_int()) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPUIntType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"creates tensor without taking ownership memory points . must call clone want copy memory new tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"","code":"torch_tensor_from_buffer(buffer, shape, dtype = \"float\")  buffer_from_torch_tensor(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"buffer R atomic object containing data contiguous array. shape shape resulting tensor. dtype torch data type tresulting tensor. tensor Tensor object converted buffer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"buffer_from_torch_tensor(): Creates raw vector containing tensor data. Causes data copy.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensordot — torch_tensordot","title":"Tensordot — torch_tensordot","text":"Returns contraction b multiple dimensions. tensordot implements generalized matrix product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensordot — torch_tensordot","text":"","code":"torch_tensordot(a, b, dims = 2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensordot — torch_tensordot","text":"(Tensor) Left tensor contract b (Tensor) Right tensor contract dims (int tuple two lists integers) number dimensions     contract explicit lists dimensions     b respectively","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensordot — torch_tensordot","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(start = 1, end = 60)$reshape(c(3, 4, 5)) b <- torch_arange(start = 1, end = 24)$reshape(c(4, 3, 2)) torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2))) if (FALSE) { a = torch_randn(3, 4, 5, device='cuda') b = torch_randn(4, 5, 6, device='cuda') c = torch_tensordot(a, b, dims=2)$cpu() } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold_ — torch_threshold_","title":"Threshold_ — torch_threshold_","text":"Threshold_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold_ — torch_threshold_","text":"","code":"torch_threshold_(self, threshold, value)"},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold_ — torch_threshold_","text":"self input tensor threshold value threshold value value replace ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"threshold-input-threshold-value-gt-tensor-","dir":"Reference","previous_headings":"","what":"threshold_(input, threshold, value) -> Tensor","title":"Threshold_ — torch_threshold_","text":"-place version torch_threshold.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":null,"dir":"Reference","previous_headings":"","what":"Topk — torch_topk","title":"Topk — torch_topk","text":"Topk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Topk — torch_topk","text":"","code":"torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Topk — torch_topk","text":"self (Tensor) input tensor. k (int) k \"top-k\" dim (int, optional) dimension sort along largest (bool, optional) controls whether return largest           smallest elements sorted (bool, optional) controls whether return elements           sorted order","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)","title":"Topk — torch_topk","text":"Returns k largest elements given input tensor along given dimension. dim given, last dimension input chosen. largest FALSE k smallest elements returned. namedtuple (values, indices) returned, indices indices elements original input tensor. boolean option sorted TRUE, make sure returned k elements sorted","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Topk — torch_topk","text":"","code":"if (torch_is_installed()) {  x = torch_arange(1., 6.) x torch_topk(x, 3) } #> [[1]] #> torch_tensor #>  6 #>  5 #>  4 #> [ CPUFloatType{3} ] #>  #> [[2]] #> torch_tensor #>  6 #>  5 #>  4 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace — torch_trace","title":"Trace — torch_trace","text":"Trace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace — torch_trace","text":"","code":"torch_trace(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace — torch_trace","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"trace-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"trace(input) -> Tensor","title":"Trace — torch_trace","text":"Returns sum elements diagonal input 2-D matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace — torch_trace","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 9)$view(c(3, 3)) x torch_trace(x) } #> torch_tensor #> 15 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose — torch_transpose","title":"Transpose — torch_transpose","text":"Transpose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose — torch_transpose","text":"","code":"torch_transpose(self, dim0, dim1)"},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose — torch_transpose","text":"self (Tensor) input tensor. dim0 (int) first dimension transposed dim1 (int) second dimension transposed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"transpose-input-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"transpose(input, dim0, dim1) -> Tensor","title":"Transpose — torch_transpose","text":"Returns tensor transposed version input. given dimensions dim0 dim1 swapped. resulting tensor shares underlying storage input tensor, changing content one change content .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose — torch_transpose","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2, 3)) x torch_transpose(x, 1, 2) } #> torch_tensor #> -1.2440 -0.3130 #>  0.0556  0.1887 #>  0.2138 -1.4054 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":null,"dir":"Reference","previous_headings":"","what":"Trapz — torch_trapz","title":"Trapz — torch_trapz","text":"Trapz","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trapz — torch_trapz","text":"","code":"torch_trapz(y, dx = 1L, x, dim = -1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trapz — torch_trapz","text":"y (Tensor) values function integrate dx (float) distance points y sampled. x (Tensor) points function y sampled.        x ascending order, intervals decreasing        contribute negatively estimated integral (.e., convention        \\(\\int_a^b f = -\\int_b^f\\) followed). dim (int) dimension along integrate.        default, use last dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"trapz-y-x-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"trapz(y, x, *, dim=-1) -> Tensor","title":"Trapz — torch_trapz","text":"Estimate \\(\\int y\\,dx\\) along dim, using trapezoid rule.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"trapz-y-dx-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"trapz(y, *, dx=1, dim=-1) -> Tensor","title":"Trapz — torch_trapz","text":", sample points spaced uniformly distance dx.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trapz — torch_trapz","text":"","code":"if (torch_is_installed()) {  y = torch_randn(list(2, 3)) y x = torch_tensor(matrix(c(1, 3, 4, 1, 2, 3), ncol = 3, byrow=TRUE)) torch_trapz(y, x = x)  } #> torch_tensor #> -3.0178 #> -0.8236 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular_solve — torch_triangular_solve","title":"Triangular_solve — torch_triangular_solve","text":"Triangular_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular_solve — torch_triangular_solve","text":"","code":"torch_triangular_solve(   self,   A,   upper = TRUE,   transpose = FALSE,   unitriangular = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular_solve — torch_triangular_solve","text":"self (Tensor) multiple right-hand sides size \\((*, m, k)\\)                \\(*\\) zero batch dimensions (\\(b\\)) (Tensor) input triangular coefficient matrix size \\((*, m, m)\\)                \\(*\\) zero batch dimensions upper (bool, optional) whether solve upper-triangular system        equations (default) lower-triangular system equations. Default: TRUE. transpose (bool, optional) whether \\(\\) transposed        sent solver. Default: FALSE. unitriangular (bool, optional) whether \\(\\) unit triangular.        TRUE, diagonal elements \\(\\) assumed        1 referenced \\(\\). Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor)","title":"Triangular_solve — torch_triangular_solve","text":"Solves system equations triangular coefficient matrix \\(\\) multiple right-hand sides \\(b\\). particular, solves \\(AX = b\\) assumes \\(\\) upper-triangular default keyword arguments. torch_triangular_solve(b, ) can take 2D inputs b, inputs batches 2D matrices. inputs batches, returns batched outputs X","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangular_solve — torch_triangular_solve","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(2, 2))$triu() A b = torch_randn(c(2, 3)) b torch_triangular_solve(b, A) } #> [[1]] #> torch_tensor #> -2.0220 -2.1697  0.2369 #>  0.5612 -0.8687  0.3310 #> [ CPUFloatType{2,3} ] #>  #> [[2]] #> torch_tensor #>  0.5262  0.6967 #>  0.0000  0.7233 #> [ CPUFloatType{2,2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Tril — torch_tril","title":"Tril — torch_tril","text":"Tril","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tril — torch_tril","text":"","code":"torch_tril(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tril — torch_tril","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"tril-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tril(input, diagonal=0, out=NULL) -> Tensor","title":"Tril — torch_tril","text":"Returns lower triangular part matrix (2-D tensor) batch matrices input, elements result tensor set 0. lower triangular part matrix defined elements diagonal. argument diagonal controls diagonal consider. diagonal = 0, elements main diagonal retained. positive value includes just many diagonals main diagonal, similarly negative value excludes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tril — torch_tril","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_tril(a) b = torch_randn(c(4, 6)) b torch_tril(b, diagonal=1) torch_tril(b, diagonal=-1) } #> torch_tensor #>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000 #>  1.8172  0.0000  0.0000  0.0000  0.0000  0.0000 #> -0.2900 -0.3578  0.0000  0.0000  0.0000  0.0000 #>  0.8494 -0.3954  0.7822  0.0000  0.0000  0.0000 #> [ CPUFloatType{4,6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Tril_indices — torch_tril_indices","title":"Tril_indices — torch_tril_indices","text":"Tril_indices","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tril_indices — torch_tril_indices","text":"","code":"torch_tril_indices(   row,   col,   offset = 0,   dtype = NULL,   device = NULL,   layout = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tril_indices — torch_tril_indices","text":"row (int) number rows 2-D matrix. col (int) number columns 2-D matrix. offset (int) diagonal offset main diagonal.        Default: provided, 0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, torch_long. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. layout (torch.layout, optional) currently support torch_strided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tril_indices — torch_tril_indices","text":"","code":"When running on CUDA, `row * col` must be less than \\eqn{2^{59}} to prevent overflow during calculation."},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-","dir":"Reference","previous_headings":"","what":"tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor","title":"Tril_indices — torch_tril_indices","text":"Returns indices lower triangular part row-- col matrix 2--N Tensor, first row contains row coordinates indices second row contains column coordinates. Indices ordered based rows columns. lower triangular part matrix defined elements diagonal. argument offset controls diagonal consider. offset = 0, elements main diagonal retained. positive value includes just many diagonals main diagonal, similarly negative value excludes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tril_indices — torch_tril_indices","text":"","code":"if (torch_is_installed()) { if (FALSE) { a = torch_tril_indices(3, 3) a a = torch_tril_indices(4, 3, -1) a a = torch_tril_indices(4, 3, 1) a } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Triu — torch_triu","title":"Triu — torch_triu","text":"Triu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triu — torch_triu","text":"","code":"torch_triu(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triu — torch_triu","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"triu-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"triu(input, diagonal=0, out=NULL) -> Tensor","title":"Triu — torch_triu","text":"Returns upper triangular part matrix (2-D tensor) batch matrices input, elements result tensor set 0. upper triangular part matrix defined elements diagonal. argument diagonal controls diagonal consider. diagonal = 0, elements main diagonal retained. positive value excludes just many diagonals main diagonal, similarly negative value includes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triu — torch_triu","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_triu(a) torch_triu(a, diagonal=1) torch_triu(a, diagonal=-1) b = torch_randn(c(4, 6)) b torch_triu(b, diagonal=1) torch_triu(b, diagonal=-1) } #> torch_tensor #> -0.4660 -0.2273 -0.6166 -1.9900 -0.7221 -0.9500 #>  1.1051 -0.7226 -0.2677  0.6145 -2.1794  0.7118 #>  0.0000 -0.8139  1.0528 -0.1026  1.3411 -0.7295 #>  0.0000  0.0000  0.2327 -0.4739 -0.6007 -0.3636 #> [ CPUFloatType{4,6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Triu_indices — torch_triu_indices","title":"Triu_indices — torch_triu_indices","text":"Triu_indices","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triu_indices — torch_triu_indices","text":"","code":"torch_triu_indices(   row,   col,   offset = 0,   dtype = NULL,   device = NULL,   layout = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triu_indices — torch_triu_indices","text":"row (int) number rows 2-D matrix. col (int) number columns 2-D matrix. offset (int) diagonal offset main diagonal.        Default: provided, 0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, torch_long. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. layout (torch.layout, optional) currently support torch_strided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Triu_indices — torch_triu_indices","text":"","code":"When running on CUDA, `row * col` must be less than \\eqn{2^{59}} to prevent overflow during calculation."},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-","dir":"Reference","previous_headings":"","what":"triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor","title":"Triu_indices — torch_triu_indices","text":"Returns indices upper triangular part row col matrix 2--N Tensor, first row contains row coordinates indices second row contains column coordinates. Indices ordered based rows columns. upper triangular part matrix defined elements diagonal. argument offset controls diagonal consider. offset = 0, elements main diagonal retained. positive value excludes just many diagonals main diagonal, similarly negative value includes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triu_indices — torch_triu_indices","text":"","code":"if (torch_is_installed()) { if (FALSE) { a = torch_triu_indices(3, 3) a a = torch_triu_indices(4, 3, -1) a a = torch_triu_indices(4, 3, 1) a } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"TRUE_divide — torch_true_divide","title":"TRUE_divide — torch_true_divide","text":"TRUE_divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRUE_divide — torch_true_divide","text":"","code":"torch_true_divide(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRUE_divide — torch_true_divide","text":"self (Tensor) dividend (Tensor Scalar) divisor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"true-divide-dividend-divisor-gt-tensor-","dir":"Reference","previous_headings":"","what":"true_divide(dividend, divisor) -> Tensor","title":"TRUE_divide — torch_true_divide","text":"Performs \"true division\" always computes division floating point. Analogous division Python 3 equivalent torch_div except inputs bool integer scalar types, case cast default (floating) scalar type division. $$     \\mbox{}_i = \\frac{\\mbox{dividend}_i}{\\mbox{divisor}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRUE_divide — torch_true_divide","text":"","code":"if (torch_is_installed()) {  dividend = torch_tensor(c(5, 3), dtype=torch_int()) divisor = torch_tensor(c(3, 2), dtype=torch_int()) torch_true_divide(dividend, divisor) torch_true_divide(dividend, 2) } #> torch_tensor #>  2.5000 #>  1.5000 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":null,"dir":"Reference","previous_headings":"","what":"Trunc — torch_trunc","title":"Trunc — torch_trunc","text":"Trunc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trunc — torch_trunc","text":"","code":"torch_trunc(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trunc — torch_trunc","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"trunc-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"trunc(input, out=NULL) -> Tensor","title":"Trunc — torch_trunc","text":"Returns new tensor truncated integer values elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trunc — torch_trunc","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_trunc(a) } #> torch_tensor #> -0 #> -0 #>  0 #>  0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":null,"dir":"Reference","previous_headings":"","what":"Unbind — torch_unbind","title":"Unbind — torch_unbind","text":"Unbind","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unbind — torch_unbind","text":"","code":"torch_unbind(self, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unbind — torch_unbind","text":"self (Tensor) tensor unbind dim (int) dimension remove","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"unbind-input-dim-gt-seq-","dir":"Reference","previous_headings":"","what":"unbind(input, dim=0) -> seq","title":"Unbind — torch_unbind","text":"Removes tensor dimension. Returns tuple slices along given dimension, already without .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unbind — torch_unbind","text":"","code":"if (torch_is_installed()) {  torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE))) } #> [[1]] #> torch_tensor #>  1 #>  2 #>  3 #> [ CPULongType{3} ] #>  #> [[2]] #> torch_tensor #>  4 #>  5 #>  6 #> [ CPULongType{3} ] #>  #> [[3]] #> torch_tensor #>  7 #>  8 #>  9 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":null,"dir":"Reference","previous_headings":"","what":"Unique_consecutive — torch_unique_consecutive","title":"Unique_consecutive — torch_unique_consecutive","text":"Unique_consecutive","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unique_consecutive — torch_unique_consecutive","text":"","code":"torch_unique_consecutive(   self,   return_inverse = FALSE,   return_counts = FALSE,   dim = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unique_consecutive — torch_unique_consecutive","text":"self (Tensor) input tensor return_inverse (bool) Whether also return indices        elements original input ended returned unique list. return_counts (bool) Whether also return counts unique        element. dim (int) dimension apply unique. NULL, unique        flattened input returned. default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Unique_consecutive — torch_unique_consecutive","text":"Eliminates first element every consecutive group equivalent elements.","code":".. note:: This function is different from [`torch_unique`] in the sense that this function     only eliminates consecutive duplicate values. This semantics is similar to `std::unique`     in C++."},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unique_consecutive — torch_unique_consecutive","text":"","code":"if (torch_is_installed()) { x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2)) output = torch_unique_consecutive(x) output torch_unique_consecutive(x, return_inverse=TRUE) torch_unique_consecutive(x, return_counts=TRUE) } #> [[1]] #> torch_tensor #>  1 #>  2 #>  3 #>  1 #>  2 #> [ CPUFloatType{5} ] #>  #> [[2]] #> torch_tensor #> [ CPULongType{0} ] #>  #> [[3]] #> torch_tensor #>  2 #>  2 #>  1 #>  2 #>  1 #> [ CPULongType{5} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsafe_chunk — torch_unsafe_chunk","title":"Unsafe_chunk — torch_unsafe_chunk","text":"Unsafe_chunk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsafe_chunk — torch_unsafe_chunk","text":"","code":"torch_unsafe_chunk(self, chunks, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsafe_chunk — torch_unsafe_chunk","text":"self (Tensor) tensor split chunks (int) number chunks return dim (int) dimension along split tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"unsafe-chunk-input-chunks-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"unsafe_chunk(input, chunks, dim=0) -> List of Tensors","title":"Unsafe_chunk — torch_unsafe_chunk","text":"Works like torch_chunk() without enforcing autograd restrictions inplace modification outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unsafe_chunk — torch_unsafe_chunk","text":"function safe use long input, outputs modified inplace calling function. user's responsibility ensure case. input one outputs modified inplace, gradients computed autograd silently incorrect.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsafe_split — torch_unsafe_split","title":"Unsafe_split — torch_unsafe_split","text":"Unsafe_split","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsafe_split — torch_unsafe_split","text":"","code":"torch_unsafe_split(self, split_size, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsafe_split — torch_unsafe_split","text":"self (Tensor) tensor split. split_size (int) size single chunk list sizes chunk dim (int) dimension along split tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors","title":"Unsafe_split — torch_unsafe_split","text":"Works like torch_split() without enforcing autograd restrictions inplace modification outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unsafe_split — torch_unsafe_split","text":"function safe use long input, outputs modified inplace calling function. user's responsibility ensure case. input one outputs modified inplace, gradients computed autograd silently incorrect.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsqueeze — torch_unsqueeze","title":"Unsqueeze — torch_unsqueeze","text":"Unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsqueeze — torch_unsqueeze","text":"","code":"torch_unsqueeze(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsqueeze — torch_unsqueeze","text":"self (Tensor) input tensor. dim (int) index insert singleton dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"unsqueeze-input-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"unsqueeze(input, dim) -> Tensor","title":"Unsqueeze — torch_unsqueeze","text":"Returns new tensor dimension size one inserted specified position. returned tensor shares underlying data tensor. dim value within range [-input.dim() - 1, input.dim() + 1) can used. Negative dim correspond unsqueeze applied dim = dim + input.dim() + 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unsqueeze — torch_unsqueeze","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3, 4)) torch_unsqueeze(x, 1) torch_unsqueeze(x, 2) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPUFloatType{4,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":null,"dir":"Reference","previous_headings":"","what":"Vander — torch_vander","title":"Vander — torch_vander","text":"Vander","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vander — torch_vander","text":"","code":"torch_vander(x, N = NULL, increasing = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vander — torch_vander","text":"x (Tensor) 1-D input tensor. N (int, optional) Number columns output. N specified, square array returned \\((N = len(x))\\). increasing (bool, optional) Order powers columns. TRUE, powers increase left right, FALSE (default) reversed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"vander-x-n-none-increasing-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"vander(x, N=None, increasing=FALSE) -> Tensor","title":"Vander — torch_vander","text":"Generates Vandermonde matrix. columns output matrix elementwise powers input vector \\(x^{(N-1)}, x^{(N-2)}, ..., x^0\\). increasing TRUE, order columns reversed \\(x^0, x^1, ..., x^{(N-1)}\\). matrix geometric progression row named Alexandre-Theophile Vandermonde.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vander — torch_vander","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(c(1, 2, 3, 5)) torch_vander(x) torch_vander(x, N=3) torch_vander(x, N=3, increasing=TRUE) } #> torch_tensor #>   1   1   1 #>   1   2   4 #>   1   3   9 #>   1   5  25 #> [ CPUFloatType{4,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Var — torch_var","title":"Var — torch_var","text":"Var","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Var — torch_var","text":"","code":"torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Var — torch_var","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"var-input-unbiased-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"var(input, unbiased=TRUE) -> Tensor","title":"Var — torch_var","text":"Returns variance elements input tensor. unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor","title":"Var — torch_var","text":"Returns variance row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Var — torch_var","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_var(a)   a = torch_randn(c(4, 4)) a torch_var(a, 1) } #> torch_tensor #>  0.1741 #>  0.8066 #>  0.8044 #>  0.2940 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Var_mean — torch_var_mean","title":"Var_mean — torch_var_mean","text":"Var_mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Var_mean — torch_var_mean","text":"","code":"torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Var_mean — torch_var_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"var-mean-input-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"var_mean(input, unbiased=TRUE) -> (Tensor, Tensor)","title":"Var_mean — torch_var_mean","text":"Returns variance mean elements input tensor. unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor)","title":"Var_mean — torch_var_mean","text":"Returns variance mean row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Var_mean — torch_var_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_var_mean(a)   a = torch_randn(c(4, 4)) a torch_var_mean(a, 1) } #> [[1]] #> torch_tensor #>  0.2780 #>  1.6386 #>  0.5056 #>  1.7292 #> [ CPUFloatType{4} ] #>  #> [[2]] #> torch_tensor #>  0.0747 #> -0.1032 #> -0.7452 #>  0.3950 #> [ CPUFloatType{4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Vdot — torch_vdot","title":"Vdot — torch_vdot","text":"Vdot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vdot — torch_vdot","text":"","code":"torch_vdot(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vdot — torch_vdot","text":"self (Tensor) first tensor dot product. conjugate used complex. (Tensor) second tensor dot product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Vdot — torch_vdot","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"vdot-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"vdot(input, other, *, out=None) -> Tensor","title":"Vdot — torch_vdot","text":"Computes dot product (inner product) two tensors. vdot(, b) function handles complex numbers differently dot(, b). first argument complex, complex conjugate first argument used calculation dot product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vdot — torch_vdot","text":"","code":"if (torch_is_installed()) {  torch_vdot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1))) if (FALSE) { a <- torch_tensor(list(1 +2i, 3 - 1i)) b <- torch_tensor(list(2 +1i, 4 - 0i)) torch_vdot(a, b) torch_vdot(b, a) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"View_as_complex — torch_view_as_complex","title":"View_as_complex — torch_view_as_complex","text":"View_as_complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View_as_complex — torch_view_as_complex","text":"","code":"torch_view_as_complex(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View_as_complex — torch_view_as_complex","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"view-as-complex-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"view_as_complex(input) -> Tensor","title":"View_as_complex — torch_view_as_complex","text":"Returns view input complex tensor. input complex tensor size \\(m1, m2, \\dots, mi, 2\\), function returns new complex tensor size \\(m1, m2, \\dots, mi\\) last dimension input tensor expected represent real imaginary components complex numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"View_as_complex — torch_view_as_complex","text":"torch_view_as_complex supported tensors torch_dtype torch_float64() torch_float32().  input expected last dimension size 2. addition, tensor must stride 1 last dimension. strides dimensions must even numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View_as_complex — torch_view_as_complex","text":"","code":"if (torch_is_installed()) { if (FALSE) { x=torch_randn(c(4, 2)) x torch_view_as_complex(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":null,"dir":"Reference","previous_headings":"","what":"View_as_real — torch_view_as_real","title":"View_as_real — torch_view_as_real","text":"View_as_real","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View_as_real — torch_view_as_real","text":"","code":"torch_view_as_real(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View_as_real — torch_view_as_real","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"view-as-real-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"view_as_real(input) -> Tensor","title":"View_as_real — torch_view_as_real","text":"Returns view input real tensor. input complex tensor size \\(m1, m2, \\dots, mi\\), function returns new real tensor size \\(m1, m2, \\dots, mi, 2\\), last dimension size 2 represents real imaginary components complex numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"View_as_real — torch_view_as_real","text":"torch_view_as_real() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View_as_real — torch_view_as_real","text":"","code":"if (torch_is_installed()) {  if (FALSE) { x <- torch_randn(4, dtype=torch_cfloat()) x torch_view_as_real(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Vstack — torch_vstack","title":"Vstack — torch_vstack","text":"Vstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vstack — torch_vstack","text":"","code":"torch_vstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vstack — torch_vstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"vstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"vstack(tensors, *, out=None) -> Tensor","title":"Vstack — torch_vstack","text":"Stack tensors sequence vertically (row wise). equivalent concatenation along first axis 1-D tensors reshaped torch_atleast_2d().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vstack — torch_vstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_vstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_vstack(list(a,b)) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #>  5 #>  6 #> [ CPUFloatType{6,1} ]"},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Where — torch_where","text":"","code":"torch_where(condition, self = NULL, other = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Where — torch_where","text":"condition (BoolTensor) TRUE (nonzero), yield x, otherwise yield y self (Tensor) values selected indices condition TRUE (Tensor) values selected indices condition FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Where — torch_where","text":"See also torch_nonzero().","code":"The tensors `condition`, `x`, `y` must be broadcastable ."},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"where-condition-x-y-gt-tensor-","dir":"Reference","previous_headings":"","what":"where(condition, x, y) -> Tensor","title":"Where — torch_where","text":"Return tensor elements selected either x y, depending condition. operation defined : $$     \\mbox{}_i = \\left\\{ \\begin{array}{ll}         \\mbox{x}_i & \\mbox{} \\mbox{condition}_i \\\\         \\mbox{y}_i & \\mbox{otherwise} \\\\     \\end{array}     \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"where-condition-gt-tuple-of-longtensor-","dir":"Reference","previous_headings":"","what":"where(condition) -> tuple of LongTensor","title":"Where — torch_where","text":"torch_where(condition) identical torch_nonzero(condition, as_tuple=TRUE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Where — torch_where","text":"","code":"if (torch_is_installed()) {  if (FALSE) { x = torch_randn(c(3, 2)) y = torch_ones(c(3, 2)) x torch_where(x > 0, x, y) }    }"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros — torch_zeros","title":"Zeros — torch_zeros","text":"Zeros","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros — torch_zeros","text":"","code":"torch_zeros(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros — torch_zeros","text":"... sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional dimension names dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Zeros — torch_zeros","text":"Returns tensor filled scalar value 0, shape defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros — torch_zeros","text":"","code":"if (torch_is_installed()) {  torch_zeros(c(2, 3)) torch_zeros(c(5)) } #> torch_tensor #>  0 #>  0 #>  0 #>  0 #>  0 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros_like — torch_zeros_like","title":"Zeros_like — torch_zeros_like","text":"Zeros_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros_like — torch_zeros_like","text":"","code":"torch_zeros_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros_like — torch_zeros_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Zeros_like — torch_zeros_like","text":"Returns tensor filled scalar value 0, size input. torch_zeros_like(input) equivalent torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Zeros_like — torch_zeros_like","text":"0.4, function support keyword. alternative, old torch_zeros_like(input, =output) equivalent torch_zeros(input.size(), =output).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros_like — torch_zeros_like","text":"","code":"if (torch_is_installed()) {  input = torch_empty(c(2, 3)) torch_zeros_like(input) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":null,"dir":"Reference","previous_headings":"","what":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"two things:","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"","code":"with_detect_anomaly(code)"},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"code Code executed detect anomaly context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"Running forward pass detection enabled allow backward pass print traceback forward operation created failing backward function. backward computation generate \"nan\" value raise error.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"mode enabled debugging different tests slow program execution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, requires_grad = TRUE) y <- torch_randn(1) b <- (x^y)$sum() y$add_(1)  try({   b$backward()    with_detect_anomaly({     b$backward()   }) }) } #> Error in (function (self, inputs, gradient, retain_graph, create_graph)  :  #>   one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). #> Exception raised from unpack at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/saved_variable.cpp:194 (most recent call first): #> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 81 (0x10404eca1 in libc10.dylib) #> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 98 (0x10404d342 in libc10.dylib) #> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 2815 (0x124574d0f in libtorch_cpu.dylib) #> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 168 (0x1236c7488 in libtorch_cpu.dylib) #> frame #4: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 99 (0x1245456c3 in libtorch_cpu.dylib) #> frame #5: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&) + 3401 (0x12453ba89 in libtorch_cpu.dylib) #> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 954 (0x12453a6da in libtorch_cpu.dylib) #> frame #7: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr<torch::autograd::GraphTask> const&, std::__1::shared_ptr<torch::autograd::Node>, torch::autograd::InputBuffer&&) + 374 (0x124544646 in libtorch_cpu.dylib) #> frame #8: torch::autograd::Engine::execute(std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool, bool, std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge>> const&) + 2605 (0x12454268d in libtorch_cpu.dylib) #> frame #9: torch::autograd::run_backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool) + 2129 (0x124528df1 in libtorch_cpu.dylib) #> frame #10: torch::autograd::backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, c10::optional<bool>, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&) + 104 (0x124529408 in libtorch_cpu.dylib) #> frame #11: torch::autograd::VariableHooks::_backward(at::Tensor const&, c10::ArrayRef<at::Tensor>, c10::optional<at::Tensor> const&, c10::optional<bool>, bool) const + 435 (0x12457a9f3 in libtorch_cpu.dylib) #> frame #12: at::Tensor::_backward(c10::ArrayRef<at::Tensor>, c10::optional<at::Tensor> const&, c10::optional<bool>, bool) const + 75 (0x120ebbeab in libtorch_cpu.dylib) #> frame #13: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 427 (0x1068a3b2b in liblantern.dylib) #> frame #14: std::__1::__function::__func<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_1, std::__1::allocator<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_1>, void ()>::operator()() + 54 (0x10513fbf6 in torchpkg.so) #> frame #15: std::__1::packaged_task<void ()>::operator()() + 72 (0x10513df88 in torchpkg.so) #> frame #16: EventLoop<void>::run() + 379 (0x10513ddbb in torchpkg.so) #> frame #17: void* std::__1::__thread_proxy[abi:v160006]<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, ThreadPool<void>::ThreadPool(int)::'lambda'()>>(void*) + 50 (0x10513db32 in torchpkg.so) #> frame #18: _pthread_start + 125 (0x7ff805e541d3 in libsystem_pthread.dylib) #> frame #19: thread_start + 15 (0x7ff805e4fbd3 in libsystem_pthread.dylib) #>"},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable grad — with_enable_grad","title":"Enable grad — with_enable_grad","text":"Context-manager enables gradient calculation. Enables gradient calculation, disabled via with_no_grad.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable grad — with_enable_grad","text":"","code":"with_enable_grad(code)  local_enable_grad(.env = parent.frame())"},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable grad — with_enable_grad","text":"code code executed gradient recording. .env environment use scoping.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enable grad — with_enable_grad","text":"context manager thread local; affect computation threads.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Enable grad — with_enable_grad","text":"local_enable_grad(): Locally enable gradient computations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable grad — with_enable_grad","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(1, requires_grad = TRUE) with_no_grad({   with_enable_grad({     y <- x * 2   }) }) y$backward() x$grad } #> torch_tensor #>  2 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Temporarily modify gradient recording. — with_no_grad","title":"Temporarily modify gradient recording. — with_no_grad","text":"Temporarily modify gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Temporarily modify gradient recording. — with_no_grad","text":"","code":"with_no_grad(code)  local_no_grad(.env = parent.frame())"},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Temporarily modify gradient recording. — with_no_grad","text":"code code executed gradient recording. .env environment use scoping.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Temporarily modify gradient recording. — with_no_grad","text":"local_no_grad(): Disable autograd goes scope","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Temporarily modify gradient recording. — with_no_grad","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(runif(5), requires_grad = TRUE) with_no_grad({   x$sub_(torch_tensor(as.numeric(1:5))) }) x x$grad } #> torch_tensor #> [ Tensor (undefined) ]"},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0130","dir":"Changelog","previous_headings":"","what":"torch 0.13.0","title":"torch 0.13.0","text":"CRAN release: 2024-05-21","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-13-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.13.0","text":"lantern now distributed different URL (https://torch-cdn.mlverse.org). users shouldn’t effect, unless need special authorization access URL’s. (#1162)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-13-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.13.0","text":"Added support private $finalize_deep_clone() method nn_module allows run code cloning module. compare_proxy method torch_tensor type added allows compare torch tensors using testthat::expect_equal(). Converting torch tensor R array works tensor ‘cuda’ device (#1130)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-13-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.13.0","text":"Fix bug using input projection initialization bias nnf_multi_head_attention_forward (#1154 @cregouby) Bugfix: calling $detach() tensor now preserves attributes (#1136) Make sure deep cloning tensor nn_module preserves class attributes requires_grad field. (#1129) Fixed parameters buffers children nn_modules cloned Cloned objects longer reference object cloned Fixed bug nn_module’s patched clone method invalid call internal create_nn_module_callable() Printing grad_fn now appends new line end. Make sure deep cloning preserve state dict attributes. (#1129) Added separate setter unsetter autocast context instead allowing local_autocast(). (#1142) Fixed bug torch_arange() causing return 1:(n-1) values specific request dtype = torch_int64() (#1160)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0120","dir":"Changelog","previous_headings":"","what":"torch 0.12.0","title":"torch 0.12.0","text":"CRAN release: 2023-12-15","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-12-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.12.0","text":"New torch_save serialization format. ’s ~10x faster since ’s based safetensors, files can read safetensors implementation. (#1071) Updated LibTorch 2.0.1. (#1085) torch_load longer supports device=NULL load weights device saved. (#1085) Lantern binaries torch pre-built binaries now built Ubuntu 20.04. (#1124)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-12-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.12.0","text":"Added support CUDA 11.8. (#1089) Added support iterable datasets. (#1095)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-12-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.12.0","text":"fix printer torch device (add new line end) .array now moves tensors cpu copying data R. (#1080) Fixed segfault caused comparing dtype NULL. (#1090) Fixed incorrect naming complex data type names, torch_cfloat64. (#1091) Fixed name out_features attribute nn_linear module. (#1097) Fixed issues loading state dict optimizers learning rate schedulers. (#1100) Fixed bug cloning nn_modules empty state dicts. (#1108) distr_multivariate_normal now correctly handles precision matrix’s. (#1110) Moved length.torch_tensor implementation R7 avoid problems torch dataset torch_tensor class. (#1111) Fixed problem deep cloning nn_module. (#1123)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0110","dir":"Changelog","previous_headings":"","what":"torch 0.11.0","title":"torch 0.11.0","text":"CRAN release: 2023-06-06","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-11-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.11.0","text":"load_state_dict() optimizers now default cloning tensors state dict, don’t keep references objects dict. (#1041)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-11-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.11.0","text":"Added nn_utils_weight_norm (#1025) Added support reading ordered state dicts serialized PyTorch. (#1031) Added jit_ops allowing access JIT operators. (#1023) Added with_device local_device allow temporarily modify default device tensors get initialized. (#1034) nnf_gelu() nn_gelu() gained approximate argument. (#1043) Implemented != torch devices. (#1042) Allows setting dtype string. (#1045) can now create named list modules using nn_module_dict(). (#1046) Faster load_state_dict(), also using less memory. ’ possible use legacy implementation required, see PR. (#1051) Export helpers handling RNG state, temprarily modifying . (#1057) Added support converting half tensors R .numeric(). (#1056) Added new torch_tensor_from_buffer() buffer_from_torch_tensor() allow low level creation torch tensors. (#1061, #1062)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-11-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.11.0","text":"Improved documentation LBFGS optimizer. (#1035) Added message asking user restart session manual installation install_torch(). (#1055)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-11-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.11.0","text":"Fixed bug related handling non-persistent buffers. get added state_dict() even . (#1036) Fixed typo optim_adamw class name. Fixed nn_cross_entropy_loss class name. (#1043) Fixed bug LBFGS w/ line search. (#1048) Correctly handle installation RemoteSha package version. (#1058)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-11-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.11.0","text":"Started building LibLantern macOS 11 instead macOS12 maximum compatibility. (#1026) Added CXXSTD Makevars enable C+11 compilation options. Refactored codepath TensorOptions now tensors initialization handled codepath. (#1033) Added internal argument .refer_to_state_dict load_state_dict() nn_module() method. Allows loading state dict model keeping parmaters references state dict. (#1036)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0100","dir":"Changelog","previous_headings":"","what":"torch 0.10.0","title":"torch 0.10.0","text":"CRAN release: 2023-04-13","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-10-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.10.0","text":"Updated LibTorch v1.13.1 (#977)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-10-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.10.0","text":"Provide pre-built binaries torch using GH Action workflow. (#975) Added nn_silu() nnf_silu(). (#985) Added support deep cloning nn_modules. (#986) Added local_no_grad() local_enable_grad() alternatives with_ functions. (#990) Added optim_adamw optimizer. (#991) Added support automatic mixed precision (#996) Added functionality temporarily modify torch seed. (#999) Support creating torch tensors raw vectors back. (#1003)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-10-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.10.0","text":"Dataloaders now preserve batch dimension batch_size=1 used. (#994)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-10-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.10.0","text":"Large refactoring build system. (#964) Use native symbol registration instead dynamic lookup. (#976) Returning lists tensors R now much faster. (#993)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-091","dir":"Changelog","previous_headings":"","what":"torch 0.9.1","title":"torch 0.9.1","text":"CRAN release: 2023-01-23","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-9-1","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.9.1","text":"torch_where now returns 1-based indices ’s called condition argument . (#951, @skeydan)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-9-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.9.1","text":"Added support nonABI builds CUDA 11.6. (#919) torch_fft_fftfreq() function now exported. (#950, @skeydan)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-9-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.9.1","text":"Fixed bug caused distr_normal$sample() able generate reproducible results setting seeds. (#938) torch_cat error message now correctly reflects 1-based indexing. (#952, @skeydan)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-9-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.9.1","text":"Fixed warnings R CMD Check generated unsafe use sprintf. (#959, @shikokuchuo) Import, suggest glue (#960)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-090","dir":"Changelog","previous_headings":"","what":"torch 0.9.0","title":"torch 0.9.0","text":"CRAN release: 2022-10-24","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-9-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.9.0","text":"Updated LibTorch v1.12.1. (#889, #893, #899) torch_bincount now 1-based indexed. (#896) torch_movedim() $movedim() now 1-based indexed. (#905)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-9-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.9.0","text":"Added cuda_synchronize() allow synchronization CUDA operations. (#887) Added support M1 Macs, including creating Tensors MPS device. (#890) Added support CUDA 11.6 Linux. (#902) Added cuda_empty_cache() allow freeing memory caching allocator system. (#903) Added $is_sparse() method check wether Tensor sparse . (#903) dataset_subset now adds class modified dataset original dataset classes postfixed _subset. (#904) Added torch_serialize() allow creating raw vector torch objects. (#908)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.9.0","text":"Fixed bug torch_arange causing end value getting included result. (#885, @skeydan) Fixed bug window functions setting default dtype. (#886, @skeydan) Fixed bug using install_torch(reinstall = TRUE). (#883) dims argument torch_tile() longer modified, ’s meant 1-based dimension. (#905) nn_module$state_diict() now detaches output tensors default. (#916)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-9-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.9.0","text":"Re-implemented $ method R7 classes C/C++ improve speed calling methods. (#873) Re-implemented garbage collection logic calling inside backward() call. improves speed longer need call GC everytime backward called. (#873) now use thread pool instead launching new thread backward calls. (#883) Implemented options allow configuring activation garbage collection allocating CUDA memory. (#883) nnf_ functions updated use single torch_ kernel instead custom implementation. (#896) Improved performance dataloaders. (#900) now let LibTorch query default generator, allows one use torch_bernoulli() device=\"gpu\". (#906)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-081","dir":"Changelog","previous_headings":"","what":"torch 0.8.1","title":"torch 0.8.1","text":"CRAN release: 2022-08-19","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-8-1","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.8.1","text":"now prompt user installing torch additional dependencies interactive environments. requested CRAN maintainers. (#864)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-8-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.8.1","text":"Dataloaders can now handle logical values. (#858, @ryan-heslin) now provide builds Pre CXX11 ABI version LibTorch. can used setting environment variable PRECXX11ABI=1. can useful environments older versions GLIBC. (#870)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-8-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.8.1","text":"Fixed way errors passed dataloaders workers main process. Now using new rlang error chaining. (#864)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-8-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.8.1","text":"can now call GC even backward call (ie, different thread) allows better memory management. (#853) Fix HTML5 Manual information resquested CRAN (#869)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-080","dir":"Changelog","previous_headings":"","what":"torch 0.8.0","title":"torch 0.8.0","text":"CRAN release: 2022-06-09","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.8.0","text":"Serialization now much faster avoid base64 encoding serialized tensors. result, files serialized newer versions torch can’t opened older versions torch. Set options(torch.serialization_version = 1) want file readable older versions. (#803) Deprecated support CUDA 10.2 Windows. (#835) linalg_matrix_rank linalg_pinv gained atol rtol arguments deprecating tol rcond. 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(#841)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-072","dir":"Changelog","previous_headings":"","what":"torch 0.7.2","title":"torch 0.7.2","text":"CRAN release: 2022-02-25","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fix-0-7-2","dir":"Changelog","previous_headings":"","what":"Bug fix","title":"torch 0.7.2","text":"Fixed vignette building Windows.","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-071","dir":"Changelog","previous_headings":"","what":"torch 0.7.1","title":"torch 0.7.1","text":"CRAN release: 2022-02-25","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-7-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.7.1","text":"Added cuda_runtime_version() query CUDA Tolkit version torch using. (#790)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-070","dir":"Changelog","previous_headings":"","what":"torch 0.7.0","title":"torch 0.7.0","text":"CRAN release: 2022-02-18","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.7.0","text":"torch_sort Tensor$sort now return 1-indexed results. (#709, @mohamed-180) Support LibTorch 1.10.2. See also release notes PyTorch v1.10. (#758, #763, #775, @hsbadr). Changed default dim 1 2 nnf_cosine_similarity. (#769) default value arguments various functions changed. bug code generation truncating default values specially float values needed 6 digit precision. (#770)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-7-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.7.0","text":"jit_save_for_mobile allows save traced model bytecode form, loaded LiteModuleLoader. (#713) Exported is_torch_tensor check wether object tensor . (#730, @rdinnager) Adds cuda_get_device_properties(device) allows one query device capability properties. (#734, @rdinnager) Implemented call_torch_function() allow calling potentially unexported torch core functions. (#743, @rdinnager) Now installing torch LibTorch Lantern headers installed within inst directory. allow packages extending torch bind directly C++ library. (#718) dataset_subset use .getbatch method wrapped dataset one available. (#742, @egillax) Added nn_flatten nn_unflatten modules. (#773) Added cuda_memory_stats() cuda_memory_summary() verify amount memory torch using GPU. 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(#723).","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-060","dir":"Changelog","previous_headings":"","what":"torch 0.6.0","title":"torch 0.6.0","text":"CRAN release: 2021-10-07","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.6.0","text":"nn_sequential now bare nn_module, allowing easily inherit . breaking change used name argument. name behavior can achieved subclassing; see tests PR. (#699)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.6.0","text":"Additional info showed printing tensors like requires grad grad fn. (#668, #669, #673, @mohamed-180) can now subset nn_sequential modules using [. (#678, @mohamed-180) now allow padding='' padding='valid' using convolutions. 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(#698)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-050","dir":"Changelog","previous_headings":"","what":"torch 0.5.0","title":"torch 0.5.0","text":"CRAN release: 2021-08-17","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.5.0","text":"Droped support CUDA 10.1 (#610) torch_manual_seed() now matches PyTorch’s behavior can easily compare implementations. Since breaking change added torch.old_seed_behavior=TRUE option users can stick old behavior. (#639) Indexing vectors now behavior R indexing, making easier understand. Users can still use old behavior using torch_index torch_index_put. (#649)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.5.0","text":"Added support ScriptModule. Loaded JIT modules now operate nn_modules. (#593) Added jit_compile function allows compiling arbitrary TorchScript code script function can serialized executed. (#601) Added jit_trace support nn_module created R. (#604) Updated LibTorch version 1.9.0 (#610) Added Linear Algebra functions (#612) Added contrib_sort_vertices efficiently sort vertices CUDA. (#619) Allows querying graph traced modules. (#623) Added with_detect_anomaly debug autograd errors. (#628) Implemented traced_module$graph_for() allow inspecting optimized jit graph. (#643) Added slc allow dynamically creating slices indexing tensors. (#648)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.5.0","text":"Fixed bug using .getbatch method didn’t return torch_tensor. (#615) Fixed warning using %/% caused call deprecated torch_floor_divide (#616) Improved CUDA version auto-detection (#644)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.5.0","text":"Improved R <-> JIT types conversion. (#593) Added Dockerfiles CUDA 11.1 (#597) warning raised incompatible dataset passed parallel dataloader. (#626) Additionally calling gc CUDA memory exhausted now call R_RunPendingFinalizers. improve memory usage, now delete tensors earlier. (#654) Fix rchk issues (#667)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-040","dir":"Changelog","previous_headings":"","what":"torch 0.4.0","title":"torch 0.4.0","text":"CRAN release: 2021-06-10","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.4.0","text":"torch_multinomial now returns 1-based indexes comply 1-based indexing across torch. (#588)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.4.0","text":"Added parameter multihead attention module allow output unaveraged attention weights. (@jonathanbratt #542) now allow jit_trace functions 1 argument. (#544) Added Multivariate normal distribution (#552) Export torch_diff function added docs . (#565) Added device argument torch_load() allowing one select device parameters loaded. (#578) Added distr_categorical() (#576) Added distr_mixture_same_family() (#576) Improve handling optimizers state implement load_state_dict() state_dict() optimizers. (#585) Added ability save R lists containing torch_tensors using torch_save. allows us save state optimizers modules using torch_save(). (#586)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.4.0","text":"Fixed bug nn_multihead_attention q,k,v inputs . (@jonathanbratt #540) Fixed $copy_ correctly respects src requires_grad() reloading saved models torch_load(). (#545) Fixed nn_init_xavier_normal_() nn_init_xavier_uniform_() standard deviation calculation. (#557) Fixed bug torch_tensordot() called infering dimensions. (#563) Dataset’s .getbatch now takes integer vector input instead list(). (#572) Fixed bug tensor$size() indexing negative numbers. (#570) Fixed bug log_prob distr_bernoulli() (#581)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.4.0","text":"Better handling optional Tensor arguments using explicit XPtrTorchOptionalTensor class. (#565) Tensors R side point C++ Tensor now guaranteed object. allows easily determine unique model parameters. (#582)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-030","dir":"Changelog","previous_headings":"","what":"torch 0.3.0","title":"torch 0.3.0","text":"CRAN release: 2021-04-28","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.3.0","text":"torch_nonzero tensor$nonzero() now return 1-based indexes. (#432) Breaking change: torch_arange returns closed interval [start, end] instead half open [start, end). makes behave similar R’s seq. (#506)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.3.0","text":"torch_split now accepts list sizes well fixed size. (#429) Added nn_layer_norm. (#435) Allow timeout=360 install_torch() parameter large file download (@cregouby #438) Added install_torch_from_file() get_install_libs_url()setup cases direct download possible (@cregouby #439) Added mean.torch_tensor (#448) New arguments worker_globals worker_packages allowing easily pass objects workers parallel dataloaders (#449). now call R garbage collector ’s memory available GPU, can help cases laziness garbage collector allows many tensors memory even though longer referenced R. (#456) Implemented nn_group_norm fixed bug nnf_group_norm (#474) Added backend functions allowing us query optimizations LibTorch compiled (#476) Added normal distribution (#462) Added bernoulli distribution (#484) .list nn_modules (#492) Enumerate support Bernoulli distribution (#490) Added Poisson Distriibution (#495) Allow optional .getbatch datasets/dataloaders (#498) nn_lstm, nn_gru nn_gru can now use cudnn accelerations available (#503). Added Gamma distribution (#489) now respect TORCH_HOME env var automatically install torch. (#522) Implement comparison operator != torch dtypes. (#524) Added Chi-square distribution. (#518) Added optimizer function allowing easily implement custom optimizers. (#527)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.3.0","text":"Fixed bug optim_lbfgs make model objects exponentially big. (#431) Correctly handle NaNs L-BFGS optimizer (#433) default collate function now respects data type converting tensor (dataset returns R object) (#434) Fixed torch_normal. (#450) Fixed backward compatibility issue loading models saved older versions torch. bug introduced #452 now fixed also added regression test. (#458) Fixed bug using RNN’s GPU (#460) Found fixed memory leaks, specially creating datatypes strings saving models torch_save. (#454) Fixed bug nnf_pad using mode='circular'. (#471) Bugfixes nn_multihead_attention (#496) Fixed bug using packed sequences nn_lstm (#500) Fixed bug method nn_module reset requires_grad attribute parameters. (#501) Added strong_wolfe option optim_lbfgs. (#517) Fixed default argument nn_init_trunc_normal_ initializer function. (#535)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-3-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.3.0","text":"Added vignette reading models Python (#469)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.3.0","text":"Removed PerformanceReporter tests get easier read stack traces. (#449) Internal change R7 classes R7 objects simple external pointer instead environments. might cause breaking change relied saving kind state Tensor object. (#452) Internal refactoring making Rcpp aware XPtrTorch* types making simpler return Rcpp code. might cause breaking change relying torch_dtype() R6 class. (#451) Internal changes auto unwrap arguments SEXP’s Rcpp. make easier move dispatcher system C++ future, already allows us gain ~30% speedups small operations. (#454) Added Windows GPU CI workflow (#508). Update LibTorch v1.8 (#513) Moved parts dispatcher C++ make faster. (#520)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-021","dir":"Changelog","previous_headings":"","what":"torch 0.2.1","title":"torch 0.2.1","text":"CRAN release: 2021-01-05","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-2-1","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.2.1","text":"Made torch_one_hot nnf_one_hot use 1-based indexing. (#410) nn_module$eval() nn_module$train() now return callable nn_module instead nn_Module. (#425)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-2-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.2.1","text":"Added custom CPU allocator call gc torch might need memory (#402) Updated LibTorch 1.7.1 (#412) Allow listing nested modules nn_module (#417) Allow modifying requires_grad attribute using $<- operator (#419) Added length method nn_sequential container. (#423) Added support CUDA 11 linux (#424)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-2-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.2.1","text":"Fix support cuda 9.2 (#398) Fixed GPU CI skipping tests. (#398) Fixed memory leak printing tensors (#402) Fixed memory leak passing integer vectors lantern. (#402) Fixed memory leaks related autograd context (#405) Fixed nnf_normalize x$norm() able called (#409)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-2-1","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.2.1","text":"Small improvement nn_module documentation (#399). getting started section removed pkgdown website favor new guide landing page (#401) Updated landing page include getting started tutorial (#400)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-020","dir":"Changelog","previous_headings":"","what":"torch 0.2.0","title":"torch 0.2.0","text":"CRAN release: 2020-12-15","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.2.0","text":"Dataloaders now returns coro::exhausted intead raising stop_iteration_error dataloader exceeds. (#366) Fixed bug happen functions need transform tensors 0-based 1-based GPU. (#317) Fixed torch_argsort x$argsort return 1-based indexes (#342) Fixed torch_argmax, torch_argmin, x$argmax() x$argmin() return 1-based indexes. (#389)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.2.0","text":"Added $element_size() method (@dirkschumacher #322) Added $bool() method (@dirkschumacher #323) torch__addr torch__addr_ removed longer available LibTorch 1.7. now check MD5 hashes downloaded LibTorch binaries. (@dirkschumacher #325) Added Distribution abstract class (@krzjoa #333) Updated LibTorch 1.7 (#337) now warn converting long tensors R ’s chance integer overflow. (#347) Allow private active methods nn_module’s dataset’s. (#349) Added nn_batch_norm3d (@mattwarkentin #354) Added nn_lstm nn_gru modules. (#362) Added distribution constraints (@krzjoa #364) Dataloaders now use num_workers argument load data parallel (#366) Added Exponential Family classs distributions (#373) Added Dockerfile docker compose file GPU support, -guide. (#380 #386) Added R 3.6 CI system fixed compilation source Windows (#387) Initial support JIT tracing (#377) Added LBFGS optimizer (#392) Improved nn_module UI improving autocomplete support adding print method (#391)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.2.0","text":"Fixed bug trying print grad_fn Tensor doesn’t one. See (#321) Refactored optimizers code avoid duplication parameter checks, etc. (@dirkschumacher #328) Fixed torch_norm can called dim argument. (#345) Fixed crash calling torch_hann_window invalid NULL window_length. (#351) Fixed torch_stft calls LibTorch 1.7 (added return_complex argument) (#355) Fixed bug strides NULL pooling operations. (#361) Use nvcc --version instead nvidia-smi find CUDA version nvidia-smi reports latest supported version installed one. (#363) Corrected URL download LibTorch Linux CUDA 10.2 (#367) Fixed handling integer tensors indexing tensors (#385) Fixed bug passing length zero vectors lantern/libtorch. (#388)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-011","dir":"Changelog","previous_headings":"","what":"torch 0.1.1","title":"torch 0.1.1","text":"CRAN release: 2020-10-20","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-1-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.1.1","text":"Fixed bug made RandomSampler(replacement = TRUE) never take last element dataset. (84861fa) Fixed torch_topk x$topk returned indexes 1-based (#280) Fixed bug (#275) cause 1 - torch_tensor(1, device = \"cuda\") fail 1 created CPU. (#279) now preserve names dataloader output (#286) torch_narrow, Tensor$narrow() Tensor$narrow_copy now indexed starting 1. (#294) Tensor$is_leaf now active method. (#295) Fixed bug passing equations torch_einsum. (#296) Fixed nn_module_list() correctly name added modules, otherwise returned state_dict() . (#300) Fixed bug related random number seeds using -place methods. (#303) Fixed nn_batchnorm* returns results PyTorch (#302) Fixed bug made nn_module$parameter shared parameters layers. (#306) Fixed $max $min return 1-based indexes. (#315)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-1-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.1.1","text":"Expanded utils_data_default_collate support converting R objects torch tensors needed. (#269) Added .matrix method torch Tensors. (#282) default now truncate output print(totrch_tensor(1:40)) spans 30 lines. useful spamming console taking long print print large tensor. (#283) Added Adadelta optimizer (@krzjoa #284) Added support GPU’s Windows (#281) Added Adagrad optimizer (@krzjoa #289) Added RMSprop optimizer (@krzjoa #290) Added Rprop optimizer (@krzjoa #297) Added gradient clipping utilities (#299) Added nnf_contrib_sparsemax nn_contrib_sparsemax. (#309) Added ASGD optimizer (@krzjoa #307) Getters setters number threads used torch (#311)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-010","dir":"Changelog","previous_headings":"","what":"torch 0.1.0","title":"torch 0.1.0","text":"CRAN release: 2020-09-28 Added many missing losses (#252) Implemented $<- [[<- operators nn_module class. (#253) Export nn_parameter, nn_buffer, is_* auxiliary functions. Added new serialization vignette. Added learning rate schedulers (#258)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-002","dir":"Changelog","previous_headings":"","what":"torch 0.0.2","title":"torch 0.0.2","text":"CRAN release: 2020-08-31 Added NEWS.md file track changes package. Auto install loading package first time.","code":""}]
+[{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to torch","title":"Contributing to torch","text":"Many thanks wanting contribute torch! welcome kinds contributions, correcting typos via bug fixes feature additions.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to torch","text":"fix typos, spelling mistakes, grammatical errors, need make changes source .R file question, generated .Rd file. Thereafter, run tools/document.R regenerate documentation. (See Workflow .)","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"filing-bugs","dir":"","previous_headings":"","what":"Filing bugs","title":"Contributing to torch","text":"find bug torch, please open issue . Please provide detailed information reproduce bug. great also provide reprex.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"feature-requests","dir":"","previous_headings":"","what":"Feature requests","title":"Contributing to torch","text":"Feel free open issues , add feature-request tag. Also try searching ’s already open issue feature-request – case ’s better comment upvote instead opening new one.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Contributing to torch","text":"welcome contributed examples. Feel free open PR new examples. examples placed vignettes/examples folder. examples consist .R file .Rmd name just renders code. See mnist-mlp.R mnist-mlp.Rmd example. ’s important one can run example without manually downloading dataset/file. also add entry _pkgdown.yaml file example.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"code-contributions","dir":"","previous_headings":"","what":"Code contributions","title":"Contributing to torch","text":"many open issues github repo. ’s one item want work , can comment ask directions.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Code contributions","what":"Code style","title":"Contributing to torch","text":"New R code follow tidyverse style guide. can use styler package apply styles, simply run tools/style.sh script, also formats code removes whitespace. Please don’t re-style code nothing PR. New C/C++ code follow Google style guide. can use clang-format apply styles, simply run tools/style.sh, also formats code removes whitespace. Please don’t re-style code nothing PR. use roxygen2, Markdown syntax, build documentation package. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"requirements","dir":"","previous_headings":"Code contributions","what":"Requirements","title":"Contributing to torch","text":"R installation R Tools compilation (Windows) devtools package CMake compile lantern binaries","code":""},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"workflow","dir":"","previous_headings":"Code contributions","what":"Workflow","title":"Contributing to torch","text":"use devtools toolchain development, steps must done upfront. first time clone repository, run: first download LibTorch copy binaries deps folder working directory, , compile lantern binaries . command must run everytime modify lantern code, .e., code lives lantern/src. can run load torch, test interactively. Alternatively, run execute test suite. Finally, ’s important update documentation. Please always use custom tools/document.R script instead devtools::document(), need patch roxygen2 avoid running examples CRAN. ’re looking forward contributions!","code":"source(\"tools/buildlantern.R\") devtools::load_all() devtools::test()"},{"path":"https://torch.mlverse.org/docs/CONTRIBUTING.html","id":"known-issues","dir":"","previous_headings":"Code contributions","what":"Known Issues","title":"Contributing to torch","text":"running tests twice (e.g. via devtools::test()) cause segfault, see #1218 discussion.","code":""},{"path":"https://torch.mlverse.org/docs/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Daniel Falbel Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/amp.html","id":"example","dir":"Articles","previous_headings":"","what":"Example","title":"Automatic Mixed Precision","text":"define model random training data showcase enabled AMP torch: Now let’s define model: train model without mixed precision can : enabled step 1. mixed precision, ie, allowing model run operations half precision possible, one simply run model computations (including loss computation) inside with_autocast context. additionally enable gradient scaling now introduce cuda_amp_grad_scaler() object use scale loss calling backward() also use wrap calls optimizer, can ‘unscale’ gradients actually updating weights. training loop now implemented :","code":"batch_size <- 512 # Try, for example, 128, 256, 513. in_size <- 4096 out_size <- 4096 num_layers <- 3 num_batches <- 50 epochs <- 3  # Creates data in default precision. # The same data is used for both default and mixed precision trials below. # You don't need to manually change inputs' dtype when enabling mixed precision. data <- lapply(1:num_batches, function(x) torch_randn(batch_size, in_size, device=\"cuda\")) targets <- lapply(1:num_batches, function(x) torch_randn(batch_size, out_size, device=\"cuda\")) make_model <- function(in_size, out_size, num_layers) {   layers <- list()   for (i in seq_len(num_layers-1)) {     layers <- c(layers, list(nn_linear(in_size, in_size), nn_relu()))   }   layers <- c(layers, list(nn_linear(in_size, out_size)))   nn_sequential(!!!layers)$cuda() } loss_fn <- nn_mse_loss()$cuda() net <- make_model(in_size, out_size, num_layers) opt <- optim_sgd(net$parameters, lr=0.1)  for (epoch in seq_len(epochs)) {   for (i in seq_along(data)) {          output <- net(data[[i]])     loss <- loss_fn(output, targets[[i]])        loss$backward()     opt$step()     opt$zero_grad()   } } loss_fn <- nn_mse_loss()$cuda() net <- make_model(in_size, out_size, num_layers) opt <- optim_sgd(net$parameters, lr=0.1)  for (epoch in seq_len(epochs)) {   for (i in seq_along(data)) {     with_autocast(device_type = \"cuda\", {       output <- net(data[[i]])       loss <- loss_fn(output, targets[[i]])       })          loss$backward()     opt$step()     opt$zero_grad()   } } loss_fn <- nn_mse_loss()$cuda() net <- make_model(in_size, out_size, num_layers) opt <- optim_sgd(net$parameters, lr=0.1) scaler <- cuda_amp_grad_scaler()  for (epoch in seq_len(epochs)) {   for (i in seq_along(data)) {     with_autocast(device_type = \"cuda\", {       output <- net(data[[i]])       loss <- loss_fn(output, targets[[i]])       })          scaler$scale(loss)$backward()     scaler$step(opt)     scaler$update()     opt$zero_grad()   } }"},{"path":"https://torch.mlverse.org/docs/articles/amp.html","id":"benchmark","dir":"Articles","previous_headings":"","what":"Benchmark","title":"Automatic Mixed Precision","text":"now write simple function allows us quickly switch feature can benchmark AMP:","code":"run <- function(autocast, scale) {   loss_fn <- nn_mse_loss()$cuda()   net <- make_model(in_size, out_size, num_layers)   opt <- optim_sgd(net$parameters, lr=0.1)   scaler <- cuda_amp_grad_scaler(enabled = scale)      for (epoch in seq_len(epochs)) {     for (i in seq_along(data)) {       with_autocast(enabled = autocast, device_type = \"cuda\", {         output <- net(data[[i]])         loss <- loss_fn(output, targets[[i]])         })              scaler$scale(loss)$backward()       scaler$step(opt)       scaler$update()       opt$zero_grad()     }   }   loss$item() }   system.time({run(autocast = FALSE, scale = FALSE)}) #>    user  system elapsed  #>   3.967   1.644   5.621 system.time({run(autocast = TRUE, scale = FALSE)}) #>    user  system elapsed  #>   1.857   0.591   2.462 system.time({run(autocast = TRUE, scale = TRUE)}) #>    user  system elapsed  #>   3.434   0.114   3.550"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"basic-univariate-distributions","dir":"Articles","previous_headings":"","what":"Basic univariate distributions","title":"Distributions","text":"Let’s start create new instance normal distribution: can draw samples : , draw multiple samples: can evaluate log probability values: , evaluate multiple log probabilities:","code":"n <- distr_normal(loc = 0, scale = 1) n #> torch_Normal () n$sample() #> torch_tensor #>  0.6614 #> [ CPUFloatType{1} ] n$sample(3) #> torch_tensor #>  0.2669 #>  0.0617 #>  0.6213 #> [ CPUFloatType{3,1} ] n$log_prob(0) #> torch_tensor #> -0.9189 #> [ CPUFloatType{1} ] log(dnorm(0)) # equivalent R code #> [1] -0.9189385 n$log_prob(c(0, 2, 4)) #> torch_tensor #> -0.9189 #> -2.9189 #> -8.9189 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"multiple-distributions","dir":"Articles","previous_headings":"","what":"Multiple distributions","title":"Distributions","text":"distribution can take tensor ’s parameters: object represents 3 independent Bernoulli distributions, one element tensor. can sample single observation: , batch n observations:","code":"b <- distr_bernoulli(probs = torch_tensor(c(0.25, 0.5, 0.75))) b #> torch_Bernoulli () b$sample() #> torch_tensor #>  0 #>  1 #>  1 #> [ CPUFloatType{3} ] b$sample(6) #> torch_tensor #>  0  0  1 #>  0  1  1 #>  0  0  1 #>  0  1  1 #>  0  1  1 #>  0  0  1 #> [ CPUFloatType{6,3} ]"},{"path":"https://torch.mlverse.org/docs/articles/distributions.html","id":"using-distributions-within-models","dir":"Articles","previous_headings":"","what":"Using distributions within models","title":"Distributions","text":"log_prob method distributions can differentiated, thus, distributions can used train models torch. Let’s implement Gaussian linear model, first let’s simulate data plot:  can now define model: can now train model : can see parameter estimates : quickly compare glm() function:","code":"x <- torch_randn(100, 1) y <- 2*x + 1 + torch_randn(100, 1) plot(as.numeric(x), as.numeric(y)) GaussianLinear <- nn_module(   initialize = function() {     # this linear predictor will estimate the mean of the normal distribution     self$linear <- nn_linear(1, 1)     # this parameter will hold the estimate of the variability     self$scale <- nn_parameter(torch_ones(1))   },   forward = function(x) {     # we estimate the mean     loc <- self$linear(x)     # return a normal distribution     distr_normal(loc, self$scale)   } )  model <- GaussianLinear() opt <- optim_sgd(model$parameters, lr = 0.1)  for (i in 1:100) {   opt$zero_grad()   d <- model(x)   loss <- torch_mean(-d$log_prob(y))   loss$backward()   opt$step()   if (i %% 10 == 0)     cat(\"iter: \", i, \" loss: \", loss$item(), \"\\n\") } #> iter:  10  loss:  1.975726  #> iter:  20  loss:  1.790831  #> iter:  30  loss:  1.64495  #> iter:  40  loss:  1.532009  #> iter:  50  loss:  1.478054  #> iter:  60  loss:  1.465937  #> iter:  70  loss:  1.464229  #> iter:  80  loss:  1.464002  #> iter:  90  loss:  1.463971  #> iter:  100  loss:  1.463966 model$parameters #> $linear.weight #> torch_tensor #>  2.1256 #> [ CPUFloatType{1,1} ][ requires_grad = TRUE ] #>  #> $linear.bias #> torch_tensor #>  1.1215 #> [ CPUFloatType{1} ][ requires_grad = TRUE ] #>  #> $scale #> torch_tensor #>  1.0461 #> [ CPUFloatType{1} ][ requires_grad = TRUE ] summary(glm(as.numeric(y) ~ as.numeric(x))) #>  #> Call: #> glm(formula = as.numeric(y) ~ as.numeric(x)) #>  #> Coefficients: #>               Estimate Std. Error t value Pr(>|t|)     #> (Intercept)     1.1226     0.1057   10.62   <2e-16 *** #> as.numeric(x)   2.1259     0.1009   21.08   <2e-16 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> (Dispersion parameter for gaussian family taken to be 1.116565) #>  #>     Null deviance: 605.56  on 99  degrees of freedom #> Residual deviance: 109.42  on 98  degrees of freedom #> AIC: 298.79 #>  #> Number of Fisher Scoring iterations: 2"},{"path":"https://torch.mlverse.org/docs/articles/extending-autograd.html","id":"note","dir":"Articles","previous_headings":"","what":"Note","title":"Extending Autograd","text":"’s user’s responsibility use special functions forward’s ctx properly order ensure new autograd_function works properly autograd engine. save_for_backward() must used saving input ouput forward used later backward. mark_dirty() must used mark input modified inplace forward function. mark_non_differentiable() must used tell engine output differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/extending-autograd.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"Extending Autograd","text":"can find code linear function: , give additional example function parametrized non-Tensor arguments:","code":"linear <- autograd_function(   forward = function(ctx, input, weight, bias = NULL) {     ctx$save_for_backward(input = input, weight = weight, bias = bias)     output <- input$mm(weight$t())     if (!is.null(bias))       output <- output + bias$unsqueeze(0)$expand_as(output)          output   },   backward = function(ctx, grad_output) {          s <- ctx$saved_variables          grads <- list(       input = NULL,       weight = NULL,       bias = NULL     )          if (ctx$needs_input_grad$input)       grads$input <- grad_output$mm(s$weight)          if (ctx$needs_input_grad$weight)       grads$weight <- grad_output$t()$mm(s$input)          if (!is.null(s$bias) && ctx$needs_input_grad$bias)       grads$bias <- grad_output$sum(dim = 0)          grads   } ) mul_constant <- autograd_function(   forward = function(ctx, tensor, constant) {     ctx$save_for_backward(constant = constant)     tensor * constant   },   backward = function(ctx, grad_output) {     v <- ctx$saved_variables     list(       tensor = grad_output * v$constant     )   } ) x <- torch_tensor(1, requires_grad = TRUE) o <- mul_constant(x, 2) o$backward() x$grad #> torch_tensor #>  2 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"single-element-indexing","dir":"Articles","previous_headings":"","what":"Single element indexing","title":"Indexing tensors","text":"Single element indexing 1-D tensors works mostly expected. Like R, 1-based. Unlike R though, accepts negative indices indexing end array. (R, negative indices used remove elements.) can also subset matrices higher dimensions arrays using syntax: Note one indexes multidimensional tensor fewer indices dimensions, torch’s behaviour differs R, flattens array. torch, missing indices considered complete slices :.","code":"x <- torch_tensor(1:10) x[1] #> torch_tensor #> 1 #> [ CPULongType{} ] x[-1] #> torch_tensor #> 10 #> [ CPULongType{} ] x <- x$reshape(shape = c(2,5)) x #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #> [ CPULongType{2,5} ] x[1,3] #> torch_tensor #> 3 #> [ CPULongType{} ] x[1,-1] #> torch_tensor #> 5 #> [ CPULongType{} ] x[1] #> torch_tensor #>  1 #>  2 #>  3 #>  4 #>  5 #> [ CPULongType{5} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"slicing-and-striding","dir":"Articles","previous_headings":"","what":"Slicing and striding","title":"Indexing tensors","text":"possible slice stride arrays extract sub-arrays number dimensions, different sizes original. best illustrated examples: can also use 1:10:2 syntax means: range 1 10, take every second item. example: Another special syntax N, meaning size specified dimension. Note: slicing behavior relies Non Standard Evaluation. requires expression passed [ exactly resulting R vector. allow dynamic dynamic indices, can create new slice using slc function. example: equivalent :","code":"x <- torch_tensor(1:10) x #> torch_tensor #>   1 #>   2 #>   3 #>   4 #>   5 #>   6 #>   7 #>   8 #>   9 #>  10 #> [ CPULongType{10} ] x[2:5] #> torch_tensor #>  2 #>  3 #>  4 #>  5 #> [ CPULongType{4} ] x[1:(-7)] #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPULongType{4} ] x[1:5:2] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ] x[5:N] #> torch_tensor #>   5 #>   6 #>   7 #>   8 #>   9 #>  10 #> [ CPULongType{6} ] x[1:5:2] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ] x[slc(start = 1, end = 5, step = 2)] #> torch_tensor #>  1 #>  3 #>  5 #> [ CPULongType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"getting-the-complete-dimension","dir":"Articles","previous_headings":"","what":"Getting the complete dimension","title":"Indexing tensors","text":"Like R, can take elements dimension leaving index empty. Consider matrix: following syntax give first row: give first 2 columns:","code":"x <- torch_randn(2, 3) x #> torch_tensor #> -1.3069  1.0777 -0.0959 #>  0.8759  0.0347 -0.8753 #> [ CPUFloatType{2,3} ] x[1,] #> torch_tensor #> -1.3069 #>  1.0777 #> -0.0959 #> [ CPUFloatType{3} ] x[,1:2] #> torch_tensor #> -1.3069  1.0777 #>  0.8759  0.0347 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"dropping-dimensions","dir":"Articles","previous_headings":"","what":"Dropping dimensions","title":"Indexing tensors","text":"default, indexing single integer, dimension dropped avoid singleton dimension: can optionally use drop = FALSE argument avoid dropping dimension.","code":"x <- torch_randn(2, 3) x[1,]$shape #> [1] 3 x[1,,drop = FALSE]$shape #> [1] 1 3"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"adding-a-new-dimension","dir":"Articles","previous_headings":"","what":"Adding a new dimension","title":"Indexing tensors","text":"’s possible add new dimension tensor using index-like syntax: can also use NULL instead newaxis:","code":"x <- torch_tensor(c(10)) x$shape #> [1] 1 x[, newaxis]$shape #> [1] 1 1 x[, newaxis, newaxis]$shape #> [1] 1 1 1 x[,NULL]$shape #> [1] 1 1"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"dealing-with-variable-number-of-indices","dir":"Articles","previous_headings":"","what":"Dealing with variable number of indices","title":"Indexing tensors","text":"Sometimes don’t know many dimensions tensor , know last available dimension, first one. subsume others, can use ..:","code":"z <- torch_tensor(1:125)$reshape(c(5,5,5)) z[1,..] #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #>  11  12  13  14  15 #>  16  17  18  19  20 #>  21  22  23  24  25 #> [ CPULongType{5,5} ] z[..,1] #> torch_tensor #>    1    6   11   16   21 #>   26   31   36   41   46 #>   51   56   61   66   71 #>   76   81   86   91   96 #>  101  106  111  116  121 #> [ CPULongType{5,5} ]"},{"path":"https://torch.mlverse.org/docs/articles/indexing.html","id":"indexing-with-vectors","dir":"Articles","previous_headings":"","what":"Indexing with vectors","title":"Indexing tensors","text":"Vector indexing also supported care must taken regarding performance , general much less performant slice based indexing. Note: Starting version 0.5.0, vector indexing torch follows R semantics, prior behavior similar numpy’s advanced indexing. use old behavior, consider using ?torch_index, ?torch_index_put torch_index_put_. can also use boolean vectors, example: examples also work index long boolean tensors, instead R vectors. ’s also possible index multi-dimensional boolean tensors:","code":"x <- torch_randn(4,4) x[c(1,3), c(1,3)] #> torch_tensor #> -0.0490  0.8325 #>  0.0920 -2.4505 #> [ CPUFloatType{2,2} ] x[c(TRUE, FALSE, TRUE, FALSE), c(TRUE, FALSE, TRUE, FALSE)] #> torch_tensor #> -0.0490  0.8325 #>  0.0920 -2.4505 #> [ CPUFloatType{2,2} ] x <- torch_tensor(rbind(   c(1,2,3),   c(4,5,6) )) x[x>3] #> torch_tensor #>  4 #>  5 #>  6 #> [ CPUFloatType{3} ]"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu","dir":"Articles","previous_headings":"Windows","what":"CPU","title":"Installation","text":"don’t GPU want install CPU version torch, can install : Windows distributions don’t Visual Studio runtime pre-installed observe error like: See instructions install .","code":"install.packages(\"torch\") Error in cpp_lantern_init(normalizePath(install_path())): C:\\Users\\User\\Documents\\R\\R-4.0.2\\library\\torch\\deps\\lantern.dll - The specified module could not be found."},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu","dir":"Articles","previous_headings":"Windows","what":"GPU","title":"Installation","text":"torch specific requirements terms CUDA CUDNN versions supports. recomment installing torch using pre-built binaries. See (#pre-built) information. Since version 0.1.1 torch supports GPU installation Windows. order use GPU’s torch need : CUDA compatible NVIDIA GPU. can find CUDA compatible GPU . properly installed NVIDIA CUDA toolkit version 11.7. CUDA v11.7, follow installation instructions . Note: version CUDA toolkit must match exactly ’s mentioed . installed cuDNN - version compatible CUDA v11.7. Follow installation instructions available . installed pre-requisites can install torch : followed default installation locations detect CUDA software installed automatically download GPU enabled Lantern binaries. can also specify CUDA env var something like Sys.setenv(CUDA=\"11.7\") want force specific version CUDA toolkit.","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu-1","dir":"Articles","previous_headings":"MacOS","what":"CPU","title":"Installation","text":"support CPU builds torch MacOS. MacOS can install torch :","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu-1","dir":"Articles","previous_headings":"MacOS","what":"GPU","title":"Installation","text":"Apple Silicon architecture support GPU MPS:","code":"install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"cpu-2","dir":"Articles","previous_headings":"Linux","what":"CPU","title":"Installation","text":"install cpu version torch can run:","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"gpu-2","dir":"Articles","previous_headings":"Linux","what":"GPU","title":"Installation","text":"torch specific requirements terms CUDA CUDNN versions supports. recomment installing torch using pre-built binaries. See (#pre-built) information. install GPU version torch linux must verify : NVIDIA CUDA compatible GPU. can find CUDA compatible GPU . correctly installed NVIDIA CUDA Toolkit versions 11.6 11.7, follow instructions . installed cuDNN (version compatible CUDA version). Follow installation instructions available . installed pre-requisites can install torch : followed default installation locations detect CUDA software installed automatically download GPU enabled Lantern binaries. can also specify CUDA env var something like Sys.setenv(CUDA=\"11.7\") want force specific version CUDA toolkit.","code":"install.packages(\"torch\")"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"pre-built","dir":"Articles","previous_headings":"","what":"Installing from pre-built binaries","title":"Installation","text":"torch v0.9.1.9000 ’s now possible install torch pre-built package binaries CRAN like repository hosted Google Cloud Storage. currently provide pre-built binaries CPU (macOS, Linux Windows) GPU (Windows Linux). Packages provided CRAN-like repository bundles necessary execution, including CUDA CUDNN case GPU builds. means installing agree included software licenses. See PyTorch’s LICENSE CUDA libraries EULA. installing pre-built binaries, don’t need manually install CUDA cuDNN. CUDA installed, doesn’t need match installation ‘kind’ chosen . install pre-built binaries, can use following:","code":"options(timeout = 600) # increasing timeout is recommended since we will be downloading a 2GB file. # For Windows and Linux: \"cpu\", \"cu117\" are the only currently supported # For MacOS the supported are: \"cpu-intel\" or \"cpu-m1\" kind <- \"cu118\" version <- available.packages()[\"torch\",\"Version\"] options(repos = c(   torch = sprintf(\"https://torch-cdn.mlverse.org/packages/%s/%s/\", kind, version),   CRAN = \"https://cloud.r-project.org\" # or any other from which you want to install the other R dependencies. )) install.packages(\"torch\")"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"large-file-download-timeout","dir":"Articles","previous_headings":"Troubleshooting","what":"Large file download timeout","title":"Installation","text":"encounter timeout library download, , downloads end-warning : means encounter download timeout. , increase timeout value install_torch() like","code":"Warning messages: 1: In utils::download.file(library_url, temp_file) :   downloaded length 44901568 != reported length 141774525 2: In utils::download.file(library_url, temp_file) :   URL '...': Timeout of 60 seconds was reached 3: Failed to install Torch, manually run install_torch(). download from 'https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.7.1.zip' failed install_torch(timeout = 600)"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"file-based-download","dir":"Articles","previous_headings":"Troubleshooting","what":"File based download","title":"Installation","text":"cases reach download servers machine intend install torch , last resort install Torch Lantern library files. done 3 steps : 1- get download URLs files. 2- save files machine filesystem. use /tmp/ example . 3- install torch files","code":"get_install_libs_url() # then after making both files available into /tmp/ Sys.setenv(TORCH_URL=\"/tmp/libtorch-v1.13.1.zip\") Sys.setenv(LANTERN_URL=\"/tmp/lantern-0.9.1.9001+cpu+arm64-Darwin.zip\") torch::install_torch()"},{"path":"https://torch.mlverse.org/docs/articles/installation.html","id":"installing-older-versions","dir":"Articles","previous_headings":"Troubleshooting","what":"Installing older versions","title":"Installation","text":"v0.13.0 torch shifted using Google Cloud Storage service AWS S3 storage service required Lantern binaries. keep files GCS bucket long possible, might need remove point time. files backed new AWS S3 bucket using file structure, torch tries download URL starting https://storage.googleapis.com/torch-lantern-builds, now replace https://torch-cdn.mlverse.org. torch versions v0.10.0 v0.12.0 (included), able set environment variable LANTERN_BASE_URL=https://torch-cdn.mlverse.org/binaries/ point new address binaries. older versions torch, might need manually download file new address extract expected TORCH_HOME directory. Feel free open issue GitHub need help .","code":""},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"datasets-and-data-loaders","dir":"Articles","previous_headings":"","what":"Datasets and data loaders","title":"Loading data","text":"Central data ingestion preprocessing datasets data loaders. dataset object holds data use, data loader object load data dataset providing way access subsets data. using datasets data loaders process clearly organizing data passing components torch package, model training. Built torch premade datasets commonly used machine learning, MNIST handwriting dataset (mnist_dataset()). prebuilt datasets relate image recognition natural language processing. example use MNIST dataset dataloader. First, minst_dataset() function used create ds Dataset object. dataloader dl created query data. Finally, dataloader used coro::loop() iterate batches data: See vignettes/examples/mnist-cnn.R complete example. common situation unique set data isn’t included package ’ll need make custom Dataset subclass using dataset() function. custom Dataset subclass abstract R6 container data. need know information particular dataset, iterate . minimum, using dataset() create custom Dataset class ’ll want define following: name - convenience, keep track type data initialize - member function defining create object class. parameters, objects class , can give specific parameters usually different objects different data. .getitem - member function called dataloader goes pull new batch data. can include preprocessing function needed. Note function called extremely frequently, ’s advantageous make fast. .length - return amount data dataset, helpful users.","code":"# Create a dataset from included data ds <- mnist_dataset(   dir,    download = TRUE,    transform = function(x) {     x <- x$to(dtype = torch_float())/256     x[newaxis,..]   } )  # Create the loader to query the data in batches dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)  coro::loop(for (b in dl)) { # use the data from each batch `b` here # ... })"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"example-of-using-a-custom-dataset","dir":"Articles","previous_headings":"","what":"Example of using a custom Dataset","title":"Loading data","text":"may sound complicated base logic steps–complexity often comes data involved preprocessing . show create Dataset class train Allison Horst's penguins. addition, number helper functions can defined. , assume penguins already loaded, preprocessing consists removing lines NA values, transforming factors numbers starting 0, converting R data types torch tensors. .getitem, essentially decide data going used: variables besides species go x, predictor, species constitute y, target. Predictor target returned list, accessed batch[[1]] batch[[2]] training. Let’s create dataset , query ’s length, look first item: able iterate tuxes, need data loader (override default batch size 1): Calling .length() data loader (opposed dataset) return number batches : can create iterator inspect first batch: train network, can use coro::loop() iterate batches.","code":"library(palmerpenguins) library(magrittr)  penguins #> # A tibble: 344 × 8 #>    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g #>    <fct>   <fct>              <dbl>         <dbl>             <int>       <int> #>  1 Adelie  Torgersen           39.1          18.7               181        3750 #>  2 Adelie  Torgersen           39.5          17.4               186        3800 #>  3 Adelie  Torgersen           40.3          18                 195        3250 #>  4 Adelie  Torgersen           NA            NA                  NA          NA #>  5 Adelie  Torgersen           36.7          19.3               193        3450 #>  6 Adelie  Torgersen           39.3          20.6               190        3650 #>  7 Adelie  Torgersen           38.9          17.8               181        3625 #>  8 Adelie  Torgersen           39.2          19.6               195        4675 #>  9 Adelie  Torgersen           34.1          18.1               193        3475 #> 10 Adelie  Torgersen           42            20.2               190        4250 #> # ℹ 334 more rows #> # ℹ 2 more variables: sex <fct>, year <int> penguins_dataset <- dataset(      name = \"penguins_dataset\",      initialize = function() {     self$data <- self$prepare_penguin_data()   },      .getitem = function(index) {          x <- self$data[index, 2:-1]     y <- self$data[index, 1]$to(torch_long())          list(x, y)   },      .length = function() {     self$data$size()[[1]]   },      prepare_penguin_data = function() {          input <- na.omit(penguins)      # conveniently, the categorical data are already factors     input$species <- as.numeric(input$species)     input$island <- as.numeric(input$island)     input$sex <- as.numeric(input$sex)          input <- as.matrix(input)     torch_tensor(input)   } ) tuxes <- penguins_dataset() tuxes$.length() #> [1] 333 tuxes$.getitem(1) #> [[1]] #> torch_tensor #>     3.0000 #>    39.1000 #>    18.7000 #>   181.0000 #>  3750.0000 #>     2.0000 #>  2007.0000 #> [ CPUFloatType{7} ] #>  #> [[2]] #> torch_tensor #> 1 #> [ CPULongType{} ] dl <- tuxes %>% dataloader(batch_size = 8) dl$.length() #> [1] 42 iter <- dl$.iter() b <- iter$.next() b #> [[1]] #> torch_tensor #>     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000 #>     3.0000    39.5000    17.4000   186.0000  3800.0000     1.0000  2007.0000 #>     3.0000    40.3000    18.0000   195.0000  3250.0000     1.0000  2007.0000 #>     3.0000    36.7000    19.3000   193.0000  3450.0000     1.0000  2007.0000 #>     3.0000    39.3000    20.6000   190.0000  3650.0000     2.0000  2007.0000 #>     3.0000    38.9000    17.8000   181.0000  3625.0000     1.0000  2007.0000 #>     3.0000    39.2000    19.6000   195.0000  4675.0000     2.0000  2007.0000 #>     3.0000    41.1000    17.6000   182.0000  3200.0000     1.0000  2007.0000 #> [ CPUFloatType{8,7} ] #>  #> [[2]] #> torch_tensor #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #>  1 #> [ CPULongType{8} ]"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"training-with-data-loaders","dir":"Articles","previous_headings":"Example of using a custom Dataset","what":"Training with data loaders","title":"Loading data","text":"example network simple. (reality, want treat island categorical variable , either one-hot-encode embed .) still need optimizer: ’re ready train: example trained deep learning model using dataset() define custom class loaded batches data loader. using dataset data loader able write code split data preprocessing setup model training .","code":"net <- nn_module(   \"PenguinNet\",   initialize = function() {     self$fc1 <- nn_linear(7, 32)     self$fc2 <- nn_linear(32, 3)   },   forward = function(x) {     x %>%        self$fc1() %>%        nnf_relu() %>%        self$fc2() %>%        nnf_log_softmax(dim = 1)   } )  model <- net() optimizer <- optim_sgd(model$parameters, lr = 0.01) for (epoch in 1:10) {      l <- c()      coro::loop(for (b in dl) {     optimizer$zero_grad()     output <- model(b[[1]])     loss <- nnf_nll_loss(output, b[[2]])     loss$backward()     optimizer$step()     l <- c(l, loss$item())   })      cat(sprintf(\"Loss at epoch %d: %3f\\n\", epoch, mean(l))) } #> Loss at epoch 1: 1306.323535 #> Loss at epoch 2: 2.068251 #> Loss at epoch 3: 2.068251 #> Loss at epoch 4: 2.068251 #> Loss at epoch 5: 2.068251 #> Loss at epoch 6: 2.068251 #> Loss at epoch 7: 2.068251 #> Loss at epoch 8: 2.068251 #> Loss at epoch 9: 2.068251 #> Loss at epoch 10: 2.068251"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"notes-on-efficiency","dir":"Articles","previous_headings":"","what":"Notes on efficiency","title":"Loading data","text":"using datasets data loaders may find certain conditions code running slowly ’d expect. situations overhead using dataloaders datasets can impact overall performance. may change time R/C++ integration Torch improves, now workarounds:","code":""},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"use--getbatch-instead-of--getitem","dir":"Articles","previous_headings":"Notes on efficiency","what":"Use .getbatch() instead of .getitem()","title":"Loading data","text":"default dataloader use .getitem() member function pull single datapoint individually. can speed switching using .getbatch() pull datapoints batch : many instances change exactly replace just .getitem .getbatch since often .getitem function written handle vectors indices. penguins example .getitem function used index select rows, work fine vector instead","code":"penguins_dataset_batching <- dataset(      name = \"penguins_dataset_batching\",      initialize = function() {     self$data <- self$prepare_penguin_data()   },      # the only change is that this went from .getitem to .getbatch   .getbatch = function(index) {          x <- self$data[index, 2:-1]     y <- self$data[index, 1]$to(torch_long())          list(x, y)   },      .length = function() {     self$data$size()[[1]]   },      prepare_penguin_data = function() {          input <- na.omit(penguins)      # conveniently, the categorical data are already factors     input$species <- as.numeric(input$species)     input$island <- as.numeric(input$island)     input$sex <- as.numeric(input$sex)          input <- as.matrix(input)     torch_tensor(input)   } )"},{"path":"https://torch.mlverse.org/docs/articles/loading-data.html","id":"remove-dataset-dataloader-and-manually-define-the-function-calls","dir":"Articles","previous_headings":"Notes on efficiency","what":"Remove dataset dataloader and manually define the function calls","title":"Loading data","text":"switching .getbatch provide benefit expecting also remove dataset entirely manually pass data. point trading readability code convenience speed.","code":"input <- na.omit(penguins)  # conveniently, the categorical data are already factors input$species <- as.numeric(input$species) input$island <- as.numeric(input$island) input$sex <- as.numeric(input$sex)  input <- as.matrix(input) input <- torch_tensor(input)  data_x <- input[, 2:-1] data_y <- input[, 1]$to(torch_long())  batch_size <- 8 num_data_points <- data_y$size(1) num_batches <- floor(num_data_points/batch_size)  for(epoch in 1:10){    # rearrange the data each epoch   permute <- torch_randperm(num_data_points) + 1L   data_x <- data_x[permute]   data_y <- data_y[permute]      # manually loop through the batches   for(batch_idx in 1:num_batches){      # here index is a vector of the indices in the batch     index <- (batch_size*(batch_idx-1) + 1):(batch_idx*batch_size)          x <- data_x[index]     y <- data_y[index]$to(torch_long())      optimizer$zero_grad()     output <- model(x)     loss <- nnf_nll_loss(output, y)     loss$backward()     optimizer$step()     l <- c(l, loss$item())   }      cat(sprintf(\"Loss at epoch %d: %3f\\n\", epoch, mean(l))) }"},{"path":"https://torch.mlverse.org/docs/articles/memory-management.html","id":"cpu","dir":"Articles","previous_headings":"","what":"CPU","title":"Memory management","text":"CPU, torch possibly call R garbage collector two moments: Every 4GB memory allocated LibTorch make call R garbage collector cleans dangling tensors. 4GB threshold can controled setting option torch.threshold_call_gc, example using: option must set calling library(torch) calling torch function first time, setting applied torch starts . torch fails allocating enough memory creating new tensor, garbage collector called allocation retried. Note: OS’s (specially UNIX based) ’s hard allocation fail ’s large, system tries use swap. much swaping used ’s possible system hangs completely. R session hanging, convinced enough memory operations, try setting lower value torch.threshold_call_gc option, call GC often make sure tensors quickly released memory. Note though, calling GC often adds lot overhead, probably slow program execution.","code":"options(torch.threshold_call_gc = 4000)"},{"path":"https://torch.mlverse.org/docs/articles/memory-management.html","id":"cuda","dir":"Articles","previous_headings":"","what":"CUDA","title":"Memory management","text":"CUDA memory tends scarcer CPU memory, also, allocation must faster otherwise allocation overhead can counterbalance speed GPU. make allocations fast avoid segmentation, LibTorch uses caching allocator manage GPU memory, ie. LibTorch allocated CUDA memory won’t give back operation system, instead reuses memory future allocations. means nvidia-smi nvtop report amount memory used tensors, memory LibTorch reserved OS. can use torch::cuda_memory_summary() query exactly memory used LibTorch. Like CPU allocator, torch’s CUDA allocator also call R garbage collector situations cleanup tensors might dangling. torch’s implementation R garbage collector called whenever reusing cached block fails. case, GC called retry getting new block. However, unlike CPU case, allocations failures rare, reusing block common programs LibTorch never reserves large chunk memory, causing GC called almost every allocation (calling GC time consuming). fix , torch allocator call faster GC moments make full collection others. don’t make collection current reserved memory (cached memory) divided total GPU memory smaller 20%. can controlled torch.cuda_allocator_reserved_rate default 0.2. make full collection current allocated memory (memory used tensors) divided total device memory larger 80%. can controlled via torch.cuda_allocator_allocated_rate default 0.8. make full collection current allocated memory larger divided current reserved memory larger 80%. controlled torch.cuda_allocator_allocated_reserved_rate default 0.8. cases light collection made. Equivalent calling gc(full = FALSE) R. options can help tuning allocation performance depending program running. Besides R specific options can set LibTorch options via environment variables described . behavior caching allocator can controlled via environment variable PYTORCH_CUDA_ALLOC_CONF. format PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2><value2>... Available options: max_split_size_mb prevents allocator splitting blocks larger size (MB). can help prevent fragmentation may allow borderline workloads complete without running memory. Performance cost can range ‘zero’ ‘substatial’ depending allocation patterns. Default value unlimited, .e. blocks can split. memory_stats() memory_summary() methods useful tuning. option used last resort workload aborting due ‘memory’ showing large amount inactive split blocks. roundup_power2_divisions helps rounding requested allocation size nearest power-2 division making better use blocks. current CUDACachingAllocator, sizes rounded multiple blocks size 512, works fine smaller sizes. However, can inefficient large near-allocations go different size blocks re-use blocks minimized. might create lots unused blocks waste GPU memory capacity. option enables rounding allocation size nearest power-2 division. example, need round-size 1200 number divisions 4, size 1200 lies 1024 2048 4 divisions , values 1024, 1280, 1536, 1792. , allocation size 1200 rounded 1280 nearest ceiling power-2 division. garbage_collection_threshold helps actively reclaiming unused GPU memory avoid triggering expensive sync--reclaim-operation (release_cached_blocks), can unfavorable latency-critical GPU applications (e.g., servers). Upon setting threshold (e.g., 0.8), allocator start reclaiming GPU memory blocks GPU memory capacity usage exceeds threshold (.e., 80% total memory allocated GPU application). algorithm prefers free old & unused blocks first avoid freeing blocks actively reused. threshold value greater 0.0 less 1.0. Notice garbage collector refered R garbage collector LibTorch’s collector, releases memory cache OS.","code":""},{"path":"https://torch.mlverse.org/docs/articles/memory-management.html","id":"mps","dir":"Articles","previous_headings":"","what":"MPS","title":"Memory management","text":"Memory management MPS devices similar strategy used CUDA devices, except ’s currently configuration tuning possible. R garbage collector called whenever ’s available memory GPU thus, possibly deleting Tensors. Allocation retried fails, OOM error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Modifying torch source code","text":"torch R package composed multiple abstraction layers R code finally calls LibTorch code. Depending part code want modify might need different build requirements setting . special requirements project , ’s always possible use standard devtools tools work torch. guide intended contributors torch codebase quickly get set able modify source code without spending much time understanding internals. torch R package composed following layers, going top : R code calling torch_ functions. R code calling cpp_torch_ functions. Rcpp (C++) code calling lantern_torch_ functions. Lantern (C++) code calling LibTorch functions. Depending function 2, 3 4. might auto-generated (via tools/torchgen) package . next sections explain contribute code depending layer modifying.","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"r-source-code","dir":"Articles","previous_headings":"","what":"R source code","title":"Modifying torch source code","text":"Modifying R source code simplest case shouldn’t required contributors tools standard R development toolbox (like RTools Windows C++ compiler MacOS). standard devtools just work . Ie, can clone project GitHub, modify R source code, including documentation run devtools::load_all() first time run devtools::load_all(), torch installation prompt appear LibTorch lantern binaries downloaded installed inside inst directory installation. Subsequent calls work expected. Note loading torch twice without restarting R session causes session crash. Unbfortunatelly don’t know yet fix bug ’s related problems correctly unloading shared libraries. restart session calling devtools::load_all() . Also note functions might call devtools::load_all() internally, like eg. devtools::test() devtools::document(). Also, running Build Check RStudio pane fail, calls Rcpp::CompileAttributes without calling Makevars file instructions, required torch work correctly. use devtools::check() pass argument devtools::check(document=FALSE). reason described .","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"documentation","dir":"Articles","previous_headings":"R source code","what":"Documentation","title":"Modifying torch source code","text":"torch registers roxygen2 roclets handle examples sessions roxygen2 documentation. Unfortunately unaware method automatically register roclets without messing DESCRIPTION’s collate field. recommended way update documentation torch using tools/document.R script. can either call source(\"tools/document.R\") , recommended, run: terminal. advantage since devtools::document() called internally calls pkgload::load_all(), won’t crash R session forget restart session (see note ).","code":"Rscript tools/document.R"},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"rcpp-code","dir":"Articles","previous_headings":"","what":"Rcpp code","title":"Modifying torch source code","text":"Modifying Rcpp source code src similar modifying C++ R packages. pay attention calling lantern_* functions though. lantern_* functions return void* leak referring objects correctly freed use. best way handle always assign variable correct type defined inst/include/torch_types.h specially defined C++ namespace torch {}. use kind objects also gain ability return R without write custom Rcpp code implemented SEXP() operator.","code":""},{"path":"https://torch.mlverse.org/docs/articles/modifying-source-code.html","id":"lantern-code","dir":"Articles","previous_headings":"","what":"Lantern code","title":"Modifying torch source code","text":"order modify Lantern source code first need make sure environment variable BUILD_LANTERN set 1. avoid forgetting set can add .Renviron using, eg usethis::edit_r_environ(). flag trigger configure file call lantern target Makevars. Building lantern requires CMake path. can install CMake major platforms following instructions install page. BUILD_LANTERN=1 run devtools::load_all(), Lantern compiled part workflow. see new directory called build-lantern package directory containing files related Lantern build. Lantern source code located src/lantern/src. lantern CMakeLists file located src/lantern/CMakeLists.txt. can also take look lantern target src/Makevars.get sense CMake commands called building Lantern source. default, rebuilding Lantern incremental, ie, calling devtools::load_all() rebuild modified parts Lantern code. Depending extend modifications Lantern source code might need completely clean build directory, can simply removing build-lantern directory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/python-to-r.html","id":"loading-models-saved-in-python","dir":"Articles","previous_headings":"","what":"Loading models saved in python","title":"Python to R","text":"Currently way load models python rewrite model architecture R. parameter names must identical. complete example Python R shown . extension Serialization vignette. artificial neural net implemented Python. Note final line uses torch.save(). saved .pth object can load R. example use case training model Python using Shiny develop GUI predictions trained model.","code":"import torch import numpy as np  #Make up data  madeUpData_x = np.random.rand(1000,100) madeUpData_y = np.random.rand(1000)  #Convert to categorical madeUpData_y = madeUpData_y.round()  train_py_X = torch.from_numpy(madeUpData_x).float()  train_py_Y = torch.from_numpy(madeUpData_y).float()  #Note that this class must be replicated identically in R class simpleMLP(torch.nn.Module):     def __init__(self):         super(simpleMLP, self).__init__()         self.modelFit = torch.nn.Sequential(             torch.nn.Linear(100,20),             torch.nn.ReLU(),             torch.nn.Linear(20,1),             torch.nn.Sigmoid())                  def forward(self, x):         x =self.modelFit(x)          return x  model = simpleMLP()   def modelTrainer(data_X,data_Y,model):     criterion = torch.nn.BCELoss()     optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)      for epoch in range(100):          optimizer.zero_grad()          yhat = model(data_X)          loss = criterion(yhat,data_Y.unsqueeze(1))          loss.backward()         optimizer.step()  modelTrainer(data_X = train_py_X,data_Y = train_py_Y,model = model)  #----------------------------------------------------------------- #save the model   #Note that model.state_dict() comes out as an ordered dictionary #The code below converts to a dictionary stateDict = dict(model.state_dict())  #Note the argument _use_new_zipfile_serialization torch.save(stateDict,f=\"path/babyTest.pth\",            _use_new_zipfile_serialization=True) library(torch)  #Make up some test data #note that proper installation of torch will yield no errors when we run #this code y <- torch_tensor(array(runif(8),dim = c(2,2,2)),dtype = torch_float64())  #Note the identical names between the Python class definition and our #class definition simpleMLP <- torch::nn_module(   \"simpleMLP\",   initialize = function(){          self$modelFit <- nn_sequential(nn_linear(100,20),                                    nn_relu(),                                    nn_linear(20,1),                                    nn_sigmoid())        },   forward = function(x){     self$modelFit(x)     } )   model <- simpleMLP()  state_dict <- torch::load_state_dict(\"p/babyTest.pth\") model$load_state_dict(state_dict)  #Note that the dtype set in R has to match the made up data from Python #More generally if reading new data into R you must ensure that it matches the  #dtype that the model was trained with in Python newData = torch_tensor(array(rnorm(n=1000),dim=c(10,100)),dtype=torch_float32())  predictMe <- model(newData)"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-tensors","dir":"Articles","previous_headings":"","what":"Saving tensors","title":"Serialization","text":"can save object type torch_tensor disk using:","code":"x <- torch_randn(10, 10) torch_save(x, \"tensor.pt\") x_ <- torch_load(\"tensor.pt\")  torch_allclose(x, x_) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-modules","dir":"Articles","previous_headings":"","what":"Saving modules","title":"Serialization","text":"torch_save torch_load functions also work nn_modules objects. saving nn_module, object serialized including model structure ’s state.","code":"module <- nn_module(   \"my_module\",   initialize = function() {     self$fc1 <- nn_linear(10, 10)     self$fc2 <- nn_linear(10, 1)   },   forward = function(x) {     x %>%        self$fc1() %>%        self$fc2()   } )  model <- module() torch_save(model, \"model.pt\") model_ <- torch_load(\"model.pt\")  # input tensor x <- torch_randn(50, 10) torch_allclose(model(x), model_(x)) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"loading-models-saved-in-python","dir":"Articles","previous_headings":"","what":"Loading models saved in python","title":"Serialization","text":"Currently way load models python rewrite model architecture R. parameter names must identical. can save PyTorch model state_dict using: can reload state dict R reload model : can find working examples torchvision. example AlexNet model.","code":"torch.save(model, fpath, _use_new_zipfile_serialization=True) state_dict <- load_state_dict(fpath) model <- Model() model$load_state_dict(state_dict)"},{"path":"https://torch.mlverse.org/docs/articles/serialization.html","id":"saving-optimizer-state","dir":"Articles","previous_headings":"","what":"Saving optimizer state","title":"Serialization","text":"can save state optimizers can continue training exact position. order use state_dict() load_state_dict() methods optimizer combined torch_save:","code":"model <- nn_linear(1, 1) opt <- optim_adam(model$parameters)  train_x <- torch_randn(100, 1) train_y <- torch_randn(100, 1)  loss <- nnf_mse_loss(model(train_x), train_y) loss$backward() opt$step() #> NULL  # Now let's save the optimizer state tmp <- tempfile() torch_save(opt$state_dict(), tmp)  # And now let's create a new optimizer and load back opt2 <- optim_adam(model$parameters) opt2$load_state_dict(torch_load(tmp))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numpy_t","dir":"Articles > Tensor","previous_headings":"","what":"numpy_T","title":"Tensor objects","text":"Tensor dimensions reversed. n number dimensions x, x$numpy_T() equivalent x$permute(n, n-1, ..., 1).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"abs","dir":"Articles > Tensor","previous_headings":"","what":"abs","title":"Tensor objects","text":"abs() -> Tensor See ?torch_abs","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"abs_","dir":"Articles > Tensor","previous_headings":"","what":"abs_","title":"Tensor objects","text":"abs_() -> Tensor -place version $abs","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"absolute","dir":"Articles > Tensor","previous_headings":"","what":"absolute","title":"Tensor objects","text":"absolute() -> Tensor Alias [$abs()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"absolute_","dir":"Articles > Tensor","previous_headings":"","what":"absolute_","title":"Tensor objects","text":"absolute_() -> Tensor -place version $absolute Alias [$abs_()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acos","dir":"Articles > Tensor","previous_headings":"","what":"acos","title":"Tensor objects","text":"acos() -> Tensor See ?torch_acos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acos_","dir":"Articles > Tensor","previous_headings":"","what":"acos_","title":"Tensor objects","text":"acos_() -> Tensor -place version $acos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acosh","dir":"Articles > Tensor","previous_headings":"","what":"acosh","title":"Tensor objects","text":"acosh() -> Tensor See ?torch_acosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"acosh_","dir":"Articles > Tensor","previous_headings":"","what":"acosh_","title":"Tensor objects","text":"acosh_() -> Tensor -place version $acosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"add","dir":"Articles > Tensor","previous_headings":"","what":"add","title":"Tensor objects","text":"add(, *, alpha=1) -> Tensor Add scalar tensor self tensor. alpha specified, element scaled alpha used. tensor, shape must broadcastable shape underlying tensor See ?torch_add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"add_","dir":"Articles > Tensor","previous_headings":"","what":"add_","title":"Tensor objects","text":"add_(, *, alpha=1) -> Tensor -place version $add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addbmm","dir":"Articles > Tensor","previous_headings":"","what":"addbmm","title":"Tensor objects","text":"addbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor See ?torch_addbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addbmm_","dir":"Articles > Tensor","previous_headings":"","what":"addbmm_","title":"Tensor objects","text":"addbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor -place version $addbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcdiv","dir":"Articles > Tensor","previous_headings":"","what":"addcdiv","title":"Tensor objects","text":"addcdiv(tensor1, tensor2, *, value=1) -> Tensor See ?torch_addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcdiv_","dir":"Articles > Tensor","previous_headings":"","what":"addcdiv_","title":"Tensor objects","text":"addcdiv_(tensor1, tensor2, *, value=1) -> Tensor -place version $addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcmul","dir":"Articles > Tensor","previous_headings":"","what":"addcmul","title":"Tensor objects","text":"addcmul(tensor1, tensor2, *, value=1) -> Tensor See ?torch_addcmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addcmul_","dir":"Articles > Tensor","previous_headings":"","what":"addcmul_","title":"Tensor objects","text":"addcmul_(tensor1, tensor2, *, value=1) -> Tensor -place version $addcmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmm","dir":"Articles > Tensor","previous_headings":"","what":"addmm","title":"Tensor objects","text":"addmm(mat1, mat2, *, beta=1, alpha=1) -> Tensor See ?torch_addmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmm_","dir":"Articles > Tensor","previous_headings":"","what":"addmm_","title":"Tensor objects","text":"addmm_(mat1, mat2, *, beta=1, alpha=1) -> Tensor -place version $addmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmv","dir":"Articles > Tensor","previous_headings":"","what":"addmv","title":"Tensor objects","text":"addmv(mat, vec, *, beta=1, alpha=1) -> Tensor See ?torch_addmv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addmv_","dir":"Articles > Tensor","previous_headings":"","what":"addmv_","title":"Tensor objects","text":"addmv_(mat, vec, *, beta=1, alpha=1) -> Tensor -place version $addmv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addr","dir":"Articles > Tensor","previous_headings":"","what":"addr","title":"Tensor objects","text":"addr(vec1, vec2, *, beta=1, alpha=1) -> Tensor See ?torch_addr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"addr_","dir":"Articles > Tensor","previous_headings":"","what":"addr_","title":"Tensor objects","text":"addr_(vec1, vec2, *, beta=1, alpha=1) -> Tensor -place version $addr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"align_as","dir":"Articles > Tensor","previous_headings":"","what":"align_as","title":"Tensor objects","text":"align_as() -> Tensor Permutes dimensions self tensor match dimension order tensor, adding size-one dims new names. operation useful explicit broadcasting names (see examples). dims self must named order use method. resulting tensor view original tensor. dimension names self must present $names. may contain named dimensions self$names; output tensor size-one dimension new names. align tensor specific order, use $align_to.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples","dir":"Articles > Tensor","previous_headings":"align_as","what":"Examples:","title":"Tensor objects","text":"","code":"# Example 1: Applying a mask mask <- torch_randint(low = 0, high = 2, size = c(127, 128), dtype=torch_bool())$refine_names(c('W', 'H')) imgs <- torch_randn(32, 128, 127, 3, names=c('N', 'H', 'W', 'C')) imgs$masked_fill_(mask$align_as(imgs), 0)  # Example 2: Applying a per-channel-scale scale_channels <- function(input, scale) {   scale <- scale$refine_names(\"C\")   input * scale$align_as(input) }  num_channels <- 3 scale <- torch_randn(num_channels, names='C') imgs <- torch_rand(32, 128, 128, num_channels, names=c('N', 'H', 'W', 'C')) more_imgs = torch_rand(32, num_channels, 128, 128, names=c('N', 'C', 'H', 'W')) videos = torch_randn(3, num_channels, 128, 128, 128, names=c('N', 'C', 'H', 'W', 'D'))  # scale_channels is agnostic to the dimension order of the input scale_channels(imgs, scale) scale_channels(more_imgs, scale) scale_channels(videos, scale)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning","dir":"Articles > Tensor","previous_headings":"align_as","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"align_to","dir":"Articles > Tensor","previous_headings":"","what":"align_to","title":"Tensor objects","text":"Permutes dimensions self tensor match order specified names, adding size-one dims new names. dims self must named order use method. resulting tensor view original tensor. dimension names self must present names. names may contain additional names self$names; output tensor size-one dimension new names.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments","dir":"Articles > Tensor","previous_headings":"align_to","what":"Arguments:","title":"Tensor objects","text":"names (iterable str): desired dimension ordering output tensor. May contain one Ellipsis expanded unmentioned dim names self.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-1","dir":"Articles > Tensor","previous_headings":"align_to > Examples:","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"all","dir":"Articles > Tensor","previous_headings":"","what":"all","title":"Tensor objects","text":"() -> bool Returns TRUE elements tensor TRUE, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-2","dir":"Articles > Tensor","previous_headings":"all","what":"Examples:","title":"Tensor objects","text":"(dim, keepdim=FALSE, =NULL) -> Tensor Returns TRUE elements row tensor given dimension dim TRUE, FALSE otherwise. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see ?torch_squeeze()), resulting output tensor 1 fewer dimension input.","code":"a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$all()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-1","dir":"Articles > Tensor","previous_headings":"all","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension reduce keepdim (bool): whether output tensor dim retained (Tensor, optional): output tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-3","dir":"Articles > Tensor","previous_headings":"all","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_rand(4, 2)$to(dtype = torch_bool()) a a$all(dim=2) a$all(dim=1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"allclose","dir":"Articles > Tensor","previous_headings":"","what":"allclose","title":"Tensor objects","text":"allclose(, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor See ?torch_allclose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"angle","dir":"Articles > Tensor","previous_headings":"","what":"angle","title":"Tensor objects","text":"angle() -> Tensor See ?torch_angle","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"any","dir":"Articles > Tensor","previous_headings":"","what":"any","title":"Tensor objects","text":"() -> bool Returns TRUE elements tensor TRUE, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-4","dir":"Articles > Tensor","previous_headings":"any","what":"Examples:","title":"Tensor objects","text":"(dim, keepdim=FALSE, =NULL) -> Tensor Returns TRUE elements row tensor given dimension dim TRUE, FALSE otherwise. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see ?torch_squeeze()), resulting output tensor 1 fewer dimension input.","code":"a <- torch_rand(1, 2)$to(dtype = torch_bool()) a a$any()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-2","dir":"Articles > Tensor","previous_headings":"any","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension reduce keepdim (bool): whether output tensor dim retained (Tensor, optional): output tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-5","dir":"Articles > Tensor","previous_headings":"any","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_randn(4, 2) < 0 a a$any(2) a$any(1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"apply_","dir":"Articles > Tensor","previous_headings":"","what":"apply_","title":"Tensor objects","text":"apply_(callable) -> Tensor Applies function callable element tensor, replacing element value returned callable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note","dir":"Articles > Tensor","previous_headings":"apply_","what":"Note:","title":"Tensor objects","text":"function works CPU tensors used code sections require high performance.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argmax","dir":"Articles > Tensor","previous_headings":"","what":"argmax","title":"Tensor objects","text":"argmax(dim=NULL, keepdim=FALSE) -> LongTensor See ?torch_argmax","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argmin","dir":"Articles > Tensor","previous_headings":"","what":"argmin","title":"Tensor objects","text":"argmin(dim=NULL, keepdim=FALSE) -> LongTensor See ?torch_argmin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"argsort","dir":"Articles > Tensor","previous_headings":"","what":"argsort","title":"Tensor objects","text":"argsort(dim=-1, descending=FALSE) -> LongTensor See ?torch_argsort","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"as_strided","dir":"Articles > Tensor","previous_headings":"","what":"as_strided","title":"Tensor objects","text":"as_strided(size, stride, storage_offset=0) -> Tensor See [torch_as_strided()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"as_subclass","dir":"Articles > Tensor","previous_headings":"","what":"as_subclass","title":"Tensor objects","text":"as_subclass(cls) -> Tensor Makes cls instance data pointer self. Changes output mirror changes self, output stays attached autograd graph. cls must subclass Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asin","dir":"Articles > Tensor","previous_headings":"","what":"asin","title":"Tensor objects","text":"asin() -> Tensor See ?torch_asin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asin_","dir":"Articles > Tensor","previous_headings":"","what":"asin_","title":"Tensor objects","text":"asin_() -> Tensor -place version $asin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asinh","dir":"Articles > Tensor","previous_headings":"","what":"asinh","title":"Tensor objects","text":"asinh() -> Tensor See ?torch_asinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"asinh_","dir":"Articles > Tensor","previous_headings":"","what":"asinh_","title":"Tensor objects","text":"asinh_() -> Tensor -place version $asinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan","dir":"Articles > Tensor","previous_headings":"","what":"atan","title":"Tensor objects","text":"atan() -> Tensor See ?torch_atan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan2","dir":"Articles > Tensor","previous_headings":"","what":"atan2","title":"Tensor objects","text":"atan2() -> Tensor See [torch_atan2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan2_","dir":"Articles > Tensor","previous_headings":"","what":"atan2_","title":"Tensor objects","text":"atan2_() -> Tensor -place version $atan2","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atan_","dir":"Articles > Tensor","previous_headings":"","what":"atan_","title":"Tensor objects","text":"atan_() -> Tensor -place version $atan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atanh","dir":"Articles > Tensor","previous_headings":"","what":"atanh","title":"Tensor objects","text":"atanh() -> Tensor See ?torch_atanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"atanh_","dir":"Articles > Tensor","previous_headings":"","what":"atanh_","title":"Tensor objects","text":"-place version $atanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"backward","dir":"Articles > Tensor","previous_headings":"","what":"backward","title":"Tensor objects","text":"Computes gradient current tensor w.r.t. graph leaves. graph differentiated using chain rule. tensor non-scalar (.e. data one element) requires gradient, function additionally requires specifying gradient. tensor matching type location, contains gradient differentiated function w.r.t. self. function accumulates gradients leaves - might need zero $grad attributes set NULL calling . See Default gradient layouts<default-grad-layouts> details memory layout accumulated gradients.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-3","dir":"Articles > Tensor","previous_headings":"backward","what":"Arguments:","title":"Tensor objects","text":"gradient (Tensor NULL): Gradient w.r.t. tensor. tensor, automatically converted Tensor require grad unless create_graph TRUE. NULL values can specified scalar Tensors ones don’t require grad. NULL value acceptable argument optional. retain_graph (bool, optional): FALSE, graph used compute grads freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional): TRUE, graph derivative constructed, allowing compute higher order derivative products. Defaults FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"baddbmm","dir":"Articles > Tensor","previous_headings":"","what":"baddbmm","title":"Tensor objects","text":"baddbmm(batch1, batch2, *, beta=1, alpha=1) -> Tensor See ?torch_baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"baddbmm_","dir":"Articles > Tensor","previous_headings":"","what":"baddbmm_","title":"Tensor objects","text":"baddbmm_(batch1, batch2, *, beta=1, alpha=1) -> Tensor -place version $baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bernoulli","dir":"Articles > Tensor","previous_headings":"","what":"bernoulli","title":"Tensor objects","text":"bernoulli(*, generator=NULL) -> Tensor Returns result tensor 𝚛𝚎𝚜𝚞𝚕𝚝[𝚒]\\texttt{result[]} independently sampled Bernoulli(𝚜𝚎𝚕𝚏[𝚒])\\text{Bernoulli}(\\texttt{self[]}). self must floating point dtype, result dtype. See ?torch_bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bernoulli_","dir":"Articles > Tensor","previous_headings":"","what":"bernoulli_","title":"Tensor objects","text":"bernoulli_(p=0.5, *, generator=NULL) -> Tensor Fills location self independent sample Bernoulli(𝚙)\\text{Bernoulli}(\\texttt{p}). self can integral dtype. bernoulli_(p_tensor, *, generator=NULL) -> Tensor p_tensor tensor containing probabilities used drawing binary random number. ith\\text{}^{th} element self tensor set value sampled Bernoulli(𝚙_𝚝𝚎𝚗𝚜𝚘𝚛[𝚒])\\text{Bernoulli}(\\texttt{p\\_tensor[]}). self can integral dtype, p_tensor must floating point dtype. See also $bernoulli ?torch_bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bfloat16","dir":"Articles > Tensor","previous_headings":"","what":"bfloat16","title":"Tensor objects","text":"bfloat16(memory_format=torch_preserve_format) -> Tensor self$bfloat16() equivalent self$(torch_bfloat16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-4","dir":"Articles > Tensor","previous_headings":"bfloat16","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bincount","dir":"Articles > Tensor","previous_headings":"","what":"bincount","title":"Tensor objects","text":"bincount(weights=NULL, minlength=0) -> Tensor See ?torch_bincount","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_and","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_and","title":"Tensor objects","text":"bitwise_and() -> Tensor See [torch_bitwise_and()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_and_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_and_","title":"Tensor objects","text":"bitwise_and_() -> Tensor -place version $bitwise_and","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_not","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_not","title":"Tensor objects","text":"bitwise_not() -> Tensor See [torch_bitwise_not()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_not_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_not_","title":"Tensor objects","text":"bitwise_not_() -> Tensor -place version $bitwise_not","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_or","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_or","title":"Tensor objects","text":"bitwise_or() -> Tensor See [torch_bitwise_or()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_or_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_or_","title":"Tensor objects","text":"bitwise_or_() -> Tensor -place version $bitwise_or","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_xor","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_xor","title":"Tensor objects","text":"bitwise_xor() -> Tensor See [torch_bitwise_xor()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bitwise_xor_","dir":"Articles > Tensor","previous_headings":"","what":"bitwise_xor_","title":"Tensor objects","text":"bitwise_xor_() -> Tensor -place version $bitwise_xor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bmm","dir":"Articles > Tensor","previous_headings":"","what":"bmm","title":"Tensor objects","text":"bmm(batch2) -> Tensor See ?torch_bmm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"bool","dir":"Articles > Tensor","previous_headings":"","what":"bool","title":"Tensor objects","text":"bool(memory_format=torch_preserve_format) -> Tensor self$bool() equivalent self$(torch_bool). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-5","dir":"Articles > Tensor","previous_headings":"bool","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"byte","dir":"Articles > Tensor","previous_headings":"","what":"byte","title":"Tensor objects","text":"byte(memory_format=torch_preserve_format) -> Tensor self$byte() equivalent self$(torch_uint8). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-6","dir":"Articles > Tensor","previous_headings":"byte","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cauchy_","dir":"Articles > Tensor","previous_headings":"","what":"cauchy_","title":"Tensor objects","text":"cauchy_(median=0, sigma=1, *, generator=NULL) -> Tensor Fills tensor numbers drawn Cauchy distribution: f(x)=1πσ(x−median)2+σ2 f(x) = \\dfrac{1}{\\pi} \\dfrac{\\sigma}{(x - \\text{median})^2 + \\sigma^2}","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ceil","dir":"Articles > Tensor","previous_headings":"","what":"ceil","title":"Tensor objects","text":"ceil() -> Tensor See ?torch_ceil","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ceil_","dir":"Articles > Tensor","previous_headings":"","what":"ceil_","title":"Tensor objects","text":"ceil_() -> Tensor -place version $ceil","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"char","dir":"Articles > Tensor","previous_headings":"","what":"char","title":"Tensor objects","text":"char(memory_format=torch_preserve_format) -> Tensor self$char() equivalent self$(torch_int8). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-7","dir":"Articles > Tensor","previous_headings":"char","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky","dir":"Articles > Tensor","previous_headings":"","what":"cholesky","title":"Tensor objects","text":"cholesky(upper=FALSE) -> Tensor See ?torch_cholesky","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky_inverse","dir":"Articles > Tensor","previous_headings":"","what":"cholesky_inverse","title":"Tensor objects","text":"cholesky_inverse(upper=FALSE) -> Tensor See [torch_cholesky_inverse()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cholesky_solve","dir":"Articles > Tensor","previous_headings":"","what":"cholesky_solve","title":"Tensor objects","text":"cholesky_solve(input2, upper=FALSE) -> Tensor See [torch_cholesky_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"chunk","dir":"Articles > Tensor","previous_headings":"","what":"chunk","title":"Tensor objects","text":"chunk(chunks, dim=0) -> List Tensors See ?torch_chunk","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clamp","dir":"Articles > Tensor","previous_headings":"","what":"clamp","title":"Tensor objects","text":"clamp(min, max) -> Tensor See ?torch_clamp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clamp_","dir":"Articles > Tensor","previous_headings":"","what":"clamp_","title":"Tensor objects","text":"clamp_(min, max) -> Tensor -place version $clamp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"clone","dir":"Articles > Tensor","previous_headings":"","what":"clone","title":"Tensor objects","text":"clone(memory_format=torch_preserve_format()) -> Tensor Returns copy self tensor. copy size data type self.","code":"x <- torch_tensor(1) y <- x$clone()  x$add_(1) y"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-1","dir":"Articles > Tensor","previous_headings":"clone","what":"Note:","title":"Tensor objects","text":"Unlike copy_(), function recorded computation graph. Gradients propagating cloned tensor propagate original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-8","dir":"Articles > Tensor","previous_headings":"clone","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"conj","dir":"Articles > Tensor","previous_headings":"","what":"conj","title":"Tensor objects","text":"conj() -> Tensor See ?torch_conj","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"contiguous","dir":"Articles > Tensor","previous_headings":"","what":"contiguous","title":"Tensor objects","text":"contiguous(memory_format=torch_contiguous_format) -> Tensor Returns contiguous memory tensor containing data self tensor. self tensor already specified memory format, function returns self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-9","dir":"Articles > Tensor","previous_headings":"contiguous","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_contiguous_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"copy_","dir":"Articles > Tensor","previous_headings":"","what":"copy_","title":"Tensor objects","text":"copy_(src, non_blocking=FALSE) -> Tensor Copies elements src self tensor returns self. src tensor must broadcastable self tensor. may different data type reside different device.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-10","dir":"Articles > Tensor","previous_headings":"copy_","what":"Arguments:","title":"Tensor objects","text":"src (Tensor): source tensor copy non_blocking (bool): TRUE copy CPU GPU, copy may occur asynchronously respect host. cases, argument effect.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cos","dir":"Articles > Tensor","previous_headings":"","what":"cos","title":"Tensor objects","text":"cos() -> Tensor See ?torch_cos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cos_","dir":"Articles > Tensor","previous_headings":"","what":"cos_","title":"Tensor objects","text":"cos_() -> Tensor -place version $cos","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cosh","dir":"Articles > Tensor","previous_headings":"","what":"cosh","title":"Tensor objects","text":"cosh() -> Tensor See ?torch_cosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cosh_","dir":"Articles > Tensor","previous_headings":"","what":"cosh_","title":"Tensor objects","text":"cosh_() -> Tensor -place version $cosh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cpu","dir":"Articles > Tensor","previous_headings":"","what":"cpu","title":"Tensor objects","text":"cpu(memory_format=torch_preserve_format) -> Tensor Returns copy object CPU memory. object already CPU memory correct device, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-11","dir":"Articles > Tensor","previous_headings":"cpu","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cross","dir":"Articles > Tensor","previous_headings":"","what":"cross","title":"Tensor objects","text":"cross(, dim=-1) -> Tensor See ?torch_cross","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cuda","dir":"Articles > Tensor","previous_headings":"","what":"cuda","title":"Tensor objects","text":"cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -> Tensor Returns copy object CUDA memory. object already CUDA memory correct device, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-12","dir":"Articles > Tensor","previous_headings":"cuda","what":"Arguments:","title":"Tensor objects","text":"device (torch_device): destination GPU device. Defaults current CUDA device. non_blocking (bool): TRUE source pinned memory, copy asynchronous respect host. Otherwise, argument effect. Default: FALSE. memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cummax","dir":"Articles > Tensor","previous_headings":"","what":"cummax","title":"Tensor objects","text":"cummax(dim) -> (Tensor, Tensor) See ?torch_cummax","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cummin","dir":"Articles > Tensor","previous_headings":"","what":"cummin","title":"Tensor objects","text":"cummin(dim) -> (Tensor, Tensor) See ?torch_cummin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cumprod","dir":"Articles > Tensor","previous_headings":"","what":"cumprod","title":"Tensor objects","text":"cumprod(dim, dtype=NULL) -> Tensor See ?torch_cumprod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"cumsum","dir":"Articles > Tensor","previous_headings":"","what":"cumsum","title":"Tensor objects","text":"cumsum(dim, dtype=NULL) -> Tensor See ?torch_cumsum","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"data_ptr","dir":"Articles > Tensor","previous_headings":"","what":"data_ptr","title":"Tensor objects","text":"data_ptr() -> int Returns address first element self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"deg2rad","dir":"Articles > Tensor","previous_headings":"","what":"deg2rad","title":"Tensor objects","text":"deg2rad() -> Tensor See [torch_deg2rad()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"deg2rad_","dir":"Articles > Tensor","previous_headings":"","what":"deg2rad_","title":"Tensor objects","text":"deg2rad_() -> Tensor -place version $deg2rad","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dense_dim","dir":"Articles > Tensor","previous_headings":"","what":"dense_dim","title":"Tensor objects","text":"dense_dim() -> int self sparse COO tensor (.e., torch_sparse_coo layout), returns number dense dimensions. Otherwise, throws error. See also $sparse_dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dequantize","dir":"Articles > Tensor","previous_headings":"","what":"dequantize","title":"Tensor objects","text":"dequantize() -> Tensor Given quantized Tensor, dequantize return dequantized float Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"det","dir":"Articles > Tensor","previous_headings":"","what":"det","title":"Tensor objects","text":"det() -> Tensor See ?torch_det","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"detach","dir":"Articles > Tensor","previous_headings":"","what":"detach","title":"Tensor objects","text":"Returns new Tensor, detached current graph. result never require gradient.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-2","dir":"Articles > Tensor","previous_headings":"detach","what":"Note:","title":"Tensor objects","text":"Returned Tensor shares storage original one. -place modifications either seen, may trigger errors correctness checks. IMPORTANT NOTE: Previously, -place size / stride / storage changes (resize_ / resize_as_ / set_ / transpose_) returned tensor also update original tensor. Now, -place changes update original tensor anymore, instead trigger error. sparse tensors: -place indices / values changes (zero_ / copy_ / add_) returned tensor update original tensor anymore, instead trigger error.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"detach_","dir":"Articles > Tensor","previous_headings":"","what":"detach_","title":"Tensor objects","text":"Detaches Tensor graph created , making leaf. Views detached -place.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"device","dir":"Articles > Tensor","previous_headings":"","what":"device","title":"Tensor objects","text":"torch_device Tensor .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diag","dir":"Articles > Tensor","previous_headings":"","what":"diag","title":"Tensor objects","text":"diag(diagonal=0) -> Tensor See ?torch_diag","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diag_embed","dir":"Articles > Tensor","previous_headings":"","what":"diag_embed","title":"Tensor objects","text":"diag_embed(offset=0, dim1=-2, dim2=-1) -> Tensor See [torch_diag_embed()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diagflat","dir":"Articles > Tensor","previous_headings":"","what":"diagflat","title":"Tensor objects","text":"diagflat(offset=0) -> Tensor See ?torch_diagflat","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"diagonal","dir":"Articles > Tensor","previous_headings":"","what":"diagonal","title":"Tensor objects","text":"diagonal(offset=0, dim1=0, dim2=1) -> Tensor See ?torch_diagonal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"digamma","dir":"Articles > Tensor","previous_headings":"","what":"digamma","title":"Tensor objects","text":"digamma() -> Tensor See ?torch_digamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"digamma_","dir":"Articles > Tensor","previous_headings":"","what":"digamma_","title":"Tensor objects","text":"digamma_() -> Tensor -place version $digamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dim","dir":"Articles > Tensor","previous_headings":"","what":"dim","title":"Tensor objects","text":"dim() -> int Returns number dimensions self tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dist","dir":"Articles > Tensor","previous_headings":"","what":"dist","title":"Tensor objects","text":"dist(, p=2) -> Tensor See ?torch_dist","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"div","dir":"Articles > Tensor","previous_headings":"","what":"div","title":"Tensor objects","text":"div(value) -> Tensor See ?torch_div","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"div_","dir":"Articles > Tensor","previous_headings":"","what":"div_","title":"Tensor objects","text":"div_(value) -> Tensor -place version $div","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"dot","dir":"Articles > Tensor","previous_headings":"","what":"dot","title":"Tensor objects","text":"dot(tensor2) -> Tensor See ?torch_dot","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"double","dir":"Articles > Tensor","previous_headings":"","what":"double","title":"Tensor objects","text":"double(memory_format=torch_preserve_format) -> Tensor self$double() equivalent self$(torch_float64). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-13","dir":"Articles > Tensor","previous_headings":"double","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eig","dir":"Articles > Tensor","previous_headings":"","what":"eig","title":"Tensor objects","text":"eig(eigenvectors=FALSE) -> (Tensor, Tensor) See ?torch_eig","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"element_size","dir":"Articles > Tensor","previous_headings":"","what":"element_size","title":"Tensor objects","text":"element_size() -> int Returns size bytes individual element.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-6","dir":"Articles > Tensor","previous_headings":"element_size","what":"Examples:","title":"Tensor objects","text":"","code":"torch_tensor(c(1))$element_size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eq","dir":"Articles > Tensor","previous_headings":"","what":"eq","title":"Tensor objects","text":"eq() -> Tensor See ?torch_eq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"eq_","dir":"Articles > Tensor","previous_headings":"","what":"eq_","title":"Tensor objects","text":"eq_() -> Tensor -place version $eq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"equal","dir":"Articles > Tensor","previous_headings":"","what":"equal","title":"Tensor objects","text":"equal() -> bool See ?torch_equal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erf","dir":"Articles > Tensor","previous_headings":"","what":"erf","title":"Tensor objects","text":"erf() -> Tensor See ?torch_erf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erf_","dir":"Articles > Tensor","previous_headings":"","what":"erf_","title":"Tensor objects","text":"erf_() -> Tensor -place version $erf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfc","dir":"Articles > Tensor","previous_headings":"","what":"erfc","title":"Tensor objects","text":"erfc() -> Tensor See ?torch_erfc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfc_","dir":"Articles > Tensor","previous_headings":"","what":"erfc_","title":"Tensor objects","text":"erfc_() -> Tensor -place version $erfc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfinv","dir":"Articles > Tensor","previous_headings":"","what":"erfinv","title":"Tensor objects","text":"erfinv() -> Tensor See ?torch_erfinv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"erfinv_","dir":"Articles > Tensor","previous_headings":"","what":"erfinv_","title":"Tensor objects","text":"erfinv_() -> Tensor -place version $erfinv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exp","dir":"Articles > Tensor","previous_headings":"","what":"exp","title":"Tensor objects","text":"exp() -> Tensor See ?torch_exp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exp_","dir":"Articles > Tensor","previous_headings":"","what":"exp_","title":"Tensor objects","text":"exp_() -> Tensor -place version $exp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expand","dir":"Articles > Tensor","previous_headings":"","what":"expand","title":"Tensor objects","text":"expand(*sizes) -> Tensor Returns new view self tensor singleton dimensions expanded larger size. Passing -1 size dimension means changing size dimension. Tensor can also expanded larger number dimensions, new ones appended front. new dimensions, size set -1. Expanding tensor allocate new memory, creates new view existing tensor dimension size one expanded larger size setting stride 0. dimension size 1 can expanded arbitrary value without allocating new memory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-14","dir":"Articles > Tensor","previous_headings":"expand","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): desired expanded size","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-2","dir":"Articles > Tensor","previous_headings":"expand","what":"Warning:","title":"Tensor objects","text":"one element expanded tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-7","dir":"Articles > Tensor","previous_headings":"expand","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(c(1,2,3), ncol = 1)) x$size() x$expand(c(3, 4)) x$expand(c(-1, 4))  # -1 means not changing the size of that dimension"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expand_as","dir":"Articles > Tensor","previous_headings":"","what":"expand_as","title":"Tensor objects","text":"expand_as() -> Tensor Expand tensor size . self$expand_as() equivalent self$expand(.size()). Please see $expand information expand.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-15","dir":"Articles > Tensor","previous_headings":"expand_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor size .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expm1","dir":"Articles > Tensor","previous_headings":"","what":"expm1","title":"Tensor objects","text":"expm1() -> Tensor See [torch_expm1()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"expm1_","dir":"Articles > Tensor","previous_headings":"","what":"expm1_","title":"Tensor objects","text":"expm1_() -> Tensor -place version $expm1","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"exponential_","dir":"Articles > Tensor","previous_headings":"","what":"exponential_","title":"Tensor objects","text":"exponential_(lambd=1, *, generator=NULL) -> Tensor Fills self tensor elements drawn exponential distribution: f(x)=λe−λx f(x) = \\lambda e^{-\\lambda x}","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fft","dir":"Articles > Tensor","previous_headings":"","what":"fft","title":"Tensor objects","text":"fft(signal_ndim, normalized=FALSE) -> Tensor See ?torch_fft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fill_","dir":"Articles > Tensor","previous_headings":"","what":"fill_","title":"Tensor objects","text":"fill_(value) -> Tensor Fills self tensor specified value.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fill_diagonal_","dir":"Articles > Tensor","previous_headings":"","what":"fill_diagonal_","title":"Tensor objects","text":"fill_diagonal_(fill_value, wrap=FALSE) -> Tensor Fill main diagonal tensor least 2-dimensions. dims>2, dimensions input must equal length. function modifies input tensor -place, returns input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-16","dir":"Articles > Tensor","previous_headings":"fill_diagonal_","what":"Arguments:","title":"Tensor objects","text":"fill_value (Scalar): fill value wrap (bool): diagonal ‘wrapped’ N columns tall matrices.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-8","dir":"Articles > Tensor","previous_headings":"fill_diagonal_","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_zeros(3, 3) a$fill_diagonal_(5) b <- torch_zeros(7, 3) b$fill_diagonal_(5) c <- torch_zeros(7, 3) c$fill_diagonal_(5, wrap=TRUE)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flatten","dir":"Articles > Tensor","previous_headings":"","what":"flatten","title":"Tensor objects","text":"flatten(input, start_dim=0, end_dim=-1) -> Tensor see ?torch_flatten","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flip","dir":"Articles > Tensor","previous_headings":"","what":"flip","title":"Tensor objects","text":"flip(dims) -> Tensor See ?torch_flip","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fliplr","dir":"Articles > Tensor","previous_headings":"","what":"fliplr","title":"Tensor objects","text":"fliplr() -> Tensor See ?torch_fliplr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"flipud","dir":"Articles > Tensor","previous_headings":"","what":"flipud","title":"Tensor objects","text":"flipud() -> Tensor See ?torch_flipud","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"float","dir":"Articles > Tensor","previous_headings":"","what":"float","title":"Tensor objects","text":"float(memory_format=torch_preserve_format) -> Tensor self$float() equivalent self$(torch_float32). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-17","dir":"Articles > Tensor","previous_headings":"float","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor","dir":"Articles > Tensor","previous_headings":"","what":"floor","title":"Tensor objects","text":"floor() -> Tensor See ?torch_floor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_","dir":"Articles > Tensor","previous_headings":"","what":"floor_","title":"Tensor objects","text":"floor_() -> Tensor -place version $floor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_divide","dir":"Articles > Tensor","previous_headings":"","what":"floor_divide","title":"Tensor objects","text":"floor_divide(value) -> Tensor See [torch_floor_divide()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"floor_divide_","dir":"Articles > Tensor","previous_headings":"","what":"floor_divide_","title":"Tensor objects","text":"floor_divide_(value) -> Tensor -place version $floor_divide","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fmod","dir":"Articles > Tensor","previous_headings":"","what":"fmod","title":"Tensor objects","text":"fmod(divisor) -> Tensor See ?torch_fmod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"fmod_","dir":"Articles > Tensor","previous_headings":"","what":"fmod_","title":"Tensor objects","text":"fmod_(divisor) -> Tensor -place version $fmod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"frac","dir":"Articles > Tensor","previous_headings":"","what":"frac","title":"Tensor objects","text":"frac() -> Tensor See ?torch_frac","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"frac_","dir":"Articles > Tensor","previous_headings":"","what":"frac_","title":"Tensor objects","text":"frac_() -> Tensor -place version $frac","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gather","dir":"Articles > Tensor","previous_headings":"","what":"gather","title":"Tensor objects","text":"gather(dim, index) -> Tensor See ?torch_gather","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ge","dir":"Articles > Tensor","previous_headings":"","what":"ge","title":"Tensor objects","text":"ge() -> Tensor See ?torch_ge","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ge_","dir":"Articles > Tensor","previous_headings":"","what":"ge_","title":"Tensor objects","text":"ge_() -> Tensor -place version $ge","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"geometric_","dir":"Articles > Tensor","previous_headings":"","what":"geometric_","title":"Tensor objects","text":"geometric_(p, *, generator=NULL) -> Tensor Fills self tensor elements drawn geometric distribution: f(X=k)=pk−1(1−p) f(X=k) = p^{k - 1} (1 - p)","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"geqrf","dir":"Articles > Tensor","previous_headings":"","what":"geqrf","title":"Tensor objects","text":"geqrf() -> (Tensor, Tensor) See ?torch_geqrf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ger","dir":"Articles > Tensor","previous_headings":"","what":"ger","title":"Tensor objects","text":"ger(vec2) -> Tensor See ?torch_ger","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"get_device","dir":"Articles > Tensor","previous_headings":"","what":"get_device","title":"Tensor objects","text":"get_device() -> Device ordinal (Integer) CUDA tensors, function returns device ordinal GPU tensor resides. CPU tensors, error thrown.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-9","dir":"Articles > Tensor","previous_headings":"get_device","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(3, 4, 5, device='cuda:0') x$get_device() x$cpu()$get_device()  # RuntimeError: get_device is not implemented for type torch_FloatTensor"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"grad","dir":"Articles > Tensor","previous_headings":"","what":"grad","title":"Tensor objects","text":"attribute NULL default becomes Tensor first time call backward computes gradients self. attribute contain gradients computed future calls [backward()] accumulate (add) gradients .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gt","dir":"Articles > Tensor","previous_headings":"","what":"gt","title":"Tensor objects","text":"gt() -> Tensor See ?torch_gt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"gt_","dir":"Articles > Tensor","previous_headings":"","what":"gt_","title":"Tensor objects","text":"gt_() -> Tensor -place version $gt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"half","dir":"Articles > Tensor","previous_headings":"","what":"half","title":"Tensor objects","text":"half(memory_format=torch_preserve_format) -> Tensor self$half() equivalent self$(torch_float16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-18","dir":"Articles > Tensor","previous_headings":"half","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"hardshrink","dir":"Articles > Tensor","previous_headings":"","what":"hardshrink","title":"Tensor objects","text":"hardshrink(lambd=0.5) -> Tensor See [torch_nn.functional.hardshrink()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"has_names","dir":"Articles > Tensor","previous_headings":"","what":"has_names","title":"Tensor objects","text":"TRUE tensor’s dimensions named. Otherwise, FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"histc","dir":"Articles > Tensor","previous_headings":"","what":"histc","title":"Tensor objects","text":"histc(bins=100, min=0, max=0) -> Tensor See ?torch_histc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ifft","dir":"Articles > Tensor","previous_headings":"","what":"ifft","title":"Tensor objects","text":"ifft(signal_ndim, normalized=FALSE) -> Tensor See ?torch_ifft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"imag","dir":"Articles > Tensor","previous_headings":"","what":"imag","title":"Tensor objects","text":"Returns new tensor containing imaginary values self tensor. returned tensor self share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-3","dir":"Articles > Tensor","previous_headings":"imag","what":"Warning:","title":"Tensor objects","text":"[imag()] supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-10","dir":"Articles > Tensor","previous_headings":"imag","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(4, dtype=torch_cfloat()) x x$imag"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_add","dir":"Articles > Tensor","previous_headings":"","what":"index_add","title":"Tensor objects","text":"index_add(tensor1, dim, index, tensor2) -> Tensor --place version $index_add_. tensor1 corresponds self $index_add_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_add_","dir":"Articles > Tensor","previous_headings":"","what":"index_add_","title":"Tensor objects","text":"index_add_(dim, index, tensor) -> Tensor Accumulate elements tensor self tensor adding indices order given index. example, dim == 0 index[] == j,  th row tensor added j th row self. dim th dimension tensor must size length index (must vector), dimensions must match self, error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-3","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Note:","title":"Tensor objects","text":"circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch_backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-19","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices tensor select tensor (Tensor): tensor containing values add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-11","dir":"Articles > Tensor","previous_headings":"index_add_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_ones(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1L, 4L, 3L)) x$index_add_(1, index, t)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_copy","dir":"Articles > Tensor","previous_headings":"","what":"index_copy","title":"Tensor objects","text":"index_copy(tensor1, dim, index, tensor2) -> Tensor --place version $index_copy_. tensor1 corresponds self $index_copy_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_copy_","dir":"Articles > Tensor","previous_headings":"","what":"index_copy_","title":"Tensor objects","text":"index_copy_(dim, index, tensor) -> Tensor Copies elements tensor self tensor selecting indices order given index. example, dim == 0 index[] == j,  th row tensor copied j th row self. dim th dimension tensor must size length index (must vector), dimensions must match self, error raised.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-20","dir":"Articles > Tensor","previous_headings":"index_copy_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices tensor select tensor (Tensor): tensor containing values copy","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-12","dir":"Articles > Tensor","previous_headings":"index_copy_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_zeros(5, 3) t <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 5, 3)) x$index_copy_(1, index, t)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_fill","dir":"Articles > Tensor","previous_headings":"","what":"index_fill","title":"Tensor objects","text":"index_fill(tensor1, dim, index, value) -> Tensor --place version $index_fill_. tensor1 corresponds self $index_fill_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_fill_","dir":"Articles > Tensor","previous_headings":"","what":"index_fill_","title":"Tensor objects","text":"index_fill_(dim, index, val) -> Tensor Fills elements self tensor value val selecting indices order given index.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-21","dir":"Articles > Tensor","previous_headings":"index_fill_","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension along index index (LongTensor): indices self tensor fill val (float): value fill ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-13","dir":"Articles > Tensor","previous_headings":"index_fill_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:9, ncol = 3), dtype=torch_float()) index <- torch_tensor(c(1, 3), dtype = torch_long()) x$index_fill_(1, index, -1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_put","dir":"Articles > Tensor","previous_headings":"","what":"index_put","title":"Tensor objects","text":"index_put(tensor1, indices, value, accumulate=FALSE) -> Tensor -place version $index_put_. tensor1 corresponds self $index_put_.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_put_","dir":"Articles > Tensor","previous_headings":"","what":"index_put_","title":"Tensor objects","text":"index_put_(indices, value, accumulate=FALSE) -> Tensor Puts values tensor value tensor self using indices specified indices (tuple Tensors). expression tensor.index_put_(indices, value) equivalent tensor[indices] = value. Returns self. accumulate TRUE, elements value added self. accumulate FALSE, behavior undefined indices contain duplicate elements.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-22","dir":"Articles > Tensor","previous_headings":"index_put_","what":"Arguments:","title":"Tensor objects","text":"indices (tuple LongTensor): tensors used index self. value (Tensor): tensor dtype self. accumulate (bool): whether accumulate self","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"index_select","dir":"Articles > Tensor","previous_headings":"","what":"index_select","title":"Tensor objects","text":"index_select(dim, index) -> Tensor See [torch_index_select()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"indices","dir":"Articles > Tensor","previous_headings":"","what":"indices","title":"Tensor objects","text":"indices() -> Tensor self sparse COO tensor (.e., torch_sparse_coo layout), returns view contained indices tensor. Otherwise, throws error. See also Tensor.values.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-4","dir":"Articles > Tensor","previous_headings":"indices","what":"Note:","title":"Tensor objects","text":"method can called coalesced sparse tensor. See Tensor.coalesce details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"int","dir":"Articles > Tensor","previous_headings":"","what":"int","title":"Tensor objects","text":"int(memory_format=torch_preserve_format) -> Tensor self$int() equivalent self$(torch_int32). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-23","dir":"Articles > Tensor","previous_headings":"int","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"int_repr","dir":"Articles > Tensor","previous_headings":"","what":"int_repr","title":"Tensor objects","text":"int_repr() -> Tensor Given quantized Tensor, self$int_repr() returns CPU Tensor uint8_t data type stores underlying uint8_t values given Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"inverse","dir":"Articles > Tensor","previous_headings":"","what":"inverse","title":"Tensor objects","text":"inverse() -> Tensor See ?torch_inverse","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"irfft","dir":"Articles > Tensor","previous_headings":"","what":"irfft","title":"Tensor objects","text":"irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -> Tensor See ?torch_irfft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_complex","dir":"Articles > Tensor","previous_headings":"","what":"is_complex","title":"Tensor objects","text":"is_complex() -> bool Returns TRUE data type self complex data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_contiguous","dir":"Articles > Tensor","previous_headings":"","what":"is_contiguous","title":"Tensor objects","text":"is_contiguous(memory_format=torch_contiguous_format) -> bool Returns TRUE self tensor contiguous memory order specified memory format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-24","dir":"Articles > Tensor","previous_headings":"is_contiguous","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): Specifies memory allocation order. Default: torch_contiguous_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_cuda","dir":"Articles > Tensor","previous_headings":"","what":"is_cuda","title":"Tensor objects","text":"TRUE Tensor stored GPU, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_floating_point","dir":"Articles > Tensor","previous_headings":"","what":"is_floating_point","title":"Tensor objects","text":"is_floating_point() -> bool Returns TRUE data type self floating point data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_leaf","dir":"Articles > Tensor","previous_headings":"","what":"is_leaf","title":"Tensor objects","text":"Tensors requires_grad FALSE leaf Tensors convention. Tensors requires_grad TRUE, leaf Tensors created user. means result operation grad_fn NULL. leaf Tensors grad populated call [backward()]. get grad populated non-leaf Tensors, can use [retain_grad()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-14","dir":"Articles > Tensor","previous_headings":"is_leaf","what":"Examples:","title":"Tensor objects","text":"","code":"a <- torch_rand(10, requires_grad=TRUE) a$is_leaf  # b <- torch_rand(10, requires_grad=TRUE)$cuda() # b$is_leaf() # FALSE # b was created by the operation that cast a cpu Tensor into a cuda Tensor  c <- torch_rand(10, requires_grad=TRUE) + 2 c$is_leaf # c was created by the addition operation  # d <- torch_rand(10)$cuda() # d$is_leaf() # TRUE # d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)  # e <- torch_rand(10)$cuda()$requires_grad_() # e$is_leaf() # TRUE # e requires gradients and has no operations creating it  # f <- torch_rand(10, requires_grad=TRUE, device=\"cuda\") # f$is_leaf # TRUE # f requires grad, has no operation creating it"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_meta","dir":"Articles > Tensor","previous_headings":"","what":"is_meta","title":"Tensor objects","text":"TRUE Tensor meta tensor, FALSE otherwise. Meta tensors like normal tensors, carry data.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_pinned","dir":"Articles > Tensor","previous_headings":"","what":"is_pinned","title":"Tensor objects","text":"Returns true tensor resides pinned memory.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_quantized","dir":"Articles > Tensor","previous_headings":"","what":"is_quantized","title":"Tensor objects","text":"TRUE Tensor quantized, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_set_to","dir":"Articles > Tensor","previous_headings":"","what":"is_set_to","title":"Tensor objects","text":"is_set_to(tensor) -> bool Returns TRUE object refers THTensor object Torch C API given tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_shared","dir":"Articles > Tensor","previous_headings":"","what":"is_shared","title":"Tensor objects","text":"Checks tensor shared memory. always TRUE CUDA tensors.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"is_signed","dir":"Articles > Tensor","previous_headings":"","what":"is_signed","title":"Tensor objects","text":"is_signed() -> bool Returns TRUE data type self signed data type.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isclose","dir":"Articles > Tensor","previous_headings":"","what":"isclose","title":"Tensor objects","text":"isclose(, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor See ?torch_isclose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isfinite","dir":"Articles > Tensor","previous_headings":"","what":"isfinite","title":"Tensor objects","text":"isfinite() -> Tensor See ?torch_isfinite","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isinf","dir":"Articles > Tensor","previous_headings":"","what":"isinf","title":"Tensor objects","text":"isinf() -> Tensor See ?torch_isinf","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"isnan","dir":"Articles > Tensor","previous_headings":"","what":"isnan","title":"Tensor objects","text":"isnan() -> Tensor See ?torch_isnan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"istft","dir":"Articles > Tensor","previous_headings":"","what":"istft","title":"Tensor objects","text":"See ?torch_istft ## item item() -> number Returns value tensor standard Python number. works tensors one element. cases, see $tolist. operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-15","dir":"Articles > Tensor","previous_headings":"istft","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(1.0) x$item()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"kthvalue","dir":"Articles > Tensor","previous_headings":"","what":"kthvalue","title":"Tensor objects","text":"kthvalue(k, dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_kthvalue","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"le","dir":"Articles > Tensor","previous_headings":"","what":"le","title":"Tensor objects","text":"le() -> Tensor See ?torch_le","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"le_","dir":"Articles > Tensor","previous_headings":"","what":"le_","title":"Tensor objects","text":"le_() -> Tensor -place version $le","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lerp","dir":"Articles > Tensor","previous_headings":"","what":"lerp","title":"Tensor objects","text":"lerp(end, weight) -> Tensor See ?torch_lerp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lerp_","dir":"Articles > Tensor","previous_headings":"","what":"lerp_","title":"Tensor objects","text":"lerp_(end, weight) -> Tensor -place version $lerp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lgamma","dir":"Articles > Tensor","previous_headings":"","what":"lgamma","title":"Tensor objects","text":"lgamma() -> Tensor See ?torch_lgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lgamma_","dir":"Articles > Tensor","previous_headings":"","what":"lgamma_","title":"Tensor objects","text":"lgamma_() -> Tensor -place version $lgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log","dir":"Articles > Tensor","previous_headings":"","what":"log","title":"Tensor objects","text":"log() -> Tensor See ?torch_log","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log10","dir":"Articles > Tensor","previous_headings":"","what":"log10","title":"Tensor objects","text":"log10() -> Tensor See [torch_log10()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log10_","dir":"Articles > Tensor","previous_headings":"","what":"log10_","title":"Tensor objects","text":"log10_() -> Tensor -place version $log10","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log1p","dir":"Articles > Tensor","previous_headings":"","what":"log1p","title":"Tensor objects","text":"log1p() -> Tensor See [torch_log1p()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log1p_","dir":"Articles > Tensor","previous_headings":"","what":"log1p_","title":"Tensor objects","text":"log1p_() -> Tensor -place version $log1p","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log2","dir":"Articles > Tensor","previous_headings":"","what":"log2","title":"Tensor objects","text":"log2() -> Tensor See [torch_log2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log2_","dir":"Articles > Tensor","previous_headings":"","what":"log2_","title":"Tensor objects","text":"log2_() -> Tensor -place version $log2","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log_","dir":"Articles > Tensor","previous_headings":"","what":"log_","title":"Tensor objects","text":"log_() -> Tensor -place version $log","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"log_normal_","dir":"Articles > Tensor","previous_headings":"","what":"log_normal_","title":"Tensor objects","text":"log_normal_(mean=1, std=2, *, generator=NULL) Fills self tensor numbers samples log-normal distribution parameterized given mean \\mu standard deviation \\sigma. Note mean std mean standard deviation underlying normal distribution, returned distribution: f(x)=1xσ2πe−(lnx−μ)22σ2 f(x) = \\dfrac{1}{x \\sigma \\sqrt{2\\pi}}\\ e^{-\\frac{(\\ln x - \\mu)^2}{2\\sigma^2}}","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logaddexp","dir":"Articles > Tensor","previous_headings":"","what":"logaddexp","title":"Tensor objects","text":"logaddexp() -> Tensor See ?torch_logaddexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logaddexp2","dir":"Articles > Tensor","previous_headings":"","what":"logaddexp2","title":"Tensor objects","text":"logaddexp2() -> Tensor See [torch_logaddexp2()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logcumsumexp","dir":"Articles > Tensor","previous_headings":"","what":"logcumsumexp","title":"Tensor objects","text":"logcumsumexp(dim) -> Tensor See ?torch_logcumsumexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logdet","dir":"Articles > Tensor","previous_headings":"","what":"logdet","title":"Tensor objects","text":"logdet() -> Tensor See ?torch_logdet","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_and","dir":"Articles > Tensor","previous_headings":"","what":"logical_and","title":"Tensor objects","text":"logical_and() -> Tensor See [torch_logical_and()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_and_","dir":"Articles > Tensor","previous_headings":"","what":"logical_and_","title":"Tensor objects","text":"logical_and_() -> Tensor -place version $logical_and","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_not","dir":"Articles > Tensor","previous_headings":"","what":"logical_not","title":"Tensor objects","text":"logical_not() -> Tensor See [torch_logical_not()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_not_","dir":"Articles > Tensor","previous_headings":"","what":"logical_not_","title":"Tensor objects","text":"logical_not_() -> Tensor -place version $logical_not","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_or","dir":"Articles > Tensor","previous_headings":"","what":"logical_or","title":"Tensor objects","text":"logical_or() -> Tensor See [torch_logical_or()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_or_","dir":"Articles > Tensor","previous_headings":"","what":"logical_or_","title":"Tensor objects","text":"logical_or_() -> Tensor -place version $logical_or","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_xor","dir":"Articles > Tensor","previous_headings":"","what":"logical_xor","title":"Tensor objects","text":"logical_xor() -> Tensor See [torch_logical_xor()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logical_xor_","dir":"Articles > Tensor","previous_headings":"","what":"logical_xor_","title":"Tensor objects","text":"logical_xor_() -> Tensor -place version $logical_xor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"logsumexp","dir":"Articles > Tensor","previous_headings":"","what":"logsumexp","title":"Tensor objects","text":"logsumexp(dim, keepdim=FALSE) -> Tensor See ?torch_logsumexp","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"long","dir":"Articles > Tensor","previous_headings":"","what":"long","title":"Tensor objects","text":"long(memory_format=torch_preserve_format) -> Tensor self$long() equivalent self$(torch_int64). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-25","dir":"Articles > Tensor","previous_headings":"long","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lstsq","dir":"Articles > Tensor","previous_headings":"","what":"lstsq","title":"Tensor objects","text":"lstsq() -> (Tensor, Tensor) See ?torch_lstsq","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lt","dir":"Articles > Tensor","previous_headings":"","what":"lt","title":"Tensor objects","text":"lt() -> Tensor See ?torch_lt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lt_","dir":"Articles > Tensor","previous_headings":"","what":"lt_","title":"Tensor objects","text":"lt_() -> Tensor -place version $lt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"lu","dir":"Articles > Tensor","previous_headings":"","what":"lu","title":"Tensor objects","text":"See ?torch_lu ## lu_solve lu_solve(LU_data, LU_pivots) -> Tensor See [torch_lu_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"map_","dir":"Articles > Tensor","previous_headings":"","what":"map_","title":"Tensor objects","text":"map_(tensor, callable) Applies callable element self tensor given tensor stores results self tensor. self tensor given tensor must broadcastable. callable signature: callable(, b) -> number","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_fill","dir":"Articles > Tensor","previous_headings":"","what":"masked_fill","title":"Tensor objects","text":"masked_fill(mask, value) -> Tensor --place version $masked_fill_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_fill_","dir":"Articles > Tensor","previous_headings":"","what":"masked_fill_","title":"Tensor objects","text":"masked_fill_(mask, value) Fills elements self tensor value mask TRUE. shape mask must broadcastable <broadcasting-semantics> shape underlying tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-26","dir":"Articles > Tensor","previous_headings":"masked_fill_","what":"Arguments:","title":"Tensor objects","text":"mask (BoolTensor): boolean mask value (float): value fill ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_scatter","dir":"Articles > Tensor","previous_headings":"","what":"masked_scatter","title":"Tensor objects","text":"masked_scatter(mask, tensor) -> Tensor --place version $masked_scatter_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_scatter_","dir":"Articles > Tensor","previous_headings":"","what":"masked_scatter_","title":"Tensor objects","text":"masked_scatter_(mask, source) Copies elements source self tensor positions mask TRUE. shape mask must :ref:broadcastable <broadcasting-semantics> shape underlying tensor. source least many elements number ones mask","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-27","dir":"Articles > Tensor","previous_headings":"masked_scatter_","what":"Arguments:","title":"Tensor objects","text":"mask (BoolTensor): boolean mask source (Tensor): tensor copy ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-5","dir":"Articles > Tensor","previous_headings":"masked_scatter_","what":"Note:","title":"Tensor objects","text":"mask operates self tensor, given source tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"masked_select","dir":"Articles > Tensor","previous_headings":"","what":"masked_select","title":"Tensor objects","text":"masked_select(mask) -> Tensor See [torch_masked_select()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"matmul","dir":"Articles > Tensor","previous_headings":"","what":"matmul","title":"Tensor objects","text":"matmul(tensor2) -> Tensor See ?torch_matmul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"matrix_power","dir":"Articles > Tensor","previous_headings":"","what":"matrix_power","title":"Tensor objects","text":"matrix_power(n) -> Tensor See [torch_matrix_power()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"max","dir":"Articles > Tensor","previous_headings":"","what":"max","title":"Tensor objects","text":"max(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_max","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mean","dir":"Articles > Tensor","previous_headings":"","what":"mean","title":"Tensor objects","text":"mean(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_mean","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"median","dir":"Articles > Tensor","previous_headings":"","what":"median","title":"Tensor objects","text":"median(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_median","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"min","dir":"Articles > Tensor","previous_headings":"","what":"min","title":"Tensor objects","text":"min(dim=NULL, keepdim=FALSE) -> Tensor (Tensor, Tensor) See ?torch_min","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mm","dir":"Articles > Tensor","previous_headings":"","what":"mm","title":"Tensor objects","text":"mm(mat2) -> Tensor See ?torch_mm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mode","dir":"Articles > Tensor","previous_headings":"","what":"mode","title":"Tensor objects","text":"mode(dim=NULL, keepdim=FALSE) -> (Tensor, LongTensor) See ?torch_mode","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mul","dir":"Articles > Tensor","previous_headings":"","what":"mul","title":"Tensor objects","text":"mul(value) -> Tensor See ?torch_mul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mul_","dir":"Articles > Tensor","previous_headings":"","what":"mul_","title":"Tensor objects","text":"mul_(value) -place version $mul","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"multinomial","dir":"Articles > Tensor","previous_headings":"","what":"multinomial","title":"Tensor objects","text":"multinomial(num_samples, replacement=FALSE, *, generator=NULL) -> Tensor See ?torch_multinomial","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mv","dir":"Articles > Tensor","previous_headings":"","what":"mv","title":"Tensor objects","text":"mv(vec) -> Tensor See ?torch_mv","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mvlgamma","dir":"Articles > Tensor","previous_headings":"","what":"mvlgamma","title":"Tensor objects","text":"mvlgamma(p) -> Tensor See ?torch_mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"mvlgamma_","dir":"Articles > Tensor","previous_headings":"","what":"mvlgamma_","title":"Tensor objects","text":"mvlgamma_(p) -> Tensor -place version $mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"names","dir":"Articles > Tensor","previous_headings":"","what":"names","title":"Tensor objects","text":"Stores names tensor’s dimensions. names[idx] corresponds name tensor dimension idx. Names either string dimension named NULL dimension unnamed. Dimension names may contain characters underscore. Furthermore, dimension name must valid Python variable name (.e., start underscore). Tensors may two named dimensions name.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-4","dir":"Articles > Tensor","previous_headings":"names","what":"Warning:","title":"Tensor objects","text":"named tensor API experimental subject change.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"narrow","dir":"Articles > Tensor","previous_headings":"","what":"narrow","title":"Tensor objects","text":"narrow(dimension, start, length) -> Tensor See ?torch_narrow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-16","dir":"Articles > Tensor","previous_headings":"narrow","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:9, ncol = 3)) x$narrow(1, 1, 3) x$narrow(1, 1, 2)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"narrow_copy","dir":"Articles > Tensor","previous_headings":"","what":"narrow_copy","title":"Tensor objects","text":"narrow_copy(dimension, start, length) -> Tensor Tensor.narrow except returning copy rather shared storage. primarily sparse tensors, shared-storage narrow method. Calling narrow_copy` withdimemsion > self$sparse_dim()`` return copy relevant dense dimension narrowed, ``self$shape`` updated accordingly.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ndim","dir":"Articles > Tensor","previous_headings":"","what":"ndim","title":"Tensor objects","text":"Alias $dim()","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ndimension","dir":"Articles > Tensor","previous_headings":"","what":"ndimension","title":"Tensor objects","text":"ndimension() -> int Alias $dim()","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ne","dir":"Articles > Tensor","previous_headings":"","what":"ne","title":"Tensor objects","text":"ne() -> Tensor See ?torch_ne","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ne_","dir":"Articles > Tensor","previous_headings":"","what":"ne_","title":"Tensor objects","text":"ne_() -> Tensor -place version $ne","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"neg","dir":"Articles > Tensor","previous_headings":"","what":"neg","title":"Tensor objects","text":"neg() -> Tensor See ?torch_neg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"neg_","dir":"Articles > Tensor","previous_headings":"","what":"neg_","title":"Tensor objects","text":"neg_() -> Tensor -place version $neg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"nelement","dir":"Articles > Tensor","previous_headings":"","what":"nelement","title":"Tensor objects","text":"nelement() -> int Alias $numel","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_empty","dir":"Articles > Tensor","previous_headings":"","what":"new_empty","title":"Tensor objects","text":"new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled uninitialized data. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-28","dir":"Articles > Tensor","previous_headings":"new_empty","what":"Arguments:","title":"Tensor objects","text":"dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-17","dir":"Articles > Tensor","previous_headings":"new_empty","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(5) tensor$new_empty(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_full","dir":"Articles > Tensor","previous_headings":"","what":"new_full","title":"Tensor objects","text":"new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled fill_value. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-29","dir":"Articles > Tensor","previous_headings":"new_full","what":"Arguments:","title":"Tensor objects","text":"fill_value (scalar): number fill output tensor . dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-18","dir":"Articles > Tensor","previous_headings":"new_full","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(c(2), dtype=torch_float64()) tensor$new_full(c(3, 4), 3.141592)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_ones","dir":"Articles > Tensor","previous_headings":"","what":"new_ones","title":"Tensor objects","text":"new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled 1. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-30","dir":"Articles > Tensor","previous_headings":"new_ones","what":"Arguments:","title":"Tensor objects","text":"size (int…): list, tuple, torch_Size integers defining shape output tensor. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-19","dir":"Articles > Tensor","previous_headings":"new_ones","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_tensor(c(2), dtype=torch_int32()) tensor$new_ones(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_tensor","dir":"Articles > Tensor","previous_headings":"","what":"new_tensor","title":"Tensor objects","text":"new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns new Tensor data tensor data. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-5","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Warning:","title":"Tensor objects","text":"new_tensor always copies data(). Tensordata` want avoid copy, use [requiresgrad()][requires_grad_()] [detach()]. numpy array want avoid copy, use [torch_from_numpy()]. data tensor x, [new_tensor()()] reads ‘data’ whatever passed, constructs leaf variable. Therefore tensor$new_tensor(x) equivalent x$clone()$detach() tensor$new_tensor(x, requires_grad=TRUE) equivalent x$clone()$detach()$requires_grad_(TRUE). equivalents using clone() detach() recommended.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-31","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Arguments:","title":"Tensor objects","text":"data (array_like): returned Tensor copies data. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-20","dir":"Articles > Tensor","previous_headings":"new_tensor","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_ones(c(2), dtype=torch_int8) data <- matrix(1:4, ncol = 2) tensor$new_tensor(data)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"new_zeros","dir":"Articles > Tensor","previous_headings":"","what":"new_zeros","title":"Tensor objects","text":"new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -> Tensor Returns Tensor size size filled 0. default, returned Tensor torch_dtype torch_device tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-32","dir":"Articles > Tensor","previous_headings":"new_zeros","what":"Arguments:","title":"Tensor objects","text":"size (int…): list, tuple, torch_Size integers defining shape output tensor. dtype (torch_dtype, optional): desired type returned tensor. Default: NULL, torch_dtype tensor. device (torch_device, optional): desired device returned tensor. Default: NULL, torch_device tensor. requires_grad (bool, optional): autograd record operations returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-21","dir":"Articles > Tensor","previous_headings":"new_zeros","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_tensor(c(1), dtype=torch_float64()) tensor$new_zeros(c(2, 3))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"nonzero","dir":"Articles > Tensor","previous_headings":"","what":"nonzero","title":"Tensor objects","text":"nonzero() -> LongTensor See ?torch_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"norm","dir":"Articles > Tensor","previous_headings":"","what":"norm","title":"Tensor objects","text":"See ?torch_norm ## normal_ normal_(mean=0, std=1, *, generator=NULL) -> Tensor Fills self tensor elements samples normal distribution parameterized mean std.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numel","dir":"Articles > Tensor","previous_headings":"","what":"numel","title":"Tensor objects","text":"numel() -> int See ?torch_numel","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"numpy","dir":"Articles > Tensor","previous_headings":"","what":"numpy","title":"Tensor objects","text":"numpy() -> numpy.ndarray Returns self tensor NumPy :class:ndarray. tensor returned ndarray share underlying storage. Changes self tensor reflected :class:ndarray vice versa.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"orgqr","dir":"Articles > Tensor","previous_headings":"","what":"orgqr","title":"Tensor objects","text":"orgqr(input2) -> Tensor See ?torch_orgqr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"ormqr","dir":"Articles > Tensor","previous_headings":"","what":"ormqr","title":"Tensor objects","text":"ormqr(input2, input3, left=TRUE, transpose=FALSE) -> Tensor See ?torch_ormqr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"permute","dir":"Articles > Tensor","previous_headings":"","what":"permute","title":"Tensor objects","text":"permute(*dims) -> Tensor Returns view original tensor dimensions permuted.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-33","dir":"Articles > Tensor","previous_headings":"permute","what":"Arguments:","title":"Tensor objects","text":"dims (int…): desired ordering dimensions","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-22","dir":"Articles > Tensor","previous_headings":"permute","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(2, 3, 5) x$size() x$permute(c(3, 1, 2))$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pin_memory","dir":"Articles > Tensor","previous_headings":"","what":"pin_memory","title":"Tensor objects","text":"pin_memory() -> Tensor Copies tensor pinned memory, ’s already pinned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pinverse","dir":"Articles > Tensor","previous_headings":"","what":"pinverse","title":"Tensor objects","text":"pinverse() -> Tensor See ?torch_pinverse","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"polygamma","dir":"Articles > Tensor","previous_headings":"","what":"polygamma","title":"Tensor objects","text":"polygamma(n) -> Tensor See ?torch_polygamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"polygamma_","dir":"Articles > Tensor","previous_headings":"","what":"polygamma_","title":"Tensor objects","text":"polygamma_(n) -> Tensor -place version $polygamma","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pow","dir":"Articles > Tensor","previous_headings":"","what":"pow","title":"Tensor objects","text":"pow(exponent) -> Tensor See ?torch_pow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"pow_","dir":"Articles > Tensor","previous_headings":"","what":"pow_","title":"Tensor objects","text":"pow_(exponent) -> Tensor -place version $pow","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"prod","dir":"Articles > Tensor","previous_headings":"","what":"prod","title":"Tensor objects","text":"prod(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor See ?torch_prod","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"put_","dir":"Articles > Tensor","previous_headings":"","what":"put_","title":"Tensor objects","text":"put_(indices, tensor, accumulate=FALSE) -> Tensor Copies elements tensor positions specified indices. purpose indexing, self tensor treated 1-D tensor. accumulate TRUE, elements tensor added self. accumulate FALSE, behavior undefined indices contain duplicate elements.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-34","dir":"Articles > Tensor","previous_headings":"put_","what":"Arguments:","title":"Tensor objects","text":"indices (LongTensor): indices self tensor (Tensor): tensor containing values copy accumulate (bool): whether accumulate self","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-23","dir":"Articles > Tensor","previous_headings":"put_","what":"Examples:","title":"Tensor objects","text":"","code":"src <- torch_tensor(matrix(3:8, ncol = 3)) src$put_(torch_tensor(1:2), torch_tensor(9:10))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_axis","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_axis","title":"Tensor objects","text":"q_per_channel_axis() -> int Given Tensor quantized linear (affine) per-channel quantization, returns index dimension per-channel quantization applied.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_scales","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_scales","title":"Tensor objects","text":"q_per_channel_scales() -> Tensor Given Tensor quantized linear (affine) per-channel quantization, returns Tensor scales underlying quantizer. number elements matches corresponding dimensions (q_per_channel_axis) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_per_channel_zero_points","dir":"Articles > Tensor","previous_headings":"","what":"q_per_channel_zero_points","title":"Tensor objects","text":"q_per_channel_zero_points() -> Tensor Given Tensor quantized linear (affine) per-channel quantization, returns tensor zero_points underlying quantizer. number elements matches corresponding dimensions (q_per_channel_axis) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_scale","dir":"Articles > Tensor","previous_headings":"","what":"q_scale","title":"Tensor objects","text":"q_scale() -> float Given Tensor quantized linear(affine) quantization, returns scale underlying quantizer().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"q_zero_point","dir":"Articles > Tensor","previous_headings":"","what":"q_zero_point","title":"Tensor objects","text":"q_zero_point() -> int Given Tensor quantized linear(affine) quantization, returns zero_point underlying quantizer().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"qr","dir":"Articles > Tensor","previous_headings":"","what":"qr","title":"Tensor objects","text":"qr(=TRUE) -> (Tensor, Tensor) See ?torch_qr","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"qscheme","dir":"Articles > Tensor","previous_headings":"","what":"qscheme","title":"Tensor objects","text":"qscheme() -> torch_qscheme Returns quantization scheme given QTensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rad2deg","dir":"Articles > Tensor","previous_headings":"","what":"rad2deg","title":"Tensor objects","text":"rad2deg() -> Tensor See [torch_rad2deg()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rad2deg_","dir":"Articles > Tensor","previous_headings":"","what":"rad2deg_","title":"Tensor objects","text":"rad2deg_() -> Tensor -place version $rad2deg","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"random_","dir":"Articles > Tensor","previous_headings":"","what":"random_","title":"Tensor objects","text":"random_(=0, =NULL, *, generator=NULL) -> Tensor Fills self tensor numbers sampled discrete uniform distribution [, - 1]. specified, values usually bounded self tensor’s data type. However, floating point types, unspecified, range [0, 2^mantissa] ensure every value representable. example, torch_tensor(1, dtype=torch_double).random_() uniform [0, 2^53].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"real","dir":"Articles > Tensor","previous_headings":"","what":"real","title":"Tensor objects","text":"Returns new tensor containing real values self tensor. returned tensor self share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-6","dir":"Articles > Tensor","previous_headings":"real","what":"Warning:","title":"Tensor objects","text":"[real()] supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-24","dir":"Articles > Tensor","previous_headings":"real","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_randn(4, dtype=torch_cfloat()) x x$real"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reciprocal","dir":"Articles > Tensor","previous_headings":"","what":"reciprocal","title":"Tensor objects","text":"reciprocal() -> Tensor See ?torch_reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reciprocal_","dir":"Articles > Tensor","previous_headings":"","what":"reciprocal_","title":"Tensor objects","text":"reciprocal_() -> Tensor -place version $reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"record_stream","dir":"Articles > Tensor","previous_headings":"","what":"record_stream","title":"Tensor objects","text":"record_stream(stream) Ensures tensor memory reused another tensor current work queued stream complete.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-6","dir":"Articles > Tensor","previous_headings":"record_stream","what":"Note:","title":"Tensor objects","text":"caching allocator aware stream tensor allocated. Due awareness, already correctly manages life cycle tensors one stream. tensor used stream different stream origin, allocator might reuse memory unexpectedly. Calling method lets allocator know streams used tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"refine_names","dir":"Articles > Tensor","previous_headings":"","what":"refine_names","title":"Tensor objects","text":"Refines dimension names self according names. Refining special case renaming “lifts” unnamed dimensions. NULL dim can refined name; named dim can refined name. named tensors can coexist unnamed tensors, refining names gives nice way write named-tensor-aware code works named unnamed tensors. names may contain one Ellipsis (...). Ellipsis expanded greedily; expanded -place fill names length self$dim() using names corresponding indices self$names.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-35","dir":"Articles > Tensor","previous_headings":"refine_names","what":"Arguments:","title":"Tensor objects","text":"names (iterable str): desired names output tensor. May contain one Ellipsis.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-25","dir":"Articles > Tensor","previous_headings":"refine_names","what":"Examples:","title":"Tensor objects","text":"","code":"imgs <- torch_randn(32, 3, 128, 128) named_imgs <- imgs$refine_names(c('N', 'C', 'H', 'W')) named_imgs$names"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"register_hook","dir":"Articles > Tensor","previous_headings":"","what":"register_hook","title":"Tensor objects","text":"Registers backward hook. hook called every time gradient respect Tensor computed. hook following signature:: hook(grad) -> Tensor NULL hook modify argument, can optionally return new gradient used place grad. function returns handle method handle$remove() removes hook module.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"example","dir":"Articles > Tensor","previous_headings":"register_hook","what":"Example","title":"Tensor objects","text":"","code":"v <- torch_tensor(c(0., 0., 0.), requires_grad=TRUE) h <- v$register_hook(function(grad) grad * 2)  # double the gradient v$backward(torch_tensor(c(1., 2., 3.))) v$grad h$remove()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"remainder","dir":"Articles > Tensor","previous_headings":"","what":"remainder","title":"Tensor objects","text":"remainder(divisor) -> Tensor See ?torch_remainder","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"remainder_","dir":"Articles > Tensor","previous_headings":"","what":"remainder_","title":"Tensor objects","text":"remainder_(divisor) -> Tensor -place version $remainder","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rename","dir":"Articles > Tensor","previous_headings":"","what":"rename","title":"Tensor objects","text":"Renames dimension names self. two main usages: self$rename(**rename_map) returns view tensor dims renamed specified mapping rename_map. self$rename(*names) returns view tensor, renaming dimensions positionally using names. Use self$rename(NULL) drop names tensor. One specify positional args names keyword args rename_map.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-26","dir":"Articles > Tensor","previous_headings":"rename","what":"Examples:","title":"Tensor objects","text":"","code":"imgs <- torch_rand(2, 3, 5, 7, names=c('N', 'C', 'H', 'W')) renamed_imgs <- imgs$rename(c(\"Batch\", \"Channels\", \"Height\", \"Width\"))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rename_","dir":"Articles > Tensor","previous_headings":"","what":"rename_","title":"Tensor objects","text":"-place version $rename.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"renorm","dir":"Articles > Tensor","previous_headings":"","what":"renorm","title":"Tensor objects","text":"renorm(p, dim, maxnorm) -> Tensor See ?torch_renorm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"renorm_","dir":"Articles > Tensor","previous_headings":"","what":"renorm_","title":"Tensor objects","text":"renorm_(p, dim, maxnorm) -> Tensor -place version $renorm","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"repeat","dir":"Articles > Tensor","previous_headings":"","what":"repeat","title":"Tensor objects","text":"repeat(*sizes) -> Tensor Repeats tensor along specified dimensions. Unlike $expand, function copies tensor’s data.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-36","dir":"Articles > Tensor","previous_headings":"repeat","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): number times repeat tensor along dimension","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-27","dir":"Articles > Tensor","previous_headings":"repeat","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(c(1, 2, 3)) x$`repeat`(c(4, 2)) x$`repeat`(c(4, 2, 1))$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"repeat_interleave","dir":"Articles > Tensor","previous_headings":"","what":"repeat_interleave","title":"Tensor objects","text":"repeat_interleave(repeats, dim=NULL) -> Tensor See [torch_repeat_interleave()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"requires_grad","dir":"Articles > Tensor","previous_headings":"","what":"requires_grad","title":"Tensor objects","text":"TRUE gradients need computed Tensor, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-7","dir":"Articles > Tensor","previous_headings":"requires_grad","what":"Note:","title":"Tensor objects","text":"fact gradients need computed Tensor mean grad attribute populated, see is_leaf details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"requires_grad_","dir":"Articles > Tensor","previous_headings":"","what":"requires_grad_","title":"Tensor objects","text":"requires_grad_(requires_grad=TRUE) -> Tensor Change autograd record operations tensor: sets tensor’s requires_grad attribute -place. Returns tensor. [requires_grad_()]’s main use case tell autograd begin recording operations Tensor tensor. tensor requires_grad=FALSE (obtained DataLoader, required preprocessing initialization), tensor.requires_grad_() makes autograd begin record operations tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-37","dir":"Articles > Tensor","previous_headings":"requires_grad_","what":"Arguments:","title":"Tensor objects","text":"requires_grad (bool): autograd record operations tensor. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-28","dir":"Articles > Tensor","previous_headings":"requires_grad_","what":"Examples:","title":"Tensor objects","text":"","code":"# Let's say we want to preprocess some saved weights and use # the result as new weights. saved_weights <- c(0.1, 0.2, 0.3, 0.25) loaded_weights <- torch_tensor(saved_weights) weights <- preprocess(loaded_weights)  # some function weights  # Now, start to record operations done to weights weights$requires_grad_() out <- weights$pow(2)$sum() out$backward() weights$grad"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reshape","dir":"Articles > Tensor","previous_headings":"","what":"reshape","title":"Tensor objects","text":"reshape(*shape) -> Tensor Returns tensor data number elements self specified shape. method returns view shape compatible current shape. See $view possible return view. See ?torch_reshape","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-38","dir":"Articles > Tensor","previous_headings":"reshape","what":"Arguments:","title":"Tensor objects","text":"shape (tuple ints int…): desired shape","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"reshape_as","dir":"Articles > Tensor","previous_headings":"","what":"reshape_as","title":"Tensor objects","text":"reshape_as() -> Tensor Returns tensor shape . self$reshape_as() equivalent self$reshape(.sizes()). method returns view .sizes() compatible current shape. See $view possible return view. Please see reshape information reshape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-39","dir":"Articles > Tensor","previous_headings":"reshape_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor shape .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"resize_","dir":"Articles > Tensor","previous_headings":"","what":"resize_","title":"Tensor objects","text":"resize_(*sizes, memory_format=torch_contiguous_format) -> Tensor Resizes self tensor specified size. number elements larger current storage size, underlying storage resized fit new number elements. number elements smaller, underlying storage changed. Existing elements preserved new memory uninitialized.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"warning-7","dir":"Articles > Tensor","previous_headings":"resize_","what":"Warning:","title":"Tensor objects","text":"low-level method. storage reinterpreted C-contiguous, ignoring current strides (unless target size equals current size, case tensor left unchanged). purposes, instead want use $view(), checks contiguity, $reshape(), copies data needed. change size -place custom strides, see $set_().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-40","dir":"Articles > Tensor","previous_headings":"resize_","what":"Arguments:","title":"Tensor objects","text":"sizes (torch_Size int…): desired size memory_format (torch_memory_format, optional): desired memory format Tensor. Default: torch_contiguous_format. Note memory format self going unaffected self$size() matches sizes.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-29","dir":"Articles > Tensor","previous_headings":"resize_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:6, ncol = 2)) x$resize_(c(2, 2))"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"resize_as_","dir":"Articles > Tensor","previous_headings":"","what":"resize_as_","title":"Tensor objects","text":"resize_as_(tensor, memory_format=torch_contiguous_format) -> Tensor Resizes self tensor size specified tensor. equivalent self$resize_(tensor.size()).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-41","dir":"Articles > Tensor","previous_headings":"resize_as_","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format Tensor. Default: torch_contiguous_format. Note memory format self going unaffected self$size() matches tensor.size().","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"retain_grad","dir":"Articles > Tensor","previous_headings":"","what":"retain_grad","title":"Tensor objects","text":"Enables $grad attribute non-leaf Tensors.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rfft","dir":"Articles > Tensor","previous_headings":"","what":"rfft","title":"Tensor objects","text":"rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -> Tensor See ?torch_rfft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"roll","dir":"Articles > Tensor","previous_headings":"","what":"roll","title":"Tensor objects","text":"roll(shifts, dims) -> Tensor See ?torch_roll","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rot90","dir":"Articles > Tensor","previous_headings":"","what":"rot90","title":"Tensor objects","text":"rot90(k, dims) -> Tensor See [torch_rot90()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"round","dir":"Articles > Tensor","previous_headings":"","what":"round","title":"Tensor objects","text":"round() -> Tensor See ?torch_round","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"round_","dir":"Articles > Tensor","previous_headings":"","what":"round_","title":"Tensor objects","text":"round_() -> Tensor -place version $round","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rsqrt","dir":"Articles > Tensor","previous_headings":"","what":"rsqrt","title":"Tensor objects","text":"rsqrt() -> Tensor See ?torch_rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"rsqrt_","dir":"Articles > Tensor","previous_headings":"","what":"rsqrt_","title":"Tensor objects","text":"rsqrt_() -> Tensor -place version $rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter","dir":"Articles > Tensor","previous_headings":"","what":"scatter","title":"Tensor objects","text":"scatter(dim, index, src) -> Tensor --place version $scatter_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_","dir":"Articles > Tensor","previous_headings":"","what":"scatter_","title":"Tensor objects","text":"scatter_(dim, index, src) -> Tensor Writes values tensor src self indices specified index tensor. value src, output index specified index src dimension != dim corresponding value index dimension = dim. 3-D tensor, self updated : reverse operation manner described $gather. self, index src (Tensor) number dimensions. also required index.size(d) <= src.size(d) dimensions d, index.size(d) <= self$size(d) dimensions d != dim. Moreover, $gather, values index must 0 self$size(dim) - 1 inclusive, values row along specified dimension dim must unique.","code":"self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0 self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1 self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-42","dir":"Articles > Tensor","previous_headings":"scatter_","what":"Arguments:","title":"Tensor objects","text":"dim (int): axis along index index (LongTensor): indices elements scatter, can either empty size src. empty, operation returns identity src (Tensor): source element(s) scatter, incase value specified value (float): source element(s) scatter, incase src specified","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-30","dir":"Articles > Tensor","previous_headings":"scatter_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_rand(2, 5) x torch_zeros(3, 5)$scatter_(         1,          torch_tensor(rbind(c(2, 3, 3, 1, 1), c(3, 1, 1, 2, 3)), x) )  z <- torch_zeros(2, 4)$scatter_(         2,          torch_tensor(matrix(3:4, ncol = 1)), 1.23 )"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_add","dir":"Articles > Tensor","previous_headings":"","what":"scatter_add","title":"Tensor objects","text":"scatter_add(dim, index, src) -> Tensor --place version $scatter_add_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"scatter_add_","dir":"Articles > Tensor","previous_headings":"","what":"scatter_add_","title":"Tensor objects","text":"scatter_add_(dim, index, src) -> Tensor Adds values tensor self indices specified index tensor similar fashion ~$scatter_. value src, added index self specified index src dimension != dim corresponding value index dimension = dim. 3-D tensor, self updated :: self, index src number dimensions. also required index.size(d) <= src.size(d) dimensions d, index.size(d) <= self$size(d) dimensions d != dim.","code":"self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0 self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1 self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-8","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Note:","title":"Tensor objects","text":"circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch_backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-43","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Arguments:","title":"Tensor objects","text":"dim (int): axis along index index (LongTensor): indices elements scatter add, can either empty size src. empty, operation returns identity. src (Tensor): source elements scatter add","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-31","dir":"Articles > Tensor","previous_headings":"scatter_add_","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_rand(2, 5) x torch_ones(3, 5)$scatter_add_(1, torch_tensor(rbind(c(0, 1, 2, 0, 0), c(2, 0, 0, 1, 2))), x)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"select","dir":"Articles > Tensor","previous_headings":"","what":"select","title":"Tensor objects","text":"select(dim, index) -> Tensor Slices self tensor along selected dimension given index. function returns view original tensor given dimension removed.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-44","dir":"Articles > Tensor","previous_headings":"select","what":"Arguments:","title":"Tensor objects","text":"dim (int): dimension slice index (int): index select ","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-9","dir":"Articles > Tensor","previous_headings":"select","what":"Note:","title":"Tensor objects","text":"select equivalent slicing. example, tensor$select(0, index) equivalent tensor[index] tensor$select(2, index) equivalent tensor[:,:,index].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"set_","dir":"Articles > Tensor","previous_headings":"","what":"set_","title":"Tensor objects","text":"set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -> Tensor Sets underlying storage, size, strides. source tensor, self tensor share storage size strides source. Changes elements one tensor reflected .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-45","dir":"Articles > Tensor","previous_headings":"set_","what":"Arguments:","title":"Tensor objects","text":"source (Tensor Storage): tensor storage use storage_offset (int, optional): offset storage size (torch_Size, optional): desired size. Defaults size source. stride (tuple, optional): desired stride. Defaults C-contiguous strides.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"share_memory_","dir":"Articles > Tensor","previous_headings":"","what":"share_memory_","title":"Tensor objects","text":"Moves underlying storage shared memory. -op underlying storage already shared memory CUDA tensors. Tensors shared memory resized.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"short","dir":"Articles > Tensor","previous_headings":"","what":"short","title":"Tensor objects","text":"short(memory_format=torch_preserve_format) -> Tensor self$short() equivalent self$(torch_int16). See [()].","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-46","dir":"Articles > Tensor","previous_headings":"short","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sigmoid","dir":"Articles > Tensor","previous_headings":"","what":"sigmoid","title":"Tensor objects","text":"sigmoid() -> Tensor See ?torch_sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sigmoid_","dir":"Articles > Tensor","previous_headings":"","what":"sigmoid_","title":"Tensor objects","text":"sigmoid_() -> Tensor -place version $sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sign","dir":"Articles > Tensor","previous_headings":"","what":"sign","title":"Tensor objects","text":"sign() -> Tensor See ?torch_sign","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sign_","dir":"Articles > Tensor","previous_headings":"","what":"sign_","title":"Tensor objects","text":"sign_() -> Tensor -place version $sign","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sin","dir":"Articles > Tensor","previous_headings":"","what":"sin","title":"Tensor objects","text":"sin() -> Tensor See ?torch_sin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sin_","dir":"Articles > Tensor","previous_headings":"","what":"sin_","title":"Tensor objects","text":"sin_() -> Tensor -place version $sin","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sinh","dir":"Articles > Tensor","previous_headings":"","what":"sinh","title":"Tensor objects","text":"sinh() -> Tensor See ?torch_sinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sinh_","dir":"Articles > Tensor","previous_headings":"","what":"sinh_","title":"Tensor objects","text":"sinh_() -> Tensor -place version $sinh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"size","dir":"Articles > Tensor","previous_headings":"","what":"size","title":"Tensor objects","text":"size() -> torch_Size Returns size self tensor. returned value subclass tuple.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-32","dir":"Articles > Tensor","previous_headings":"size","what":"Examples:","title":"Tensor objects","text":"","code":"torch_empty(3, 4, 5)$size()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"slogdet","dir":"Articles > Tensor","previous_headings":"","what":"slogdet","title":"Tensor objects","text":"slogdet() -> (Tensor, Tensor) See ?torch_slogdet","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sort","dir":"Articles > Tensor","previous_headings":"","what":"sort","title":"Tensor objects","text":"sort(dim=-1, descending=FALSE) -> (Tensor, LongTensor) See ?torch_sort","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sparse_dim","dir":"Articles > Tensor","previous_headings":"","what":"sparse_dim","title":"Tensor objects","text":"sparse_dim() -> int self sparse COO tensor (.e., torch_sparse_coo layout), returns number sparse dimensions. Otherwise, throws error. See also Tensor.dense_dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sparse_mask","dir":"Articles > Tensor","previous_headings":"","what":"sparse_mask","title":"Tensor objects","text":"sparse_mask(input, mask) -> Tensor Returns new SparseTensor values Tensor input filtered indices mask values ignored. input mask must shape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-47","dir":"Articles > Tensor","previous_headings":"sparse_mask","what":"Arguments:","title":"Tensor objects","text":"input (Tensor): input Tensor mask (SparseTensor): SparseTensor filter input based indices","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"split","dir":"Articles > Tensor","previous_headings":"","what":"split","title":"Tensor objects","text":"See ?torch_split","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sqrt","dir":"Articles > Tensor","previous_headings":"","what":"sqrt","title":"Tensor objects","text":"sqrt() -> Tensor See ?torch_sqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sqrt_","dir":"Articles > Tensor","previous_headings":"","what":"sqrt_","title":"Tensor objects","text":"sqrt_() -> Tensor -place version $sqrt","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"square","dir":"Articles > Tensor","previous_headings":"","what":"square","title":"Tensor objects","text":"square() -> Tensor See ?torch_square","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"square_","dir":"Articles > Tensor","previous_headings":"","what":"square_","title":"Tensor objects","text":"square_() -> Tensor -place version $square","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"squeeze","dir":"Articles > Tensor","previous_headings":"","what":"squeeze","title":"Tensor objects","text":"squeeze(dim=NULL) -> Tensor See ?torch_squeeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"squeeze_","dir":"Articles > Tensor","previous_headings":"","what":"squeeze_","title":"Tensor objects","text":"squeeze_(dim=NULL) -> Tensor -place version $squeeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"std","dir":"Articles > Tensor","previous_headings":"","what":"std","title":"Tensor objects","text":"std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor See ?torch_std","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"stft","dir":"Articles > Tensor","previous_headings":"","what":"stft","title":"Tensor objects","text":"See ?torch_stft","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage","dir":"Articles > Tensor","previous_headings":"","what":"storage","title":"Tensor objects","text":"storage() -> torch_Storage Returns underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage_offset","dir":"Articles > Tensor","previous_headings":"","what":"storage_offset","title":"Tensor objects","text":"storage_offset() -> int Returns self tensor’s offset underlying storage terms number storage elements (bytes).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-33","dir":"Articles > Tensor","previous_headings":"storage_offset","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(c(1, 2, 3, 4, 5)) x$storage_offset() x[3:N]$storage_offset()"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"storage_type","dir":"Articles > Tensor","previous_headings":"","what":"storage_type","title":"Tensor objects","text":"storage_type() -> type Returns type underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"stride","dir":"Articles > Tensor","previous_headings":"","what":"stride","title":"Tensor objects","text":"stride(dim) -> tuple int Returns stride self tensor. Stride jump necessary go one element next one specified dimension dim. tuple strides returned argument passed . Otherwise, integer value returned stride particular dimension dim.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-48","dir":"Articles > Tensor","previous_headings":"stride","what":"Arguments:","title":"Tensor objects","text":"dim (int, optional): desired dimension stride required","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-34","dir":"Articles > Tensor","previous_headings":"stride","what":"Examples:","title":"Tensor objects","text":"","code":"x <- torch_tensor(matrix(1:10, nrow = 2)) x$stride() x$stride(1) x$stride(-1)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sub","dir":"Articles > Tensor","previous_headings":"","what":"sub","title":"Tensor objects","text":"sub(, *, alpha=1) -> Tensor Subtracts scalar tensor self tensor. alpha specified, element scaled alpha used. tensor, shape must broadcastable <broadcasting-semantics> shape underlying tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sub_","dir":"Articles > Tensor","previous_headings":"","what":"sub_","title":"Tensor objects","text":"sub_(, *, alpha=1) -> Tensor -place version $sub","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sum","dir":"Articles > Tensor","previous_headings":"","what":"sum","title":"Tensor objects","text":"sum(dim=NULL, keepdim=FALSE, dtype=NULL) -> Tensor See ?torch_sum","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"sum_to_size","dir":"Articles > Tensor","previous_headings":"","what":"sum_to_size","title":"Tensor objects","text":"sum_to_size(*size) -> Tensor Sum tensor size. size must broadcastable tensor size.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-49","dir":"Articles > Tensor","previous_headings":"sum_to_size","what":"Arguments:","title":"Tensor objects","text":"size (int…): sequence integers defining shape output tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"svd","dir":"Articles > Tensor","previous_headings":"","what":"svd","title":"Tensor objects","text":"svd(=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor) See ?torch_svd","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"t","dir":"Articles > Tensor","previous_headings":"","what":"t","title":"Tensor objects","text":"t() -> Tensor See ?torch_t","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"t_","dir":"Articles > Tensor","previous_headings":"","what":"t_","title":"Tensor objects","text":"t_() -> Tensor -place version $t","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"take","dir":"Articles > Tensor","previous_headings":"","what":"take","title":"Tensor objects","text":"take(indices) -> Tensor See ?torch_take","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tan","dir":"Articles > Tensor","previous_headings":"","what":"tan","title":"Tensor objects","text":"tan() -> Tensor See ?torch_tan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tan_","dir":"Articles > Tensor","previous_headings":"","what":"tan_","title":"Tensor objects","text":"tan_() -> Tensor -place version $tan","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tanh","dir":"Articles > Tensor","previous_headings":"","what":"tanh","title":"Tensor objects","text":"tanh() -> Tensor See ?torch_tanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tanh_","dir":"Articles > Tensor","previous_headings":"","what":"tanh_","title":"Tensor objects","text":"tanh_() -> Tensor -place version $tanh","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to","dir":"Articles > Tensor","previous_headings":"","what":"to","title":"Tensor objects","text":"(*args, **kwargs) -> Tensor Performs Tensor dtype /device conversion. torch_dtype :class:torch_device inferred arguments self$(*args, **kwargs).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-10","dir":"Articles > Tensor","previous_headings":"to","what":"Note:","title":"Tensor objects","text":"self Tensor already correct torch_dtype :class:torch_device, self returned. Otherwise, returned tensor copy self desired torch_dtype :class:torch_device. ways call : (dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor Returns Tensor specified dtype","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-50","dir":"Articles > Tensor","previous_headings":"to","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format. (device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -> Tensor Returns Tensor specified device (optional) dtype. dtype NULL inferred self$dtype. non_blocking, tries convert asynchronously respect host possible, e.g., converting CPU Tensor pinned memory CUDA Tensor. copy set, new Tensor created even Tensor already matches desired conversion.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-51","dir":"Articles > Tensor","previous_headings":"to","what":"Arguments:","title":"Tensor objects","text":"memory_format (torch_memory_format, optional): desired memory format returned Tensor. Default: torch_preserve_format. function:: (, non_blocking=FALSE, copy=FALSE) -> Tensor Returns Tensor torch_dtype :class:torch_device Tensor . non_blocking, tries convert asynchronously respect host possible, e.g., converting CPU Tensor pinned memory CUDA Tensor. copy set, new Tensor created even Tensor already matches desired conversion.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"examples-35","dir":"Articles > Tensor","previous_headings":"to","what":"Examples:","title":"Tensor objects","text":"","code":"tensor <- torch_randn(2, 2)  # Initially dtype=float32, device=cpu tensor$to(dtype = torch_float64())  other <- torch_randn(1, dtype=torch_float64()) tensor$to(other = other, non_blocking=TRUE)"},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to_mkldnn","dir":"Articles > Tensor","previous_headings":"","what":"to_mkldnn","title":"Tensor objects","text":"to_mkldnn() -> Tensor Returns copy tensor torch_mkldnn layout.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"to_sparse","dir":"Articles > Tensor","previous_headings":"","what":"to_sparse","title":"Tensor objects","text":"to_sparse(sparseDims) -> Tensor Returns sparse copy tensor. PyTorch supports sparse tensors coordinate format <sparse-docs>.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-52","dir":"Articles > Tensor","previous_headings":"to_sparse","what":"Arguments:","title":"Tensor objects","text":"sparseDims (int, optional): number sparse dimensions include new sparse tensor","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tolist","dir":"Articles > Tensor","previous_headings":"","what":"tolist","title":"Tensor objects","text":"tolist() -> list number Returns tensor (nested) list. scalars, standard Python number returned, just like $item. Tensors automatically moved CPU first necessary. operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"topk","dir":"Articles > Tensor","previous_headings":"","what":"topk","title":"Tensor objects","text":"topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor) See ?torch_topk","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trace","dir":"Articles > Tensor","previous_headings":"","what":"trace","title":"Tensor objects","text":"trace() -> Tensor See ?torch_trace","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"transpose","dir":"Articles > Tensor","previous_headings":"","what":"transpose","title":"Tensor objects","text":"transpose(dim0, dim1) -> Tensor See ?torch_transpose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"transpose_","dir":"Articles > Tensor","previous_headings":"","what":"transpose_","title":"Tensor objects","text":"transpose_(dim0, dim1) -> Tensor -place version $transpose","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triangular_solve","dir":"Articles > Tensor","previous_headings":"","what":"triangular_solve","title":"Tensor objects","text":"triangular_solve(, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -> (Tensor, Tensor) See [torch_triangular_solve()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tril","dir":"Articles > Tensor","previous_headings":"","what":"tril","title":"Tensor objects","text":"tril(k=0) -> Tensor See ?torch_tril","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"tril_","dir":"Articles > Tensor","previous_headings":"","what":"tril_","title":"Tensor objects","text":"tril_(k=0) -> Tensor -place version $tril","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triu","dir":"Articles > Tensor","previous_headings":"","what":"triu","title":"Tensor objects","text":"triu(k=0) -> Tensor See ?torch_triu","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"triu_","dir":"Articles > Tensor","previous_headings":"","what":"triu_","title":"Tensor objects","text":"triu_(k=0) -> Tensor -place version $triu","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"true_divide","dir":"Articles > Tensor","previous_headings":"","what":"true_divide","title":"Tensor objects","text":"true_divide(value) -> Tensor See [torch_true_divide()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"true_divide_","dir":"Articles > Tensor","previous_headings":"","what":"true_divide_","title":"Tensor objects","text":"true_divide_(value) -> Tensor -place version $true_divide_","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trunc","dir":"Articles > Tensor","previous_headings":"","what":"trunc","title":"Tensor objects","text":"trunc() -> Tensor See ?torch_trunc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"trunc_","dir":"Articles > Tensor","previous_headings":"","what":"trunc_","title":"Tensor objects","text":"trunc_() -> Tensor -place version $trunc","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"type","dir":"Articles > Tensor","previous_headings":"","what":"type","title":"Tensor objects","text":"type(dtype=NULL, non_blocking=FALSE, **kwargs) -> str Tensor Returns type dtype provided, else casts object specified type. already correct type, copy performed original object returned.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-53","dir":"Articles > Tensor","previous_headings":"type","what":"Arguments:","title":"Tensor objects","text":"dtype (type string): desired type non_blocking (bool): TRUE, source pinned memory destination GPU vice versa, copy performed asynchronously respect host. Otherwise, argument effect. **kwargs: compatibility, may contain key async place non_blocking argument. async arg deprecated.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"type_as","dir":"Articles > Tensor","previous_headings":"","what":"type_as","title":"Tensor objects","text":"type_as(tensor) -> Tensor Returns tensor cast type given tensor. -op tensor already correct type. equivalent self$type(tensor.type())","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-54","dir":"Articles > Tensor","previous_headings":"type_as","what":"Arguments:","title":"Tensor objects","text":"tensor (Tensor): tensor desired type","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unbind","dir":"Articles > Tensor","previous_headings":"","what":"unbind","title":"Tensor objects","text":"unbind(dim=0) -> seq See ?torch_unbind","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unflatten","dir":"Articles > Tensor","previous_headings":"","what":"unflatten","title":"Tensor objects","text":"Unflattens named dimension dim, viewing shape specified namedshape.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-55","dir":"Articles > Tensor","previous_headings":"unflatten","what":"Arguments:","title":"Tensor objects","text":"namedshape: (iterable (name, size) tuples).","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unfold","dir":"Articles > Tensor","previous_headings":"","what":"unfold","title":"Tensor objects","text":"unfold(dimension, size, step) -> Tensor Returns view original tensor contains slices size size self tensor dimension dimension. Step two slices given step. sizedim size dimension dimension self, size dimension dimension returned tensor (sizedim - size) / step + 1. additional dimension size size appended returned tensor.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-56","dir":"Articles > Tensor","previous_headings":"unfold","what":"Arguments:","title":"Tensor objects","text":"dimension (int): dimension unfolding happens size (int): size slice unfolded step (int): step slice","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"uniform_","dir":"Articles > Tensor","previous_headings":"","what":"uniform_","title":"Tensor objects","text":"uniform_(=0, =1) -> Tensor Fills self tensor numbers sampled continuous uniform distribution: P(x)=1to−P(x) = \\dfrac{1}{\\text{} - \\text{}}","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unique","dir":"Articles > Tensor","previous_headings":"","what":"unique","title":"Tensor objects","text":"Returns unique elements input tensor. See ?torch_unique","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unique_consecutive","dir":"Articles > Tensor","previous_headings":"","what":"unique_consecutive","title":"Tensor objects","text":"Eliminates first element every consecutive group equivalent elements. See [torch_unique_consecutive()]","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unsqueeze","dir":"Articles > Tensor","previous_headings":"","what":"unsqueeze","title":"Tensor objects","text":"unsqueeze(dim) -> Tensor See ?torch_unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"unsqueeze_","dir":"Articles > Tensor","previous_headings":"","what":"unsqueeze_","title":"Tensor objects","text":"unsqueeze_(dim) -> Tensor -place version $unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"values","dir":"Articles > Tensor","previous_headings":"","what":"values","title":"Tensor objects","text":"values() -> Tensor self sparse COO tensor (.e., torch_sparse_coo layout), returns view contained values tensor. Otherwise, throws error.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"note-11","dir":"Articles > Tensor","previous_headings":"values","what":"Note:","title":"Tensor objects","text":"method can called coalesced sparse tensor. See Tensor$coalesce details.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"var","dir":"Articles > Tensor","previous_headings":"","what":"var","title":"Tensor objects","text":"var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -> Tensor See ?torch_var","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"view","dir":"Articles > Tensor","previous_headings":"","what":"view","title":"Tensor objects","text":"view(*shape) -> Tensor Returns new tensor data self tensor different shape. returned tensor shares data must number elements, may different size. tensor viewed, new view size must compatible original size stride, .e., new view dimension must either subspace original dimension, span across original dimensions d, d+1, \\dots, d+k satisfy following contiguity-like condition \\forall = d, \\dots, d+k-1, stride[]=stride[+1]×size[+1] \\text{stride}[] = \\text{stride}[+1] \\times \\text{size}[+1] Otherwise, possible view self tensor shape without copying (e.g., via contiguous). unclear whether view can performed, advisable use :meth:reshape, returns view shapes compatible, copies (equivalent calling contiguous) otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-57","dir":"Articles > Tensor","previous_headings":"view","what":"Arguments:","title":"Tensor objects","text":"shape (torch_Size int…): desired size","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"view_as","dir":"Articles > Tensor","previous_headings":"","what":"view_as","title":"Tensor objects","text":"view_as() -> Tensor View tensor size . self$view_as() equivalent self$view(.size()). Please see $view information view.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"arguments-58","dir":"Articles > Tensor","previous_headings":"view_as","what":"Arguments:","title":"Tensor objects","text":"(`$): result tensor size .","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"where","dir":"Articles > Tensor","previous_headings":"","what":"where","title":"Tensor objects","text":"(condition, y) -> Tensor self$(condition, y) equivalent torch_where(condition, self, y). See ?torch_where","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor/index.html","id":"zero_","dir":"Articles > Tensor","previous_headings":"","what":"zero_","title":"Tensor objects","text":"zero_() -> Tensor Fills self tensor zeros.","code":""},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"from-r-objects","dir":"Articles","previous_headings":"","what":"From R objects","title":"Creating tensors","text":"can create tensors R objects using torch_tensor function. torch_tensor function takes R vector, matrix array creates equivalent torch_tensor. can see examples : default, create tensors cpu device, converting R datatype corresponding torch dtype. Note currently, numeric boolean types supported. can always modify dtype device converting R object torch tensor. example: options available creating tensor : requires_grad: boolean indicating want autograd record operations automatic differentiation. pin_memory: – set, tensor returned allocated pinned memory. Works CPU tensors. options available functions can used create new tensors, including factory functions listed next section.","code":"torch_tensor(c(1,2,3)) #> torch_tensor #>  1 #>  2 #>  3 #> [ CPUFloatType{3} ]  # conform to row-major indexing used in torch torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE)) #> torch_tensor #>   1   2   3   4   5 #>   6   7   8   9  10 #> [ CPULongType{2,5} ] torch_tensor(array(runif(12), dim = c(2, 2, 3))) #> torch_tensor #> (1,.,.) =  #>   0.6008  0.4978  0.8746 #>   0.0074  0.7329  0.0342 #>  #> (2,.,.) =  #>   0.1572  0.2898  0.1749 #>   0.4664  0.7725  0.3204 #> [ CPUFloatType{2,2,3} ] torch_tensor(1, dtype = torch_long()) #> torch_tensor #>  1 #> [ CPULongType{1} ] torch_tensor(1, device = \"cpu\", dtype = torch_float64()) #> torch_tensor #>  1 #> [ CPUDoubleType{1} ]"},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"using-creation-functions","dir":"Articles","previous_headings":"","what":"Using creation functions","title":"Creating tensors","text":"can also use torch_* functions listed create torch tensors using algorithm. example, torch_randn function create tensors using normal distribution mean 0 standard deviation 1. can use ... argument pass size dimensions. example, code create normally distributed tensor shape 5x3. Another example torch_ones, creates tensor filled ones. full list functions can used bulk-create tensors torch: torch_arange: Returns tensor sequence integers, torch_empty: Returns tensor uninitialized values, torch_eye: Returns identity matrix, torch_full: Returns tensor filled single value, torch_linspace: Returns tensor values linearly spaced interval, torch_logspace: Returns tensor values logarithmically spaced interval, torch_ones: Returns tensor filled ones, torch_rand: Returns tensor filled values drawn uniform distribution [0, 1). torch_randint: Returns tensor integers randomly drawn interval, torch_randn: Returns tensor filled values drawn unit normal distribution, torch_randperm: Returns tensor filled random permutation integers interval, torch_zeros: Returns tensor filled zeros.","code":"x <- torch_randn(5, 3) x #> torch_tensor #> -0.7873 -0.3195 -0.6137 #> -0.5345  0.5600 -0.5529 #> -0.8468 -2.1746  0.1606 #>  0.5567  2.3597  1.1621 #>  1.0835 -2.1803 -1.1921 #> [ CPUFloatType{5,3} ] x <- torch_ones(2, 4, dtype = torch_int64(), device = \"cpu\") x #> torch_tensor #>  1  1  1  1 #>  1  1  1  1 #> [ CPULongType{2,4} ]"},{"path":"https://torch.mlverse.org/docs/articles/tensor-creation.html","id":"conversion","dir":"Articles","previous_headings":"","what":"Conversion","title":"Creating tensors","text":"tensor exists can convert dtypes move different device method. example: can also copy tensor GPU using:","code":"x <- torch_tensor(1) y <- x$to(dtype = torch_int32()) x #> torch_tensor #>  1 #> [ CPUFloatType{1} ] y #> torch_tensor #>  1 #> [ CPUIntType{1} ] x <- torch_tensor(1) y <- x$cuda())"},{"path":[]},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"tracing","dir":"Articles","previous_headings":"Creating TorchScript programs","what":"Tracing","title":"TorchScript","text":"TorchScript programs can created R using tracing. using tracing, code automatically converted subset Python recording actual operators tensors simply executing discarding surrounding R code. Currently tracing supported way create TorchScript programs R code. example, let’s use jit_trace function create TorchScript program. pass regular R function example inputs. jit_trace function executed R function example input recorded torch operations occurred execution create graph. graph call intermediate representation TorchScript programs, can inspected : traced function can now invoked regular R function: ’s also possible trace nn_modules() defined R, example: using jit_trace nn_module forward method traced. However, default, one pass conducted ‘train’ mode, another one ‘eval’ mode, different PyTorch behavior. One can opt specifying respect_mode = FALSE trace forward pass mode network currently . can use jit_trace_module function pass example inputs methods. Traced modules look like normal nn_modules(), can called way:","code":"fn <- function(x) {   torch_relu(x) }  traced_fn <- jit_trace(fn, torch_tensor(c(-1, 0, 1))) traced_fn$graph #> graph(%0 : Float(3, strides=[1], requires_grad=0, device=cpu)): #>   %1 : Float(3, strides=[1], requires_grad=0, device=cpu) = aten::relu(%0) #>   return (%1) traced_fn(torch_randn(3)) #> torch_tensor #>  0.4826 #>  0.2149 #>  0.1657 #> [ CPUFloatType{3} ] module <- nn_module(   initialize = function() {     self$linear1 <- nn_linear(10, 10)     self$linear2 <- nn_linear(10, 1)   },   forward = function(x) {     x %>%       self$linear1() %>%       nnf_relu() %>%       self$linear2()   } ) traced_module <- jit_trace(module(), torch_randn(10, 10)) traced_module(torch_randn(3, 10)) #> torch_tensor #> -0.1682 #> -0.2424 #>  0.0244 #> [ CPUFloatType{3,1} ][ grad_fn = <AddmmBackward0> ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"limitations-of-tracing","dir":"Articles","previous_headings":"Creating TorchScript programs > Tracing","what":"Limitations of tracing","title":"TorchScript","text":"Tracing record control flow like -statements loops. control flow constant across module, fine often inlines control flow decisions. sometimes control flow actually part model . instance, recurrent network loop (possibly dynamic) length input sequence. example: returned ScriptModule, operations different behaviors training eval modes always behave mode tracing, matter mode ScriptModule . example: Tracing proegrams can take tensors lists tensors input return tensors lists tensors. example:","code":"# fn does does an operation for each dimension of a tensor fn <- function(x) {   x %>%      torch_unbind(dim = 1) %>%     lapply(function(x) x$sum()) %>%     torch_stack(dim = 1) } # we trace using as an example a tensor with size (10, 5, 5) traced_fn <- jit_trace(fn, torch_randn(10, 5, 5)) # applying it with a tensor with different size returns an error. traced_fn(torch_randn(11, 5, 5)) #> Error in cpp_call_traced_fn(ptr, inputs): The following operation failed in the TorchScript interpreter. #> Traceback of TorchScript (most recent call last): #> RuntimeError: Expected 10 elements in a list but found 11 traced_dropout <- jit_trace(nn_dropout(), torch_ones(5,5)) traced_dropout(torch_ones(3,3)) #> torch_tensor #>  0  0  0 #>  0  0  2 #>  0  0  2 #> [ CPUFloatType{3,3} ] traced_dropout$eval() #> [1] FALSE # even after setting to eval mode, dropout is applied traced_dropout(torch_ones(3,3)) #> torch_tensor #>  1  1  1 #>  1  1  1 #>  1  1  1 #> [ CPUFloatType{3,3} ] fn <- function(x, y) {   x + y } jit_trace(fn, torch_tensor(1), 1) #> Error in cpp_trace_function(tr_fn, list(...), .compilation_unit, strict, : Only tensors or (possibly nested) dict or tuples of tensors can be inputs to traced functions. Got float #> Exception raised from addInput at /Users/runner/work/libtorch-mac-m1/libtorch-mac-m1/pytorch/torch/csrc/jit/frontend/tracer.cpp:422 (most recent call first): #> frame #0: std::__1::shared_ptr<c10::(anonymous namespace)::PyTorchStyleBacktrace> std::__1::make_shared[abi:ue170006]<c10::(anonymous namespace)::PyTorchStyleBacktrace, c10::SourceLocation&, void>(c10::SourceLocation&) + 121 (0x111416639 in libc10.dylib) #> frame #1: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 54 (0x111416776 in libc10.dylib) #> frame #2: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 149 (0x111413035 in libc10.dylib) #> frame #3: torch::jit::tracer::addInput(std::__1::shared_ptr<torch::jit::tracer::TracingState> const&, c10::IValue const&, c10::Type::SingletonOrSharedTypePtr<c10::Type> const&, torch::jit::Value*) + 6225 (0x128a06cf1 in libtorch_cpu.dylib) #> frame #4: torch::jit::tracer::addInput(std::__1::shared_ptr<torch::jit::tracer::TracingState> const&, c10::IValue const&, c10::Type::SingletonOrSharedTypePtr<c10::Type> const&, torch::jit::Value*) + 4799 (0x128a0675f in libtorch_cpu.dylib) #> frame #5: torch::jit::tracer::trace(std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue>>, std::__1::function<std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue>> (std::__1::vector<c10::IValue, std::__1::allocator<c10::IValue>>)> const&, std::__1::function<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> (at::Tensor const&)>, bool, bool, torch::jit::Module*, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>> const&) + 666 (0x128a03eaa in libtorch_cpu.dylib) #> frame #6: _lantern_trace_fn + 408 (0x11608de28 in liblantern.dylib) #> frame #7: cpp_trace_function(Rcpp::Function_Impl<Rcpp::PreserveStorage>, XPtrTorchStack, XPtrTorchCompilationUnit, XPtrTorchstring, bool, XPtrTorchScriptModule, bool, bool) + 601 (0x1142e7959 in torchpkg.so) #> frame #8: _torch_cpp_trace_function + 719 (0x11410468f in torchpkg.so) #> frame #9: R_doDotCall + 13245 (0x10e8074bd in libR.dylib) #> frame #10: bcEval_loop + 146595 (0x10e86f123 in libR.dylib) #> frame #11: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #12: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #13: R_execClosure + 761 (0x10e83f039 in libR.dylib) #> frame #14: applyClosure_core + 128 (0x10e83e140 in libR.dylib) #> frame #15: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib) #> frame #16: do_eval + 1253 (0x10e843b65 in libR.dylib) #> frame #17: bcEval_loop + 44444 (0x10e85621c in libR.dylib) #> frame #18: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #19: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #20: forcePromise + 230 (0x10e83d026 in libR.dylib) #> frame #21: Rf_eval + 634 (0x10e83c57a in libR.dylib) #> frame #22: do_withVisible + 57 (0x10e843ef9 in libR.dylib) #> frame #23: do_internal + 362 (0x10e8bbb6a in libR.dylib) #> frame #24: bcEval_loop + 45071 (0x10e85648f in libR.dylib) #> frame #25: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #26: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #27: forcePromise + 230 (0x10e83d026 in libR.dylib) #> frame #28: Rf_eval + 634 (0x10e83c57a in libR.dylib) #> frame #29: forcePromise + 230 (0x10e83d026 in libR.dylib) #> frame #30: bcEval_loop + 19464 (0x10e850088 in libR.dylib) #> frame #31: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #32: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #33: R_execClosure + 761 (0x10e83f039 in libR.dylib) #> frame #34: applyClosure_core + 128 (0x10e83e140 in libR.dylib) #> frame #35: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib) #> frame #36: do_eval + 1253 (0x10e843b65 in libR.dylib) #> frame #37: bcEval_loop + 44444 (0x10e85621c in libR.dylib) #> frame #38: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #39: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #40: R_execClosure + 761 (0x10e83f039 in libR.dylib) #> frame #41: applyClosure_core + 128 (0x10e83e140 in libR.dylib) #> frame #42: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib) #> frame #43: do_begin + 429 (0x10e841a2d in libR.dylib) #> frame #44: Rf_eval + 990 (0x10e83c6de in libR.dylib) #> frame #45: R_execClosure + 761 (0x10e83f039 in libR.dylib) #> frame #46: applyClosure_core + 128 (0x10e83e140 in libR.dylib) #> frame #47: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib) #> frame #48: do_docall + 615 (0x10e7cd2a7 in libR.dylib) #> frame #49: bcEval_loop + 44444 (0x10e85621c in libR.dylib) #> frame #50: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #51: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #52: R_execClosure + 761 (0x10e83f039 in libR.dylib) #> frame #53: applyClosure_core + 128 (0x10e83e140 in libR.dylib) #> frame #54: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib) #> frame #55: do_docall + 615 (0x10e7cd2a7 in libR.dylib) #> frame #56: bcEval_loop + 44444 (0x10e85621c in libR.dylib) #> frame #57: bcEval + 628 (0x10e83cdf4 in libR.dylib) #> frame #58: Rf_eval + 506 (0x10e83c4fa in libR.dylib) #> frame #59: R_execClosure + 761 (0x10e83f039 in libR.dylib) #> frame #60: applyClosure_core + 128 (0x10e83e140 in libR.dylib) #> frame #61: Rf_eval + 1189 (0x10e83c7a5 in libR.dylib) #> frame #62: forcePromise + 230 (0x10e83d026 in libR.dylib) #> :"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"compiling-torchscript","dir":"Articles","previous_headings":"Creating TorchScript programs","what":"Compiling TorchScript","title":"TorchScript","text":"’s also possible create TorchScript programs compiling TorchScript code. TorchScript code looks lot like standard python code. example:","code":"tr <- jit_compile(\" def fn (x: Tensor):   return torch.relu(x)  \") tr$fn(torch_tensor(c(-1, 0, 1))) #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"serializing-and-loading","dir":"Articles","previous_headings":"","what":"Serializing and loading","title":"TorchScript","text":"TorchScript programs can serialized using jit_save function loaded back disk jit_load. example: Loaded programs can executed usual: Note can load TorchScript programs created libraries different torch R. Eg, TorchScript program can created PyTorch torch.jit.trace torch.jit.script, run R. R objects automatically converted TorchScript counterpart following Types table document. However, sometimes ’s necessary make type annotations jit_tuple() jit_scalar() disambiguate conversion.","code":"fn <- function(x) {   torch_relu(x) } tr_fn <- jit_trace(fn, torch_tensor(1)) jit_save(tr_fn, \"path.pt\") loaded <- jit_load(\"path.pt\") loaded(torch_tensor(c(-1, 0, 1))) #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/articles/torchscript.html","id":"types","dir":"Articles","previous_headings":"","what":"Types","title":"TorchScript","text":"following table lists TorchScript types convert back R.","code":""},{"path":"https://torch.mlverse.org/docs/articles/using-autograd.html","id":"automatic-differentiation-with-autograd","dir":"Articles","previous_headings":"","what":"Automatic differentiation with autograd","title":"Using autograd","text":"Torch uses module called autograd record operations performed tensors, store done obtain respective gradients. actions stored functions, functions applied order gradient output (normally, loss) respect tensors calculated: starting output node propagating gradients back network. form reverse mode automatic differentiation. users, can see bit implementation. prerequisite “recording” happen, tensors created requires_grad = TRUE. E.g. clear, tensor respect gradients calculated – normally, tensor representing weight bias, input data 1. now perform operation tensor, assigning result y find y now non-empty grad_fn tells torch compute gradient y respect x: Actual computation gradients triggered calling backward() output tensor. executed, x now non-empty field grad stores gradient y respect x: longer chain computations, can peek torch builds graph backward operations. slightly complex example. call retain_grad() y z just demonstration purposes; default, intermediate gradients – course computed – aren’t stored, order save memory. Starting $grad_fn, can follow graph back leaf nodes: calling $backward(), tensors graph respective gradients created. Without calls retain_grad , z$grad y$grad empty: Thus acquainted autograd, ’re ready modify example.","code":"x <- torch_ones(2,2, requires_grad = TRUE) y <- x$mean() y$grad_fn #> MeanBackward0 y$backward() x$grad #> torch_tensor #>  0.2500  0.2500 #>  0.2500  0.2500 #> [ CPUFloatType{2,2} ] x1 <- torch_ones(2,2, requires_grad = TRUE) x2 <- torch_tensor(1.1, requires_grad = TRUE) y <- x1 * (x2 + 2) y$retain_grad() z <- y$pow(2) * 3 z$retain_grad() out <- z$mean() # how to compute the gradient for mean, the last operation executed out$grad_fn #> MeanBackward0 # how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3 out$grad_fn$next_functions #> [[1]] #> MulBackward1 # how to compute the gradient for pow in z = y.pow(2) * 3 out$grad_fn$next_functions[[1]]$next_functions #> [[1]] #> PowBackward0 # how to compute the gradient for the multiplication in y = x * (x + 2) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions #> [[1]] #> MulBackward0 # how to compute the gradient for the two branches of y = x * (x + 2), # where the left branch is a leaf node (AccumulateGrad for x1) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions #> [[1]] #> torch::autograd::AccumulateGrad #>  #> [[2]] #> AddBackward1 # here we arrive at the other leaf node (AccumulateGrad for x2) out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions #> [[1]] #> torch::autograd::AccumulateGrad out$backward() z$grad #> torch_tensor #>  0.2500  0.2500 #>  0.2500  0.2500 #> [ CPUFloatType{2,2} ] y$grad #> torch_tensor #>  4.6500  4.6500 #>  4.6500  4.6500 #> [ CPUFloatType{2,2} ] x2$grad #> torch_tensor #>  18.6000 #> [ CPUFloatType{1} ] x1$grad #> torch_tensor #>  14.4150  14.4150 #>  14.4150  14.4150 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/articles/using-autograd.html","id":"the-simple-network-now-using-autograd","dir":"Articles","previous_headings":"","what":"The simple network, now using autograd","title":"Using autograd","text":"single new line calling loss$backward(), now number lines (manual backprop) gone: still manually compute forward pass, still manually update weights. last two chapters section, ’ll see parts logic can made modular reusable, well.","code":"### generate training data ----------------------------------------------------- # input dimensionality (number of input features) d_in <- 3 # output dimensionality (number of predicted features) d_out <- 1 # number of observations in training set n <- 100 # create random data x <- torch_randn(n, d_in) y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n) y <- y$unsqueeze(dim = 1) ### initialize weights --------------------------------------------------------- # dimensionality of hidden layer d_hidden <- 32 # weights connecting input to hidden layer w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE) # weights connecting hidden to output layer w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE) # hidden layer bias b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE) # output layer bias b2 <- torch_zeros(1, d_out,requires_grad = TRUE) ### network parameters --------------------------------------------------------- learning_rate <- 1e-4 ### training loop -------------------------------------------------------------- for (t in 1:200) {      ### -------- Forward pass --------      y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)     ### -------- compute loss --------      loss <- (y_pred - y)$pow(2)$mean()     if (t %% 10 == 0) cat(t, as_array(loss), \"\\n\")     ### -------- Backpropagation --------      # compute the gradient of loss with respect to all tensors with requires_grad = True.     loss$backward()       ### -------- Update weights --------           # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation     with_no_grad({              w1$sub_(learning_rate * w1$grad)       w2$sub_(learning_rate * w2$grad)       b1$sub_(learning_rate * b1$grad)       b2$sub_(learning_rate * b2$grad)              # Zero the gradients after every pass, because they'd accumulate otherwise       w1$grad$zero_()       w2$grad$zero_()       b1$grad$zero_()       b2$grad$zero_()          })      } #> 10 28.26874  #> 20 26.89888  #> 30 25.61373  #> 40 24.40569  #> 50 23.26689  #> 60 22.20304  #> 70 21.20444  #> 80 20.26246  #> 90 19.37426  #> 100 18.53972  #> 110 17.75325  #> 120 17.01111  #> 130 16.3092  #> 140 15.64703  #> 150 15.02278  #> 160 14.43382  #> 170 13.87754  #> 180 13.35267  #> 190 12.85716  #> 200 12.3887"},{"path":"https://torch.mlverse.org/docs/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Daniel Falbel. Author, maintainer, copyright holder. Javier Luraschi. Author. Dmitriy Selivanov. Contributor. Athos Damiani. Contributor. Christophe Regouby. Contributor. Krzysztof Joachimiak. Contributor. Hamada S. Badr. Contributor. Sebastian Fischer. Contributor. Maximilian Pichler. Contributor. RStudio. Copyright holder.","code":""},{"path":"https://torch.mlverse.org/docs/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Falbel D, Luraschi J (2025). torch: Tensors Neural Networks 'GPU' Acceleration. R package version 0.14.0, https://github.com/mlverse/torch, https://torch.mlverse.org/docs.","code":"@Manual{,   title = {torch: Tensors and Neural Networks with 'GPU' Acceleration},   author = {Daniel Falbel and Javier Luraschi},   year = {2025},   note = {R package version 0.14.0, https://github.com/mlverse/torch},   url = {https://torch.mlverse.org/docs}, }"},{"path":[]},{"path":"https://torch.mlverse.org/docs/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tensors and Neural Networks with GPU Acceleration","text":"torch can installed CRAN : can also install development version : first package load additional software installed. See also full installation guide .","code":"install.packages(\"torch\") remotes::install_github(\"mlverse/torch\")"},{"path":"https://torch.mlverse.org/docs/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Tensors and Neural Networks with GPU Acceleration","text":"can create torch tensors R objects torch_tensor function convert back R objects as_array.","code":"library(torch) x <- array(runif(8), dim = c(2, 2, 2)) y <- torch_tensor(x, dtype = torch_float64()) y #> torch_tensor #> (1,.,.) =  #>   0.6192  0.5800 #>   0.2488  0.3681 #>  #> (2,.,.) =  #>   0.0042  0.9206 #>   0.4388  0.5664 #> [ CPUDoubleType{2,2,2} ] identical(x, as_array(y)) #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/index.html","id":"simple-autograd-example","dir":"","previous_headings":"Examples","what":"Simple Autograd Example","title":"Tensors and Neural Networks with GPU Acceleration","text":"following snippet let torch, using autograd feature, calculate derivatives:","code":"x <- torch_tensor(1, requires_grad = TRUE) w <- torch_tensor(2, requires_grad = TRUE) b <- torch_tensor(3, requires_grad = TRUE) y <- w * x + b y$backward() x$grad #> torch_tensor #>  2 #> [ CPUFloatType{1} ] w$grad #> torch_tensor #>  1 #> [ CPUFloatType{1} ] b$grad #> torch_tensor #>  1 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Tensors and Neural Networks with GPU Acceleration","text":"matter current skills ’s possible contribute torch development. See contributing guide information.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":null,"dir":"Reference","previous_headings":"","what":"Class representing the context. — AutogradContext","title":"Class representing the context. — AutogradContext","text":"Class representing context. Class representing context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Class representing the context. — AutogradContext","text":"ptr (Dev related) pointer context c++ object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Class representing the context. — AutogradContext","text":"needs_input_grad boolean listing arguments forward whether require_grad. saved_variables list objects saved backward via save_for_backward.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Class representing the context. — AutogradContext","text":"AutogradContext$new() AutogradContext$save_for_backward() AutogradContext$mark_non_differentiable() AutogradContext$mark_dirty() AutogradContext$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Class representing the context. — AutogradContext","text":"(Dev related) Initializes context. user related.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$new(   ptr,   env,   argument_names = NULL,   argument_needs_grad = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"ptr pointer c++ object env environment encloses forward backward argument_names names forward arguments argument_needs_grad whether argument forward needs grad.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-save-for-backward-","dir":"Reference","previous_headings":"","what":"Method save_for_backward()","title":"Class representing the context. — AutogradContext","text":"Saves given objects future call backward(). called , inside forward() method. Later, saved objects can accessed saved_variables attribute. returning user, check made ensure weren’t used -place operation modified content. Arguments can also kind R object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$save_for_backward(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... kind R object saved backward pass. common pass named arguments.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-mark-non-differentiable-","dir":"Reference","previous_headings":"","what":"Method mark_non_differentiable()","title":"Class representing the context. — AutogradContext","text":"Marks outputs non-differentiable. called , inside forward() method, arguments outputs. mark outputs requiring gradients, increasing efficiency backward computation. still need accept gradient output backward(), ’s always going zero tensor shape shape corresponding output. used e.g. indices returned max Function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$mark_non_differentiable(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... non-differentiable outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-mark-dirty-","dir":"Reference","previous_headings":"","what":"Method mark_dirty()","title":"Class representing the context. — AutogradContext","text":"Marks given tensors modified -place operation. called , inside forward() method, arguments inputs. Every tensor ’s modified -place call forward() given function, ensure correctness checks. doesn’t matter whether function called modification.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$mark_dirty(...)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"... tensors modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Class representing the context. — AutogradContext","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Class representing the context. — AutogradContext","text":"","code":"AutogradContext$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/AutogradContext.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class representing the context. — AutogradContext","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":null,"dir":"Reference","previous_headings":"","what":"Abstract base class for constraints. — Constraint","title":"Abstract base class for constraints. — Constraint","text":"Abstract base class constraints. Abstract base class constraints.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Abstract base class for constraints. — Constraint","text":"constraint object represents region variable valid, e.g. within variable can optimized.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Abstract base class for constraints. — Constraint","text":"Constraint$check() Constraint$print() Constraint$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-check-","dir":"Reference","previous_headings":"","what":"Method check()","title":"Abstract base class for constraints. — Constraint","text":"Returns byte tensor sample_shape + batch_shape indicating whether event value satisfies constraint.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$check(value)"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract base class for constraints. — Constraint","text":"value event value checked.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Abstract base class for constraints. — Constraint","text":"Define print method constraints,","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$print()"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Abstract base class for constraints. — Constraint","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract base class for constraints. — Constraint","text":"","code":"Constraint$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/Constraint.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract base class for constraints. — Constraint","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic R6 class representing distributions — Distribution","title":"Generic R6 class representing distributions — Distribution","text":"Distribution abstract base class probability distributions. Note: Python, adding torch.Size objects works concatenation Try example: torch.Size((2, 1)) + torch.Size((1,))","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Generic R6 class representing distributions — Distribution","text":".validate_args whether validate arguments has_rsample whether rsample has_enumerate_support whether enumerate support","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape Returns shape parameters batched. event_shape Returns shape single sample (without batching). Returns dictionary argument names torch_Constraint objects satisfied argument distribution. Args tensors need appear dict. support Returns torch_Constraint object representing distribution's support. mean Returns mean distribution variance Returns variance distribution stddev Returns standard deviation distribution TODO: consider different message","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Generic R6 class representing distributions — Distribution","text":"Distribution$new() Distribution$expand() Distribution$sample() Distribution$rsample() Distribution$log_prob() Distribution$cdf() Distribution$icdf() Distribution$enumerate_support() Distribution$entropy() Distribution$perplexity() Distribution$.extended_shape() Distribution$.validate_sample() Distribution$print() Distribution$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Generic R6 class representing distributions — Distribution","text":"Initializes distribution class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$new(batch_shape = NULL, event_shape = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape shape parameters batched. event_shape shape single sample (without batching). validate_args whether validate arguments . Validation can time consuming might want disable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-expand-","dir":"Reference","previous_headings":"","what":"Method expand()","title":"Generic R6 class representing distributions — Distribution","text":"Returns new distribution instance (populates existing instance provided derived class) batch dimensions expanded batch_shape. method calls expand distribution’s parameters. , allocate new memory expanded distribution instance. Additionally, repeat args checking parameter broadcasting initialize, instance first created.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$expand(batch_shape, .instance = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"batch_shape desired expanded size. .instance new instance provided subclasses need override expand.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-sample-","dir":"Reference","previous_headings":"","what":"Method sample()","title":"Generic R6 class representing distributions — Distribution","text":"Generates sample_shape shaped sample sample_shape shaped batch samples distribution parameters batched.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$sample(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape shape want sample.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-rsample-","dir":"Reference","previous_headings":"","what":"Method rsample()","title":"Generic R6 class representing distributions — Distribution","text":"Generates sample_shape shaped reparameterized sample sample_shape shaped batch reparameterized samples distribution parameters batched.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$rsample(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape shape want sample.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-log-prob-","dir":"Reference","previous_headings":"","what":"Method log_prob()","title":"Generic R6 class representing distributions — Distribution","text":"Returns log probability density/mass function evaluated value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$log_prob(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-cdf-","dir":"Reference","previous_headings":"","what":"Method cdf()","title":"Generic R6 class representing distributions — Distribution","text":"Returns cumulative density/mass function evaluated value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$cdf(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-icdf-","dir":"Reference","previous_headings":"","what":"Method icdf()","title":"Generic R6 class representing distributions — Distribution","text":"Returns inverse cumulative density/mass function evaluated value. @description Returns tensor containing values supported discrete distribution. result enumerate dimension 0, shape result (cardinality,) + batch_shape + event_shape (event_shape = ()univariate distributions). Note enumerates batched tensors lock-steplist(c(0, 0), c(1, 1), ...). expand=FALSE, enumeration happens along dim 0, remaining batch dimensions singleton dimensions, list(c(0), c(1), ...)`.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$icdf(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value values evaluate density .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$enumerate_support(expand = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"expand (bool): whether expand support batch dims match distribution's batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor iterating dimension 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-entropy-","dir":"Reference","previous_headings":"","what":"Method entropy()","title":"Generic R6 class representing distributions — Distribution","text":"Returns entropy distribution, batched batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$entropy()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor shape batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-perplexity-","dir":"Reference","previous_headings":"","what":"Method perplexity()","title":"Generic R6 class representing distributions — Distribution","text":"Returns perplexity distribution, batched batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$perplexity()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Generic R6 class representing distributions — Distribution","text":"Tensor shape batch_shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-extended-shape-","dir":"Reference","previous_headings":"","what":"Method .extended_shape()","title":"Generic R6 class representing distributions — Distribution","text":"Returns size sample returned distribution, given sample_shape. Note, batch event shapes distribution instance fixed time construction. empty, returned shape upcast (1,).","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$.extended_shape(sample_shape = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-8","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"sample_shape (torch_Size): size sample drawn.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-validate-sample-","dir":"Reference","previous_headings":"","what":"Method .validate_sample()","title":"Generic R6 class representing distributions — Distribution","text":"Argument validation distribution methods log_prob, cdf icdf. rightmost dimensions value scored via methods must agree distribution's batch event shapes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-11","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$.validate_sample(value)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-9","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"value (Tensor): tensor whose log probability computed log_prob method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Generic R6 class representing distributions — Distribution","text":"Prints distribution instance.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-12","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$print()"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Generic R6 class representing distributions — Distribution","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"usage-13","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic R6 class representing distributions — Distribution","text":"","code":"Distribution$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/Distribution.html","id":"arguments-10","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic R6 class representing distributions — Distribution","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":null,"dir":"Reference","previous_headings":"","what":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Abstract base class wrapping LibTorch C++ optimizers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"torch::torch_optimizer -> OptimizerIgnite","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"OptimizerIgnite$new() OptimizerIgnite$state_dict() OptimizerIgnite$load_state_dict() OptimizerIgnite$step() OptimizerIgnite$zero_grad() OptimizerIgnite$add_param_group() OptimizerIgnite$clone()","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Initializes optimizer specified parameters defaults.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$new(params, defaults)"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"params (list()) Either list tensors list parameter groups, containing params optimizer well optimizer options learning rate, weight decay, etc. defaults (list()) list default optimizer options.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-state-dict-","dir":"Reference","previous_headings":"","what":"Method state_dict()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Returns state dictionary containing current state optimizer. returned list() contains two lists: param_groups: parameter groups optimizer (lr, ...) well parameters applied (params, integer indices) state: states optimizer. names indices parameters belong, converted character.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$state_dict()"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"(list())","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-load-state-dict-","dir":"Reference","previous_headings":"","what":"Method load_state_dict()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Loads state dictionary optimizer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$load_state_dict(state_dict)"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"state_dict (list()) state dictionary load optimizer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-step-","dir":"Reference","previous_headings":"","what":"Method step()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Performs single optimization step.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$step(closure = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"closure (function()) closure conducts forward pass returns loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"(numeric()) loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-zero-grad-","dir":"Reference","previous_headings":"","what":"Method zero_grad()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Zeros gradients parameters.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$zero_grad()"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-add-param-group-","dir":"Reference","previous_headings":"","what":"Method add_param_group()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"Adds new parameter group optimizer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$add_param_group(param_group)"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"param_group (list()) parameter group add optimizer. contain params optimize well optimizer options. options specified, defaults used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"","code":"OptimizerIgnite$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/OptimizerIgnite.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract Base Class for LibTorch Optimizers — OptimizerIgnite","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts to array — as_array","title":"Converts to array — as_array","text":"Converts array","code":""},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts to array — as_array","text":"","code":"as_array(x)"},{"path":"https://torch.mlverse.org/docs/reference/as_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts to array — as_array","text":"x object converted array","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"graph differentiated using chain rule. tensors non-scalar (.e. data one element) require gradient, Jacobian-vector product computed, case function additionally requires specifying grad_tensors. sequence matching length, contains “vector” Jacobian-vector product, usually gradient differentiated function w.r.t. corresponding tensors (None acceptable value tensors don’t need gradient tensors).","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"","code":"autograd_backward(   tensors,   grad_tensors = NULL,   retain_graph = create_graph,   create_graph = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"tensors (list Tensor) – Tensors derivative computed. grad_tensors (list (Tensor NULL)) – “vector” Jacobian-vector product, usually gradients w.r.t. element corresponding tensors. NULLvalues can specified scalar Tensors ones don’t require grad. aNULL` value acceptable grad_tensors, argument optional. retain_graph (bool, optional) – FALSE, graph used compute grad freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional) – TRUE, graph derivative constructed, allowing compute higher order derivative products. Defaults FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"function accumulates gradients leaves - might need zero calling .","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_backward.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(1, requires_grad = TRUE) y <- 2 * x  a <- torch_tensor(1, requires_grad = TRUE) b <- 3 * a  autograd_backward(list(y, b)) }"},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Records operation history and defines formulas for differentiating ops. — autograd_function","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"Every operation performed Tensor's creates new function object, performs computation, records happened. history retained form DAG functions, edges denoting data dependencies (input <- output). , backward called, graph processed topological ordering, calling backward() methods Function object, passing returned gradients next Function's.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"","code":"autograd_function(forward, backward)"},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"forward Performs operation. must accept context ctx first argument, followed number arguments (tensors types). context can used store tensors can retrieved backward pass. See AutogradContext information context methods. backward Defines formula differentiating operation. must accept context ctx first argument, followed many outputs ad forward() returned (list()). names arguments matter passed order returned forward(). function return named list, argument gradient w.r.t given output, element returned list gradient w.r.t. corresponding input. context can used retrieve tensors saved forward pass. also attribute ctx$needs_input_grad named list booleans representing whether input needs gradient. E.g., backward() ctx$needs_input_grad$input = TRUE input argument forward() needs gradient computated w.r.t. output. See AutogradContext information context methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Records operation history and defines formulas for differentiating ops. — autograd_function","text":"","code":"if (torch_is_installed()) {  exp2 <- autograd_function(   forward = function(ctx, i) {     result <- i$exp()     ctx$save_for_backward(result = result)     result   },   backward = function(ctx, grad_output) {     list(i = grad_output * ctx$saved_variable$result)   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"grad_outputs list length matching output containing “vector” Jacobian-vector product, usually pre-computed gradients w.r.t. outputs. output doesn’t require_grad, gradient can None).","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"","code":"autograd_grad(   outputs,   inputs,   grad_outputs = NULL,   retain_graph = create_graph,   create_graph = FALSE,   allow_unused = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"outputs (sequence Tensor) – outputs differentiated function. inputs (sequence Tensor) – Inputs w.r.t. gradient returned (accumulated .grad). grad_outputs (sequence Tensor) – “vector” Jacobian-vector product. Usually gradients w.r.t. output. None values can specified scalar Tensors ones don’t require grad. None value acceptable grad_tensors, argument optional. Default: None. retain_graph (bool, optional) – FALSE, graph used compute grad freed. Note nearly cases setting option TRUE needed often can worked around much efficient way. Defaults value create_graph. create_graph (bool, optional) – TRUE, graph derivative constructed, allowing compute higher order derivative products. Default: FALSE`. allow_unused (bool, optional) – FALSE, specifying inputs used computing outputs (therefore grad always zero) error. Defaults FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"only_inputs TRUE, function return list gradients w.r.t specified inputs. ’s FALSE, gradient w.r.t. remaining leaves still computed, accumulated .grad attribute.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad","text":"","code":"if (torch_is_installed()) { w <- torch_tensor(0.5, requires_grad = TRUE) b <- torch_tensor(0.9, requires_grad = TRUE) x <- torch_tensor(runif(100)) y <- 2 * x + 1 loss <- (y - (w * x + b))^2 loss <- loss$mean()  o <- autograd_grad(loss, list(w, b)) o } #> [[1]] #> torch_tensor #> -0.9935 #> [ CPUFloatType{1} ] #>  #> [[2]] #> torch_tensor #> -1.6206 #> [ CPUFloatType{1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Set grad mode — autograd_set_grad_mode","title":"Set grad mode — autograd_set_grad_mode","text":"Sets disables gradient history.","code":""},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set grad mode — autograd_set_grad_mode","text":"","code":"autograd_set_grad_mode(enabled)"},{"path":"https://torch.mlverse.org/docs/reference/autograd_set_grad_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set grad mode — autograd_set_grad_mode","text":"enabled bool wether enable disable gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"CuDNN is available — backends_cudnn_is_available","title":"CuDNN is available — backends_cudnn_is_available","text":"CuDNN available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CuDNN is available — backends_cudnn_is_available","text":"","code":"backends_cudnn_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_version.html","id":null,"dir":"Reference","previous_headings":"","what":"CuDNN version — backends_cudnn_version","title":"CuDNN version — backends_cudnn_version","text":"CuDNN version","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_cudnn_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CuDNN version — backends_cudnn_version","text":"","code":"backends_cudnn_version()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MKL is available — backends_mkl_is_available","title":"MKL is available — backends_mkl_is_available","text":"MKL available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MKL is available — backends_mkl_is_available","text":"","code":"backends_mkl_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkl_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MKL is available — backends_mkl_is_available","text":"Returns whether LibTorch built MKL support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MKLDNN is available — backends_mkldnn_is_available","title":"MKLDNN is available — backends_mkldnn_is_available","text":"MKLDNN available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MKLDNN is available — backends_mkldnn_is_available","text":"","code":"backends_mkldnn_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mkldnn_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MKLDNN is available — backends_mkldnn_is_available","text":"Returns whether LibTorch built MKL-DNN support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mps_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"MPS is available — backends_mps_is_available","title":"MPS is available — backends_mps_is_available","text":"MPS available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_mps_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MPS is available — backends_mps_is_available","text":"","code":"backends_mps_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_mps_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MPS is available — backends_mps_is_available","text":"Returns whether LibTorch built MPS support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"OpenMP is available — backends_openmp_is_available","title":"OpenMP is available — backends_openmp_is_available","text":"OpenMP available","code":""},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OpenMP is available — backends_openmp_is_available","text":"","code":"backends_openmp_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/backends_openmp_is_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"OpenMP is available — backends_openmp_is_available","text":"Returns whether LibTorch built OpenMP support.","code":""},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","title":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","text":"Raises value_error: values numeric instance, torch.*Tensor instance, instance implementing torch_function TODO: add has_torch_function((v,)) See: https://github.com/pytorch/pytorch/blob/master/torch/distributions/utils.py","code":""},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","text":"","code":"broadcast_all(values)"},{"path":"https://torch.mlverse.org/docs/reference/broadcast_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: — broadcast_all","text":"values List : torch.*Tensor instances broadcasted per _broadcasting-semantics. numeric instances (scalars) upcast tensors size type first tensor passed values.  values scalars, upcasted scalar Tensors. values (list numeric, torch.*Tensor objects implementing torch_function)","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a (Potentially Unexported) Torch Function — call_torch_function","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"function allows calling function prefixed torch_, including unexported functions potentially valuable uses yet user-friendly R wrapper function. Therefore, function used extreme caution. Make sure understand function expects input. may helpful read torch source code help , well documentation corresponding function Pytorch C++ API. Generally development advanced use .","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"","code":"call_torch_function(name, ..., quiet = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"name Name function call string. start \"torch_\" ... list arguments pass function. Argument splicing !!! supported. quiet TRUE, suppress warnings valuable information dangers function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"return value calling function name arguments ...","code":""},{"path":"https://torch.mlverse.org/docs/reference/call_torch_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Call a (Potentially Unexported) Torch Function — call_torch_function","text":"","code":"if (torch_is_installed()) { ## many unexported functions do 'backward' calculations (e.g. derivatives) ## These could be used as a part of custom autograd functions for example. x <- torch_randn(10, requires_grad = TRUE) y <- torch_tanh(x) ## calculate backwards gradient using standard torch method y$backward(torch_ones_like(x)) x$grad ## we can get the same result by calling the unexported `torch_tanh_backward()` ## function. The first argument is 1 to setup the Jacobian-vector product. ## see https://pytorch.org/blog/overview-of-pytorch-autograd-engine/ for details. call_torch_function(\"torch_tanh_backward\", 1, y) all.equal(call_torch_function(\"torch_tanh_backward\", 1, y, quiet = TRUE), x$grad)  } #> Warning: Because this function allows access to unexported functions, please use with caution, and #>             only if you are sure know what you are doing. Unexported functions will expect inputs that #>             are more C++-like than R-like. For example, they will expect all indexes to be 0-based instead #>             of 1-based. In addition unexported functions may be subject to removal from the API without #>             warning. Set quiet = TRUE to silence this warning. #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone a torch module. — clone_module","title":"Clone a torch module. — clone_module","text":"Clones module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone a torch module. — clone_module","text":"","code":"clone_module(module, deep = FALSE, ..., replace_values = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone a torch module. — clone_module","text":"module (nn_module) module clone deep (logical(1)) Whether create deep clone. ... () Additional parameters, currently unused. replace_values (logical(1)) Whether replace parameters buffers cloned values.","code":""},{"path":"https://torch.mlverse.org/docs/reference/clone_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clone a torch module. — clone_module","text":"","code":"if (torch_is_installed()) { clone_module(nn_linear(1, 1), deep = TRUE) # is the same as nn_linear(1, 1)$clone(deep = TRUE) } #> An `nn_module` containing 2 parameters. #>  #> ── Parameters ────────────────────────────────────────────────────────────────── #> • weight: Float [1:1, 1:1] #> • bias: Float [1:1]"},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":null,"dir":"Reference","previous_headings":"","what":"Contrib sort vertices — contrib_sort_vertices","title":"Contrib sort vertices — contrib_sort_vertices","text":"Based implementation Rotated_IoU","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contrib sort vertices — contrib_sort_vertices","text":"","code":"contrib_sort_vertices(vertices, mask, num_valid)"},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contrib sort vertices — contrib_sort_vertices","text":"vertices Tensor vertices. mask tensors containing masks. num_valid integer tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Contrib sort vertices — contrib_sort_vertices","text":"tensors CUDA device function can used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Contrib sort vertices — contrib_sort_vertices","text":"function make part official torch API.","code":""},{"path":"https://torch.mlverse.org/docs/reference/contrib_sort_vertices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contrib sort vertices — contrib_sort_vertices","text":"","code":"if (torch_is_installed()) { if (cuda_is_available()) {   v <- torch_randn(8, 1024, 24, 2)$cuda()   mean <- torch_mean(v, dim = 2, keepdim = TRUE)   v <- v - mean   m <- (torch_rand(8, 1024, 24) > 0.8)$cuda()   nv <- torch_sum(m$to(dtype = torch_int()), dim = -1)$to(dtype = torch_int())$cuda()   result <- contrib_sort_vertices(v, m, nv) } }"},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a gradient scaler — cuda_amp_grad_scaler","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"gradient scaler instance used perform dynamic gradient scaling avoid gradient underflow training mixed precision.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"","code":"cuda_amp_grad_scaler(   init_scale = 2^16,   growth_factor = 2,   backoff_factor = 0.5,   growth_interval = 2000,   enabled = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"init_scale numeric value indicating initial scale factor. growth_factor numeric value indicating growth factor. backoff_factor numeric value indicating backoff factor. growth_interval numeric value indicating growth interval. enabled logical value indicating whether gradient scaler enabled.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_amp_grad_scaler.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a gradient scaler — cuda_amp_grad_scaler","text":"gradient scaler object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_current_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the index of a currently selected device. — cuda_current_device","title":"Returns the index of a currently selected device. — cuda_current_device","text":"Returns index currently selected device.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_current_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the index of a currently selected device. — cuda_current_device","text":"","code":"cuda_current_device()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_device_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the number of GPUs available. — cuda_device_count","title":"Returns the number of GPUs available. — cuda_device_count","text":"Returns number GPUs available.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_device_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the number of GPUs available. — cuda_device_count","text":"","code":"cuda_device_count()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_empty_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty cache — cuda_empty_cache","title":"Empty cache — cuda_empty_cache","text":"Releases unoccupied cached memory currently held caching allocator can used GPU application visible nvidia-smi.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_empty_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty cache — cuda_empty_cache","text":"","code":"cuda_empty_cache()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_empty_cache.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Empty cache — cuda_empty_cache","text":"cuda_empty_cache() doesn’t increase amount GPU memory available torch. However, may help reduce fragmentation GPU memory certain cases. See Memory management article details GPU memory management.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"Returns major minor CUDA capability device","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"","code":"cuda_get_device_capability(device = cuda_current_device())"},{"path":"https://torch.mlverse.org/docs/reference/cuda_get_device_capability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns the major and minor CUDA capability of device — cuda_get_device_capability","text":"device Integer value CUDA device return capabilities .","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_is_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","title":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","text":"Returns bool indicating CUDA currently available.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_is_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a bool indicating if CUDA is currently available. — cuda_is_available","text":"","code":"cuda_is_available()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"return value function dictionary statistics, non-negative integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"","code":"cuda_memory_stats(device = cuda_current_device())  cuda_memory_summary(device = cuda_current_device())"},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"device Integer value CUDA device return capabilities .","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"core-statistics","dir":"Reference","previous_headings":"","what":"Core statistics","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"\"allocated.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number allocation requests received memory allocator. \"allocated_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount allocated memory. \"segment.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number reserved segments cudaMalloc(). \"reserved_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount reserved memory. \"active.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number active memory blocks. \"active_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount active memory. \"inactive_split.{,large_pool,small_pool}.{current,peak,allocated,freed}\": number inactive, non-releasable memory blocks. \"inactive_split_bytes.{,large_pool,small_pool}.{current,peak,allocated,freed}\": amount inactive, non-releasable memory. core statistics, values broken follows. Pool type: : combined statistics across memory pools. large_pool: statistics large allocation pool (October 2019, size >= 1MB allocations). small_pool: statistics small allocation pool (October 2019, size < 1MB allocations). Metric type: current: current value metric. peak: maximum value metric. allocated: historical total increase metric. freed: historical total decrease metric.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_memory_stats.html","id":"additional-metrics","dir":"Reference","previous_headings":"","what":"Additional metrics","title":"Returns a dictionary of CUDA memory allocator statistics for a given device. — cuda_memory_stats","text":"\"num_alloc_retries\": number failed cudaMalloc calls result cache flush retry. \"num_ooms\": number --memory errors thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_runtime_version.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns the CUDA runtime version — cuda_runtime_version","title":"Returns the CUDA runtime version — cuda_runtime_version","text":"Returns CUDA runtime version","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_runtime_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns the CUDA runtime version — cuda_runtime_version","text":"","code":"cuda_runtime_version()"},{"path":"https://torch.mlverse.org/docs/reference/cuda_synchronize.html","id":null,"dir":"Reference","previous_headings":"","what":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","title":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","text":"Waits kernels streams CUDA device complete.","code":""},{"path":"https://torch.mlverse.org/docs/reference/cuda_synchronize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","text":"","code":"cuda_synchronize(device = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/cuda_synchronize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Waits for all kernels in all streams on a CUDA device to complete. — cuda_synchronize","text":"device device synchronize. uses current device given cuda_current_device() device specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"Data loader. Combines dataset sampler, provides single- multi-process iterators dataset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"","code":"dataloader(   dataset,   batch_size = 1,   shuffle = FALSE,   sampler = NULL,   batch_sampler = NULL,   num_workers = 0,   collate_fn = NULL,   pin_memory = FALSE,   drop_last = FALSE,   timeout = -1,   worker_init_fn = NULL,   worker_globals = NULL,   worker_packages = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"dataset (Dataset): dataset load data. batch_size (int, optional): many samples per batch load (default: 1). shuffle (bool, optional): set TRUE data reshuffled every epoch (default: FALSE). sampler (Sampler, optional): defines strategy draw samples dataset. specified, shuffle must False. Custom samplers can created sampler(). batch_sampler (Sampler, optional): like sampler, returns batch indices time. Mutually exclusive batch_size, shuffle, sampler, drop_last. Custom samplers can created sampler(). num_workers (int, optional): many subprocesses use data loading. 0 means data loaded main process. (default: 0) collate_fn (callable, optional): merges list samples form mini-batch. pin_memory (bool, optional): TRUE, data loader copy tensors CUDA pinned memory returning .  data elements custom type, collate_fn returns batch custom type see example . drop_last (bool, optional): set TRUE drop last incomplete batch, dataset size divisible batch size. FALSE size dataset divisible batch size, last batch smaller. (default: FALSE) timeout (numeric, optional): positive, timeout value collecting batch workers. -1 means timeout. (default: -1) worker_init_fn (callable, optional): NULL, called worker subprocess worker id (int [1, num_workers]) input, seeding data loading. (default: NULL) worker_globals (list character vector, optional) used num_workers > 0. character vector, objects names copied global environment workers. named list, list copied attached worker global environment. Notice objects copied worker initialization. worker_packages (character vector, optional) used num_workers > 0 optional character vector naming packages loaded worker.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader.html","id":"parallel-data-loading","dir":"Reference","previous_headings":"","what":"Parallel data loading","title":"Data loader. Combines a dataset and a sampler, and provides single- or multi-process iterators over the dataset. — dataloader","text":"using num_workers > 0 data loading happen parallel worker. Note batches taken parallel observations. worker initialization  process happens following order: num_workers R sessions initialized. worker perform following actions: torch library loaded. random seed set using set.seed() using torch_manual_seed. packages passed worker_packages argument loaded. objects passed trough worker_globals parameters copied global environment. worker_init function ran id argument. dataset fetcher copied worker.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an iterator from a DataLoader — dataloader_make_iter","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"Creates iterator DataLoader","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"","code":"dataloader_make_iter(dataloader)"},{"path":"https://torch.mlverse.org/docs/reference/dataloader_make_iter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an iterator from a DataLoader — dataloader_make_iter","text":"dataloader dataloader object.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the next element of a dataloader iterator — dataloader_next","title":"Get the next element of a dataloader iterator — dataloader_next","text":"Get next element dataloader iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the next element of a dataloader iterator — dataloader_next","text":"","code":"dataloader_next(iter, completed = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/dataloader_next.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the next element of a dataloader iterator — dataloader_next","text":"iter DataLoader iter created dataloader_make_iter. completed returned value iterator exhausted.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create an function that generates R6 instances of class dataset — dataset","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"datasets represent map keys data samples subclass class. subclasses overwrite .getitem() method, supports fetching data sample given key. Subclasses also optionally overwrite .length(), expected return size dataset (e.g. number samples) used many sampler implementations default options dataloader().","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"","code":"dataset(   name = NULL,   inherit = Dataset,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"name name dataset. also used class . inherit can optionally inherit dataset creating new dataset. ... public methods dataset class private passed R6::R6Class(). active passed R6::R6Class(). parent_env environment use parent newly-created objects.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"output function f class dataset_generator. Calling f() creates new instance R6 class dataset. R6 class stored enclosing environment f can also accessed fs attribute Dataset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"dataloader()  default constructs index sampler yields integral indices.  make work map-style dataset non-integral indices/keys, custom sampler must provided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset.html","id":"get-a-batch-of-observations","dir":"Reference","previous_headings":"","what":"Get a batch of observations","title":"Helper function to create an function that generates R6 instances of class dataset — dataset","text":"default datasets iterated returning observation/item individually. Often possible optimized implementation take batch observations (eg, subsetting tensor multiple indexes faster subsetting index), case can implement .getbatch method used instead .getitem getting batch observations within dataloader. .getbatch must work batches size larger equal 1 care must taken drop batch dimension queried length 1 batch index - instance using drop=FALSE. .getitem() expected include batch dimension added datalaoder. see vignette(\"loading-data\").","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Dataset Subset — dataset_subset","title":"Dataset Subset — dataset_subset","text":"Subset dataset specified indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dataset Subset — dataset_subset","text":"","code":"dataset_subset(dataset, indices)"},{"path":"https://torch.mlverse.org/docs/reference/dataset_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dataset Subset — dataset_subset","text":"dataset (Dataset): whole Dataset indices (sequence): Indices whole set selected subset","code":""},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Gets and sets the default floating point dtype. — torch_set_default_dtype","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"Gets sets default floating point dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"","code":"torch_set_default_dtype(d)  torch_get_default_dtype()"},{"path":"https://torch.mlverse.org/docs/reference/default_dtype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gets and sets the default floating point dtype. — torch_set_default_dtype","text":"d default floating point dtype set. Initially set torch_float().","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"Creates Bernoulli distribution parameterized probs logits (). Samples binary (0 1). take value 1 probability p 0 probability 1 - p.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"","code":"distr_bernoulli(probs = NULL, logits = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"probs (numeric torch_tensor): probability sampling 1 logits (numeric torch_tensor): log-odds sampling 1 validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Bernoulli distribution parameterized by probs or logits (but not both). Samples are binary (0 or 1). They take the value 1 with probability p and 0 with probability 1 - p. — distr_bernoulli","text":"","code":"if (torch_is_installed()) { m <- distr_bernoulli(0.3) m$sample() # 30% chance 1; 70% chance 0 } #> torch_tensor #>  0 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"Creates categorical distribution parameterized either probs logits ().","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"","code":"distr_categorical(probs = NULL, logits = NULL, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"probs (Tensor): event probabilities logits (Tensor): event log probabilities (unnormalized) validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"equivalent distribution torch_multinomial() samples . Samples integers \\(\\{0, \\ldots, K-1\\}\\) K probs$size(-1). probs 1-dimensional length-K, element relative probability sampling class index. probs N-dimensional, first N-1 dimensions treated batch relative probability vectors. probs argument must non-negative, finite non-zero sum, normalized sum 1 along last dimension. attr:probs return normalized value. logits argument interpreted unnormalized log probabilities can therefore real number. likewise normalized resulting probabilities sum 1 along last dimension. attr:logits return normalized value. See also: torch_multinomial()","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_categorical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a categorical distribution parameterized by either probs or logits (but not both). — distr_categorical","text":"","code":"if (torch_is_installed()) { m <- distr_categorical(torch_tensor(c(0.25, 0.25, 0.25, 0.25))) m$sample() # equal probability of 1,2,3,4 } #> torch_tensor #> 2 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"Creates Chi2 distribution parameterized shape parameter df. exactly equivalent distr_gamma(alpha=0.5*df, beta=0.5)","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"","code":"distr_chi2(df, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"df (float torch_tensor): shape parameter distribution validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_chi2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to distr_gamma(alpha=0.5*df, beta=0.5) — distr_chi2","text":"","code":"if (torch_is_installed()) { m <- distr_chi2(torch_tensor(1.0)) m$sample() # Chi2 distributed with shape df=1 torch_tensor(0.1046) } #> torch_tensor #>  0.1046 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"Creates Gamma distribution parameterized shape concentration rate.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"","code":"distr_gamma(concentration, rate, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"concentration (float Tensor): shape parameter distribution (often referred alpha) rate (float Tensor): rate = 1 / scale distribution (often referred beta) validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_gamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Gamma distribution parameterized by shape concentration and rate. — distr_gamma","text":"","code":"if (torch_is_installed()) { m <- distr_gamma(torch_tensor(1.0), torch_tensor(1.0)) m$sample() # Gamma distributed with concentration=1 and rate=1 } #> torch_tensor #>  0.7523 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixture of components in the same family — distr_mixture_same_family","title":"Mixture of components in the same family — distr_mixture_same_family","text":"MixtureSameFamily distribution implements (batch ) mixture distribution component different parameterizations distribution type. parameterized Categorical selecting distribution\" (k component) component distribution, .e., Distribution rightmost batch shape (equal [k]) indexes (batch ) component.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixture of components in the same family — distr_mixture_same_family","text":"","code":"distr_mixture_same_family(   mixture_distribution,   component_distribution,   validate_args = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixture of components in the same family — distr_mixture_same_family","text":"mixture_distribution torch_distributions.Categorical-like instance. Manages probability selecting component. number categories must match rightmost batch dimension component_distribution. Must either scalar batch_shape batch_shape matching component_distribution.batch_shape[:-1] component_distribution torch_distributions.Distribution-like instance. Right-batch dimension indexes component. validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_mixture_same_family.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixture of components in the same family — distr_mixture_same_family","text":"","code":"if (torch_is_installed()) { # Construct Gaussian Mixture Model in 1D consisting of 5 equally # weighted normal distributions mix <- distr_categorical(torch_ones(5)) comp <- distr_normal(torch_randn(5), torch_rand(5)) gmm <- distr_mixture_same_family(mix, comp) }"},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian distribution — distr_multivariate_normal","title":"Gaussian distribution — distr_multivariate_normal","text":"Creates multivariate normal (also called Gaussian) distribution parameterized mean vector covariance matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian distribution — distr_multivariate_normal","text":"","code":"distr_multivariate_normal(   loc,   covariance_matrix = NULL,   precision_matrix = NULL,   scale_tril = NULL,   validate_args = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian distribution — distr_multivariate_normal","text":"loc (Tensor): mean distribution covariance_matrix (Tensor): positive-definite covariance matrix precision_matrix (Tensor): positive-definite precision matrix scale_tril (Tensor): lower-triangular factor covariance, positive-valued diagonal validate_args Bool wether validate arguments .","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Gaussian distribution — distr_multivariate_normal","text":"multivariate normal distribution can parameterized either terms positive definite covariance matrix \\(\\mathbf{\\Sigma}\\) positive definite precision matrix \\(\\mathbf{\\Sigma}^{-1}\\) lower-triangular matrix \\(\\mathbf{L}\\) positive-valued diagonal entries, \\(\\mathbf{\\Sigma} = \\mathbf{L}\\mathbf{L}^\\top\\). triangular matrix can obtained via e.g. Cholesky decomposition covariance.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gaussian distribution — distr_multivariate_normal","text":"one covariance_matrix precision_matrix scale_tril can specified. Using scale_tril efficient: computations internally based scale_tril. covariance_matrix precision_matrix passed instead, used compute corresponding lower triangular matrices using Cholesky decomposition.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_multivariate_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gaussian distribution — distr_multivariate_normal","text":"","code":"if (torch_is_installed()) { m <- distr_multivariate_normal(torch_zeros(2), torch_eye(2)) m$sample() # normally distributed with mean=`[0,0]` and covariance_matrix=`I` } #> torch_tensor #>  0.7218 #> -0.1566 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"Creates normal (also called Gaussian) distribution parameterized loc scale.","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"","code":"distr_normal(loc, scale, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"loc (float Tensor): mean distribution (often referred mu) scale (float Tensor): standard deviation distribution (often referred sigma) validate_args Additional arguments","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"Object torch_Normal class","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a normal (also called Gaussian) distribution parameterized by loc and scale. — distr_normal","text":"","code":"if (torch_is_installed()) { m <- distr_normal(loc = 0, scale = 1) m$sample() # normally distributed with loc=0 and scale=1 } #> torch_tensor #>  0.3157 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"Samples nonnegative integers, pmf given $$ \\mbox{rate}^{k} \\frac{e^{-\\mbox{rate}}}{k!} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"","code":"distr_poisson(rate, validate_args = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"rate (numeric, torch_tensor): rate parameter validate_args whether validate arguments .","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/distr_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Poisson distribution parameterized by rate, the rate parameter. — distr_poisson","text":"","code":"if (torch_is_installed()) { m <- distr_poisson(torch_tensor(4)) m$sample() } #> torch_tensor #>  3 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Enumerate an iterator — enumerate.dataloader","title":"Enumerate an iterator — enumerate.dataloader","text":"Enumerate iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enumerate an iterator — enumerate.dataloader","text":"","code":"# S3 method for class 'dataloader' enumerate(x, max_len = 1e+06, ...)"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enumerate an iterator — enumerate.dataloader","text":"x generator enumerate. max_len maximum number iterations. ... passed specific methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":null,"dir":"Reference","previous_headings":"","what":"Enumerate an iterator — enumerate","title":"Enumerate an iterator — enumerate","text":"Enumerate iterator","code":""},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enumerate an iterator — enumerate","text":"","code":"enumerate(x, ...)"},{"path":"https://torch.mlverse.org/docs/reference/enumerate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enumerate an iterator — enumerate","text":"x generator enumerate. ... passed specific methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Torch — install_torch","title":"Install Torch — install_torch","text":"Installs Torch dependencies.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Torch — install_torch","text":"","code":"install_torch(reinstall = FALSE, ..., .inform_restart = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Torch — install_torch","text":"reinstall Re-install Torch even already installed? ... Currently unused. .inform_restart TRUE running interactive() session, installation print message inform user session must restarted torch work correctly.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Install Torch — install_torch","text":"function mainly controlled environment variables can used override defaults: TORCH_HOME: installation path. default dependencies installed within package directory. Eg given system.file(package=\"torch\"). TORCH_URL: URL, path ZIP file directory containing LibTorch version. Files installed/copied TORCH_HOME directory. LANTERN_URL: TORCH_URL Lantern library. TORCH_INSTALL_DEBUG: Setting 1, shows debug log messages installation. PRECXX11ABI: Setting 1 trigger installation Pre-cxx11 ABI installation LibTorch. can useful environments older versions GLIBC like CentOS7 older Debian/Ubuntu versions. LANTERN_BASE_URL: base URL lantern files. allows passing directory lantern binaries located. filename constructed usual. TORCH_COMMIT_SHA: torch repository commit sha used querying lantern uploads. Set 'none' avoid looking build commit use latest build branch. CUDA: try automatically detect CUDA version installed system, might want manually set . can also disable CUDA installation setting 'cpu'. TORCH_R_VERSION: R torch version. unlikely need change , can useful R package installed, want install dependencies. TORCH_INSTALL environment variable can set 0 prevent auto-installing torch TORCH_LOAD set 0 avoid loading dependencies automatically. environment variables meant advanced use cases troubleshooting . timeout error occurs library archive download, length downloaded files differ reported length, increase timeout value help.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Install Torch from files — get_install_libs_url","title":"Install Torch from files — get_install_libs_url","text":"List Torch Lantern libraries URLs download local files order proceed  install_torch_from_file(). Installs Torch dependencies files.","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Install Torch from files — get_install_libs_url","text":"","code":"get_install_libs_url(version = NA, type = NA)  install_torch_from_file(version = NA, type = NA, libtorch, liblantern, ...)"},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Install Torch from files — get_install_libs_url","text":"version used type used. function deprecated. libtorch installation archive file use Torch. Shall \"file://\" URL scheme. liblantern installation archive file use Lantern. Shall \"file://\" URL scheme. ... parameters passed \"install_torch()\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Install Torch from files — get_install_libs_url","text":"\"install_torch()\" initiated download possible, installation archive files present local filesystem, \"install_torch_from_file()\" can used workaround installation issue. \"libtorch\" archive containing torch modules, \"liblantern\" C interface libtorch used R package. highly dependent, checked \"get_install_libs_url()\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/install_torch_from_file.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Install Torch from files — get_install_libs_url","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ # on a linux CPU platform  get_install_libs_url() # then after making both files available into /tmp/ Sys.setenv(TORCH_URL=\"/tmp/libtorch-v1.13.1.zip\") Sys.setenv(LANTERN_URL=\"/tmp/lantern-0.9.1.9001+cpu+arm64-Darwin.zip\") torch::install_torch() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a dataloader — is_dataloader","title":"Checks if the object is a dataloader — is_dataloader","text":"Checks object dataloader","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a dataloader — is_dataloader","text":"","code":"is_dataloader(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_dataloader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a dataloader — is_dataloader","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a nn_buffer — is_nn_buffer","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"Checks object nn_buffer","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"","code":"is_nn_buffer(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a nn_buffer — is_nn_buffer","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is an nn_module — is_nn_module","title":"Checks if the object is an nn_module — is_nn_module","text":"Checks object nn_module","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is an nn_module — is_nn_module","text":"","code":"is_nn_module(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is an nn_module — is_nn_module","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if an object is a nn_parameter — is_nn_parameter","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"Checks object nn_parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"","code":"is_nn_parameter(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_nn_parameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if an object is a nn_parameter — is_nn_parameter","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if the object is a torch optimizer — is_optimizer","title":"Checks if the object is a torch optimizer — is_optimizer","text":"Checks object torch optimizer","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if the object is a torch optimizer — is_optimizer","text":"","code":"is_optimizer(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_optimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if the object is a torch optimizer — is_optimizer","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if object is a device — is_torch_device","title":"Checks if object is a device — is_torch_device","text":"Checks object device","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if object is a device — is_torch_device","text":"","code":"is_torch_device(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if object is a device — is_torch_device","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if object is a torch data type — is_torch_dtype","title":"Check if object is a torch data type — is_torch_dtype","text":"Check object torch data type","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if object is a torch data type — is_torch_dtype","text":"","code":"is_torch_dtype(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_dtype.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if object is a torch data type — is_torch_dtype","text":"x object check.","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a torch layout. — is_torch_layout","title":"Check if an object is a torch layout. — is_torch_layout","text":"Check object torch layout.","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a torch layout. — is_torch_layout","text":"","code":"is_torch_layout(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a torch layout. — is_torch_layout","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a memory format — is_torch_memory_format","title":"Check if an object is a memory format — is_torch_memory_format","text":"Check object memory format","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a memory format — is_torch_memory_format","text":"","code":"is_torch_memory_format(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_memory_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a memory format — is_torch_memory_format","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if an object is a QScheme — is_torch_qscheme","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"Checks object QScheme","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"","code":"is_torch_qscheme(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_torch_qscheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if an object is a QScheme — is_torch_qscheme","text":"x object check","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks if a tensor is undefined — is_undefined_tensor","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"Checks tensor undefined","code":""},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"","code":"is_undefined_tensor(x)"},{"path":"https://torch.mlverse.org/docs/reference/is_undefined_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks if a tensor is undefined — is_undefined_tensor","text":"x tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an iterable dataset — iterable_dataset","title":"Creates an iterable dataset — iterable_dataset","text":"Creates iterable dataset","code":""},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an iterable dataset — iterable_dataset","text":"","code":"iterable_dataset(   name,   inherit = IterableDataset,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an iterable dataset — iterable_dataset","text":"name name dataset. also used class . inherit can optionally inherit dataset creating new dataset. ... public methods dataset class private passed R6::R6Class(). active passed R6::R6Class(). parent_env environment use parent newly-created objects.","code":""},{"path":"https://torch.mlverse.org/docs/reference/iterable_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates an iterable dataset — iterable_dataset","text":"","code":"if (torch_is_installed()) { ids <- iterable_dataset(   name = \"hello\",   initialize = function(n = 5) {     self$n <- n     self$i <- 0   },   .iter = function() {     i <- 0     function() {       i <<- i + 1       if (i > self$n) {         coro::exhausted()       } else {         i       }     }   } ) coro::collect(ids()$.iter()) } #> [[1]] #> [1] 1 #>  #> [[2]] #> [1] 2 #>  #> [[3]] #> [1] 3 #>  #> [[4]] #> [1] 4 #>  #> [[5]] #> [1] 5 #>"},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":null,"dir":"Reference","previous_headings":"","what":"Compile TorchScript code into a graph — jit_compile","title":"Compile TorchScript code into a graph — jit_compile","text":"See TorchScript language reference documentation write TorchScript code.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compile TorchScript code into a graph — jit_compile","text":"","code":"jit_compile(source)"},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compile TorchScript code into a graph — jit_compile","text":"source valid TorchScript source code.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_compile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compile TorchScript code into a graph — jit_compile","text":"","code":"if (torch_is_installed()) { comp <- jit_compile(\" def fn (x):   return torch.abs(x)  def foo (x):   return torch.sum(x)  \")  comp$fn(torch_tensor(-1)) comp$foo(torch_randn(10)) } #> torch_tensor #> 1.60211 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a script_function or script_module previously saved with jit_save — jit_load","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"Loads script_function script_module previously saved jit_save","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"","code":"jit_load(path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a script_function or script_module previously saved with jit_save — jit_load","text":"path path script_function script_module serialized jit_save(). ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable idiomatic access to JIT operators from R. — jit_ops","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"Call JIT operators directly R, keeping familiar argument types argument order. Note, however, : arguments required (defaults) axis numbering (well position numbers overall) starts 0 scalars wrapped jit_scalar()","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"","code":"jit_ops"},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"object class torch_ops length 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_ops.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable idiomatic access to JIT operators from R. — jit_ops","text":"","code":"if (torch_is_installed()) { t1 <- torch::torch_rand(4, 5) t2 <- torch::torch_ones(5, 4) # same as torch::torch_matmul(t1, t2) jit_ops$aten$matmul(t1, t2)  # same as torch_split(torch::torch_arange(0, 3), 2, 1) jit_ops$aten$split(torch::torch_arange(0, 3), torch::jit_scalar(2L), torch::jit_scalar(0L))  } #> [[1]] #> torch_tensor #>  0 #>  1 #> [ CPUFloatType{2} ] #>  #> [[2]] #> torch_tensor #>  2 #>  3 #> [ CPUFloatType{2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a script_function to a path — jit_save","title":"Saves a script_function to a path — jit_save","text":"Saves script_function path","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a script_function to a path — jit_save","text":"","code":"jit_save(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a script_function to a path — jit_save","text":"obj script_function save path path save serialized function. ... currently unused","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a script_function to a path — jit_save","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) }  input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input)  tmp <- tempfile(\"tst\", fileext = \"pt\") jit_save(tr_fn, tmp) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"Saves script_function script_module bytecode form, loaded mobile device","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"","code":"jit_save_for_mobile(obj, path, ...)"},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"obj script_function script_module save path path save serialized function. ... currently unused","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_save_for_mobile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Saves a script_function or script_module in bytecode form, to be loaded on a mobile device — jit_save_for_mobile","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) }  input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input)  tmp <- tempfile(\"tst\", fileext = \"pt\") jit_save_for_mobile(tr_fn, tmp) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds the 'jit_scalar' class to the input — jit_scalar","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"Allows disambiguating length 1 vectors scalars passing jit.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"","code":"jit_scalar(x)"},{"path":"https://torch.mlverse.org/docs/reference/jit_scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds the 'jit_scalar' class to the input — jit_scalar","text":"x length 1 R vector.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_serialize.html","id":null,"dir":"Reference","previous_headings":"","what":"Serialize a Script Module — jit_serialize","title":"Serialize a Script Module — jit_serialize","text":"Serializes script module returns raw vector. can read object using jit_unserialize.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_serialize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Serialize a Script Module — jit_serialize","text":"","code":"jit_serialize(obj)"},{"path":"https://torch.mlverse.org/docs/reference/jit_serialize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Serialize a Script Module — jit_serialize","text":"obj (script_module) Model serialized.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_serialize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Serialize a Script Module — jit_serialize","text":"raw()","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_serialize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Serialize a Script Module — jit_serialize","text":"","code":"if (torch_is_installed()) { model <- jit_trace(nn_linear(1, 1), torch_randn(1)) serialized <- jit_serialize(model) }"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace a function and return an executable script_function. — jit_trace","title":"Trace a function and return an executable script_function. — jit_trace","text":"Using jit_trace, can turn existing R function TorchScript script_function. must provide example inputs, run function, recording operations performed tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace a function and return an executable script_function. — jit_trace","text":"","code":"jit_trace(func, ..., strict = TRUE, respect_mode = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace a function and return an executable script_function. — jit_trace","text":"func R function run example_inputs. func arguments return values must tensors (possibly nested) lists contain tensors. Can also nn_module(), case jit_trace_module() used trace module. ... example inputs passed function tracing. resulting trace can run inputs different types shapes assuming traced operations support types shapes. example_inputs may also single Tensor case automatically wrapped list. Note ... can named, order respected. strict run tracer strict mode (default: TRUE). turn want tracer record mutable container types (currently list/dict) sure container using problem constant structure get used control flow (, ) conditions. respect_mode (logical(1)) Whether modes ('train' 'eval') traced. TRUE (default), underlying C++ ScriptModule two methods trainforward() evalforward(). $forward() method R torch module select either based mode. FALSE, current mode module jitted hence one forward() method exists.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trace a function and return an executable script_function. — jit_trace","text":"script_function func function script_module func nn_module().","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trace a function and return an executable script_function. — jit_trace","text":"resulting recording standalone function produces script_function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Trace a function and return an executable script_function. — jit_trace","text":"Tracing correctly records functions modules data dependent (e.g., conditionals data tensors) untracked external dependencies (e.g., perform input/output access global variables). Tracing records operations done given function run given tensors. Therefore, returned script_function always run traced graph input. important implications module expected run different sets operations, depending input /module state. example, Tracing record control-flow like -statements loops. control-flow constant across module, fine often inlines control-flow decisions. sometimes control-flow actually part model . instance, recurrent network loop (possibly dynamic) length input sequence. returned script_function, operations different behaviors training eval modes always behave mode tracing, matter mode script_function . cases like , tracing appropriate scripting better choice. trace models, may silently get incorrect results subsequent invocations model. tracer try emit warnings something may cause incorrect trace produced. scripting, see jit_compile.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace a function and return an executable script_function. — jit_trace","text":"","code":"if (torch_is_installed()) { fn <- function(x) {   torch_relu(x) } input <- torch_tensor(c(-1, 0, 1)) tr_fn <- jit_trace(fn, input) tr_fn(input) } #> torch_tensor #>  0 #>  0 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace a module — jit_trace_module","title":"Trace a module — jit_trace_module","text":"Trace module return executable ScriptModule optimized using just--time compilation. module passed jit_trace(), forward method run traced. jit_trace_module(), can specify named list method names example inputs trace (see inputs) argument .","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace a module — jit_trace_module","text":"","code":"jit_trace_module(mod, ..., strict = TRUE, respect_mode = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace a module — jit_trace_module","text":"mod torch nn_module()  containing methods whose names specified inputs. given methods compiled part single ScriptModule. ... named list containing sample inputs indexed method names mod. inputs passed methods whose names correspond inputs keys tracing. list('forward'=example_forward_input, 'method2'=example_method2_input). strict run tracer strict mode (default: TRUE). turn want tracer record mutable container types (currently list/dict) sure container using problem constant structure get used control flow (, ) conditions. respect_mode (logical(1)) Whether modes ('train' 'eval') traced. TRUE (default), underlying C++ ScriptModule two methods trainforward() evalforward(). $forward() method R torch module select either based mode. FALSE, current mode module jitted hence one forward() method exists.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trace a module — jit_trace_module","text":"See jit_trace information tracing.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_trace_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace a module — jit_trace_module","text":"","code":"if (torch_is_installed()) { linear <- nn_linear(10, 1) tr_linear <- jit_trace_module(linear, forward = list(torch_randn(10, 10)))  x <- torch_randn(10, 10) torch_allclose(linear(x), tr_linear(x)) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds the 'jit_tuple' class to the input — jit_tuple","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"Allows specifying output input must considered jit tuple instead list dictionary tracing.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"","code":"jit_tuple(x)"},{"path":"https://torch.mlverse.org/docs/reference/jit_tuple.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds the 'jit_tuple' class to the input — jit_tuple","text":"x list object converted tuple.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_unserialize.html","id":null,"dir":"Reference","previous_headings":"","what":"Unserialize a Script Module — jit_unserialize","title":"Unserialize a Script Module — jit_unserialize","text":"Unserializes script module raw vector (generated jit_serialize`).","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_unserialize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unserialize a Script Module — jit_unserialize","text":"","code":"jit_unserialize(obj)"},{"path":"https://torch.mlverse.org/docs/reference/jit_unserialize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unserialize a Script Module — jit_unserialize","text":"obj (raw) Serialized model.","code":""},{"path":"https://torch.mlverse.org/docs/reference/jit_unserialize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unserialize a Script Module — jit_unserialize","text":"script_module model <- jit_trace(nn_linear(1, 1), torch_randn(1)) serialized <- jit_serialize(model) model2 <- jit_unserialize(serialized)","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"Letting K R C, Cholesky decomposition complex Hermitian real symmetric positive-definite matrix  K^n  n defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"","code":"linalg_cholesky(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian positive-definite matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"=LLHL∈Kn×n = LL^{H}\\mathrlap{\\qquad L \\\\mathbb{K}^{n \\times n}} =LLHL∈Kn×n L lower triangular matrix L^H conjugate transpose L complex, transpose L real-valued. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky","text":"","code":"if (torch_is_installed()) { a <- torch_eye(10) linalg_cholesky(a) } #> torch_tensor #>  1  0  0  0  0  0  0  0  0  0 #>  0  1  0  0  0  0  0  0  0  0 #>  0  0  1  0  0  0  0  0  0  0 #>  0  0  0  1  0  0  0  0  0  0 #>  0  0  0  0  1  0  0  0  0  0 #>  0  0  0  0  0  1  0  0  0  0 #>  0  0  0  0  0  0  1  0  0  0 #>  0  0  0  0  0  0  0  1  0  0 #>  0  0  0  0  0  0  0  0  1  0 #>  0  0  0  0  0  0  0  0  0  1 #> [ CPUFloatType{10,10} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"function skips (slow) error checking error message construction linalg_cholesky(), instead directly returning LAPACK error codes part named tuple (L, info). makes function faster way check matrix positive-definite, provides opportunity handle decomposition errors gracefully performantly linalg_cholesky() . Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. Hermitian positive-definite matrix, batch matrices one Hermitian positive-definite matrix, info stores positive integer corresponding matrix. positive integer indicates order leading minor positive-definite, decomposition completed. info filled zeros indicates decomposition successful. check_errors=TRUE info contains positive integers, RuntimeError thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"","code":"linalg_cholesky_ex(A, check_errors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"(Tensor): Hermitian n \\times n matrix batch matrices size (*, n, n) * one batch dimensions. check_errors (bool, optional): controls whether check content infos. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"CUDA device, function may synchronize device CPU. function \"experimental\" may change future PyTorch release.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_cholesky_ex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the Cholesky decomposition of a complex Hermitian or real symmetric positive-definite matrix. — linalg_cholesky_ex","text":"","code":"if (torch_is_installed()) { A <- torch_randn(2, 2) out <- linalg_cholesky_ex(A) out } #> $L #> torch_tensor #>  0.9757  0.0000 #>  1.5533 -1.2421 #> [ CPUFloatType{2,2} ] #>  #> $info #> torch_tensor #> 2 #> [ CPUIntType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"Letting K R C, condition number  matrix  K^n  n defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"","code":"linalg_cond(A, p = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions p (2, -2), shape (*, n, n) every matrix invertible p ('fro', 'nuc', inf, -inf, 1, -1). p (int, inf, -inf, 'fro', 'nuc', optional): type matrix norm use computations (see ). Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"real-valued tensor, even complex.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"κ()=∥∥p∥−1∥p\\kappa() = \\|\\|_p\\|^{-1}\\|_pκ()=∥∥p​∥−1∥p​ condition number measures numerical stability linear system AX = B respect matrix norm. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. p defines matrix norm computed. See table 'Details' find supported norms. p one ('fro', 'nuc', inf, -inf, 1, -1), function uses linalg_norm() linalg_inv(). , case, matrix (every matrix batch) square invertible. p (2, -2), function can computed terms singular values _1  ...  _n κ2()=σ1σnκ−2()=σnσ1\\kappa_2() = \\frac{\\sigma_1}{\\sigma_n}\\qquad \\kappa_{-2}() = \\frac{\\sigma_n}{\\sigma_1}κ2​()=σn​σ1​​κ−2​()=σ1​σn​​ cases, computed using linalg_svd(). norms, matrix (every matrix batch) may shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"inputs CUDA device, function synchronizes device CPU p one ('fro', 'nuc', inf, -inf, 1, -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_cond.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the condition number of a matrix with respect to a matrix norm. — linalg_cond","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(rbind(c(1., 0, -1), c(0, 1, 0), c(1, 0, 1))) linalg_cond(a) linalg_cond(a, \"fro\") } #> torch_tensor #> 3.16228 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the determinant of a square matrix. — linalg_det","title":"Computes the determinant of a square matrix. — linalg_det","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the determinant of a square matrix. — linalg_det","text":"","code":"linalg_det(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the determinant of a square matrix. — linalg_det","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_det.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the determinant of a square matrix. — linalg_det","text":"","code":"if (torch_is_installed()) { a <- torch_randn(3, 3) linalg_det(a)  a <- torch_randn(3, 3, 3) linalg_det(a) } #> torch_tensor #> -0.1249 #>  0.1696 #> -1.5191 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"Letting K R C, eigenvalue decomposition square matrix  K^n  n (exists) defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"","code":"linalg_eig(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting diagonalizable matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"list (eigenvalues, eigenvectors) corresponds  V . eigenvalues eigenvectors always complex-valued, even real. eigenvectors given columns eigenvectors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"=Vdiag⁡(Λ)V−1V∈Cn×n,Λ∈Cn = V \\operatorname{diag}(\\Lambda) V^{-1}\\mathrlap{\\qquad V \\\\mathbb{C}^{n \\times n}, \\Lambda \\\\mathbb{C}^n} =Vdiag(Λ)V−1V∈Cn×n,Λ∈Cn decomposition exists diagonalizable_. case eigenvalues different. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"eigenvalues eigenvectors real matrix may complex.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"function assumes diagonalizable_ (example, eigenvalues different). diagonalizable, returned eigenvalues correct  V diag()V^-1. eigenvectors matrix unique, continuous respect . Due lack uniqueness, different hardware software may compute different eigenvectors. non-uniqueness caused fact multiplying eigenvector non-zero number produces another set valid eigenvectors matrix. implmentation, returned eigenvectors normalized norm 1 largest real component. Gradients computed using V finite repeated eigenvalues. Furthermore, distance two eigenvalues close zero, gradient numerically unstable, depends eigenvalues _i computation 1_i  j _i - _j.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalue decomposition of a square matrix if it exists. — linalg_eig","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) wv <- linalg_eig(a) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"Letting K R C, eigenvalue decomposition complex Hermitian real symmetric matrix  K^n  n defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"","code":"linalg_eigh(A, UPLO = \"L\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian matrices. UPLO ('L', 'U', optional): controls whether use upper lower triangular part computations. Default: 'L'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"list (eigenvalues, eigenvectors) corresponds  Q . eigenvalues always real-valued, even complex. also ordered ascending order. eigenvectors dtype contain eigenvectors columns.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"=Qdiag⁡(Λ)QHQ∈Kn×n,Λ∈Rn = Q \\operatorname{diag}(\\Lambda) Q^{H}\\mathrlap{\\qquad Q \\\\mathbb{K}^{n \\times n}, \\Lambda \\\\mathbb{R}^n} =Qdiag(Λ)QHQ∈Kn×n,Λ∈Rn Q^H conjugate transpose Q complex, transpose Q real-valued. Q orthogonal real case unitary complex case. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. assumed Hermitian (resp. symmetric), checked internally, instead: UPLO\\ = 'L' (default), lower triangular part matrix used computation. UPLO\\ = 'U', upper triangular part matrix used. eigenvalues returned ascending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"eigenvalues real symmetric complex Hermitian matrices always real.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"eigenvectors symmetric matrix unique, continuous respect . Due lack uniqueness, different hardware software may compute different eigenvectors. non-uniqueness caused fact multiplying eigenvector -1 real case e^,   R complex case produces another set valid eigenvectors matrix. non-uniqueness problem even worse matrix repeated eigenvalues. case, one may multiply associated eigenvectors spanning subspace rotation matrix resulting eigenvectors valid eigenvectors. Gradients computed using eigenvectors tensor finite unique eigenvalues. Furthermore, distance two eigvalues close zero, gradient numerically unstable, depends eigenvalues _i computation 1_i  j _i - _j.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalue decomposition of a complex Hermitian or real symmetric matrix. — linalg_eigh","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) linalg_eigh(a) } #> [[1]] #> torch_tensor #> -2.6945 #> -0.1325 #> [ CPUFloatType{2} ] #>  #> [[2]] #> torch_tensor #> -0.8597  0.5108 #> -0.5108 -0.8597 #> [ CPUFloatType{2,2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues of a square matrix. — linalg_eigvals","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"Letting K R C, eigenvalues square matrix  K^n  n defined roots (counted multiplicity) polynomial p degree n given ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"","code":"linalg_eigvals(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"p(λ)=det⁡(−λIn)λ∈C p(\\lambda) = \\operatorname{det}(- \\lambda \\mathrm{}_n)\\mathrlap{\\qquad \\lambda \\\\mathbb{C}} p(λ)=det(−λIn​)λ∈C I_n n-dimensional identity matrix. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"eigenvalues real matrix may complex, roots real polynomial may complex. eigenvalues matrix always well-defined, even matrix diagonalizable.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalues of a square matrix. — linalg_eigvals","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) w <- linalg_eigvals(a) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"Letting K R C, eigenvalues complex Hermitian real symmetric  matrix  K^n  n defined roots (counted multiplicity) polynomial p degree n given ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"","code":"linalg_eigvalsh(A, UPLO = \"L\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting symmetric Hermitian matrices. UPLO ('L', 'U', optional): controls whether use upper lower triangular part computations. Default: 'L'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"real-valued tensor cointaining eigenvalues even complex. eigenvalues returned ascending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"p(λ)=det⁡(−λIn)λ∈R p(\\lambda) = \\operatorname{det}(- \\lambda \\mathrm{}_n)\\mathrlap{\\qquad \\lambda \\\\mathbb{R}} p(λ)=det(−λIn​)λ∈R I_n n-dimensional identity matrix. eigenvalues real symmetric complex Hermitian matrix always real. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. eigenvalues returned ascending order. assumed Hermitian (resp. symmetric), checked internally, instead: UPLO\\ = 'L' (default), lower triangular part matrix used computation. UPLO\\ = 'U', upper triangular part matrix used.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_eigvalsh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the eigenvalues of a complex Hermitian or real symmetric matrix. — linalg_eigvalsh","text":"","code":"if (torch_is_installed()) { a <- torch_randn(2, 2) linalg_eigvalsh(a) } #> torch_tensor #> -1.9836 #>  0.6534 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"Letting K R C, matrix V  K^m  n columns v_i  K^m m  n vector   K^k k  n, function computes first n columns matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"","code":"linalg_householder_product(A, tau)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. tau (Tensor): tensor shape (*, k) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"H1H2...HkwithHi=Im−τiviviH H_1H_2 ... H_k \\qquad \\qquad H_i = \\mathrm{}_m - \\tau_i v_i v_i^{H} H1​H2​...Hk​withHi​=Im​−τi​vi​viH​ I_m m-dimensional identity matrix v^H conjugate transpose v complex, transpose v real-valued. See Representation Orthogonal Unitary Matrices details. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"function uses values strictly main diagonal . values ignored.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_householder_product.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the first n columns of a product of Householder matrices. — linalg_householder_product","text":"","code":"if (torch_is_installed()) { A <- torch_randn(2, 2) h_tau <- torch_geqrf(A) Q <- linalg_householder_product(h_tau[[1]], h_tau[[2]]) torch_allclose(Q, linalg_qr(A)[[1]]) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square matrix if it exists. — linalg_inv","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"Throws runtime_error matrix invertible.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"","code":"linalg_inv(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting invertible matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"Letting K R C, matrix  K^n  n, inverse matrix ^-1  K^n  n (exists) defined −1A=AA−1=^{-1}= AA^{-1} = \\mathrm{}_n −1A=AA−1=​ I_n n-dimensional identity matrix. inverse matrix exists invertible. case, inverse unique. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. Consider using linalg_solve() possible multiplying matrix left inverse, linalg_solve(, B) == $inv() %*% B always prefered use linalg_solve() possible, faster numerically stable computing inverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse of a square matrix if it exists. — linalg_inv","text":"","code":"if (torch_is_installed()) { A <- torch_randn(4, 4) linalg_inv(A) } #> torch_tensor #> -0.2312 -0.7295 -0.1168 -0.8399 #> -0.3578  0.2195  0.5738  0.0203 #>  0.6391  0.8412  0.2621 -0.7401 #> -0.1649  0.0259 -0.4296 -0.0122 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"Returns namedtuple (inverse, info). inverse contains result inverting info stores LAPACK error codes. invertible matrix, batch matrices one invertible matrix, info stores positive integer corresponding matrix. positive integer indicates diagonal element LU decomposition input matrix exactly zero. info filled zeros indicates inversion successful. check_errors=TRUE info contains positive integers, RuntimeError thrown. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"","code":"linalg_inv_ex(A, check_errors = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions consisting square matrices. check_errors (bool, optional): controls whether check content info. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"CUDA device function may synchronize device CPU. function \"experimental\" may change future PyTorch release.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_inv_ex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the inverse of a square matrix if it is invertible. — linalg_inv_ex","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) out <- linalg_inv_ex(A) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"Letting K R C, least squares problem linear system AX = B  K^m  n, B  K^m  k defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"","code":"linalg_lstsq(A, B, rcond = NULL, ..., driver = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"(Tensor): lhs tensor shape (*, m, n) * zero batch dimensions. B (Tensor): rhs tensor shape (*, m, k) * zero batch dimensions. rcond (float, optional): used determine effective rank . rcond = NULL, rcond set machine precision dtype times max(m, n). Default: NULL. ... currently unused. driver (str, optional): name LAPACK/MAGMA method used. NULL, 'gelsy' used CPU inputs 'gels' CUDA inputs. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"list (solution, residuals, rank, singular_values).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"min⁡X∈Kn×k∥AX−B∥F \\min_{X \\\\mathbb{K}^{n \\times k}} \\|AX - B\\|_F X∈Kn×kmin​∥AX−B∥F​ \\|-\\|_F denotes Frobenius norm. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions. driver chooses LAPACK/MAGMA function used. CPU inputs valid values 'gels', 'gelsy', 'gelsd, 'gelss'. CUDA input, valid driver 'gels', assumes full-rank. choose best driver CPU consider: well-conditioned (condition number large), mind precision loss. general matrix: 'gelsy' (QR pivoting) (default) full-rank: 'gels' (QR) well-conditioned. 'gelsd' (tridiagonal reduction SVD) run memory issues: 'gelss' (full SVD). See also full description drivers rcond used determine effective rank matrices driver one ('gelsy', 'gelsd', 'gelss'). case, _i singular values decreasing order, _i rounded zero _i  rcond  _1. rcond = NULL (default), rcond set machine precision dtype . function returns solution problem extra information list four tensors (solution, residuals, rank, singular_values). inputs , B shape (*, m, n), (*, m, k) respectively, cointains solution: least squares solution. shape (*, n, k). residuals: squared residuals solutions, , \\|AX - B\\|_F^2. shape equal batch dimensions . computed m > n every matrix full-rank, otherwise, empty tensor. batch matrices matrix batch full rank, empty tensor returned. behavior may change future PyTorch release. rank: tensor ranks matrices . shape equal batch dimensions . computed driver one ('gelsy', 'gelsd', 'gelss'), otherwise empty tensor. singular_values: tensor singular values matrices . shape (*, min(m, n)). computed driver one ('gelsd', 'gelss'), otherwise empty tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"function computes X = $pinverse() %*% B faster numerically stable way performing computations separately.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"default value rcond may change future PyTorch release. therefore recommended use fixed value avoid potential breaking changes.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_lstsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a solution to the least squares problem of a system of linear equations. — linalg_lstsq","text":"","code":"if (torch_is_installed()) { A <- torch_tensor(rbind(c(10, 2, 3), c(3, 10, 5), c(5, 6, 12)))$unsqueeze(1) # shape (1, 3, 3) B <- torch_stack(list(   rbind(c(2, 5, 1), c(3, 2, 1), c(5, 1, 9)),   rbind(c(4, 2, 9), c(2, 0, 3), c(2, 5, 3)) ), dim = 1) # shape (2, 3, 3) X <- linalg_lstsq(A, B)$solution # A is broadcasted to shape (2, 3, 3) }"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a matrix norm. — linalg_matrix_norm","title":"Computes a matrix norm. — linalg_matrix_norm","text":"complex valued, computes norm $abs() Support input float, double, cfloat cdouble dtypes. Also supports batches matrices: norm computed dimensions specified 2-tuple dim dimensions treated batch dimensions. output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a matrix norm. — linalg_matrix_norm","text":"","code":"linalg_matrix_norm(   A,   ord = \"fro\",   dim = c(-2, -1),   keepdim = FALSE,   dtype = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a matrix norm. — linalg_matrix_norm","text":"(Tensor): tensor two dimensions. default shape interpreted (*, m, n) * zero batch dimensions, behavior can controlled using dim. ord (int, inf, -inf, 'fro', 'nuc', optional): order norm. Default: 'fro' dim (int, Tuple[int], optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a matrix norm. — linalg_matrix_norm","text":"ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a matrix norm. — linalg_matrix_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float())$reshape(c(3, 3)) linalg_matrix_norm(a) linalg_matrix_norm(a, ord = -1) b <- a$expand(c(2, -1, -1)) linalg_matrix_norm(b) linalg_matrix_norm(b, dim = c(1, 3)) } #> torch_tensor #>   3.1623 #>  10.0000 #>  17.2627 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"","code":"linalg_matrix_power(A, n)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"(Tensor): tensor shape (*, m, m) * zero batch dimensions. n (int): exponent.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"n=0, returns identity matrix (batch) shape . n negative, returns inverse matrix (invertible) raised power abs(n).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_power.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the n-th power of a square matrix for an integer n. — linalg_matrix_power","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) linalg_matrix_power(A, 0) } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the numerical rank of a matrix. — linalg_matrix_rank","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"matrix rank computed number singular values (eigenvalues absolute value hermitian = TRUE) greater specified tol threshold.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"","code":"linalg_matrix_rank(   A,   ...,   atol = NULL,   rtol = NULL,   tol = NULL,   hermitian = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. ... currently used. atol absolute tolerance value. NULL ’s considered zero. rtol relative tolerance value. See value takes NULL. tol (float, Tensor, optional): tolerance value. See value takes NULL. Default: NULL. hermitian (bool, optional): indicates whether Hermitian complex symmetric real. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. hermitian = TRUE, assumed Hermitian complex symmetric real, checked internally. Instead, just lower triangular part matrix used computations. tol specified matrix dimensions (m, n), tolerance set tol=σ1max⁡(m,n)ε tol = \\sigma_1 \\max(m, n) \\varepsilon tol=σ1​max(m,n)ε _1 largest singular value (eigenvalue absolute value hermitian = TRUE),  epsilon value dtype (see torch_finfo()). batch matrices, tol computed way every element batch.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_matrix_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the numerical rank of a matrix. — linalg_matrix_rank","text":"","code":"if (torch_is_installed()) { a <- torch_eye(10) linalg_matrix_rank(a) } #> torch_tensor #> 10 #> [ CPULongType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Efficiently multiplies two or more matrices — linalg_multi_dot","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"Efficiently multiplies two matrices reordering multiplications fewest arithmetic operations performed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"","code":"linalg_multi_dot(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"tensors (Sequence[Tensor]): two tensors multiply. first last tensors may 1D 2D. Every tensor must 2D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"Supports inputs float, double, cfloat cdouble dtypes. function support batched inputs. Every tensor tensors must 2D, except first last may 1D. first tensor 1D vector shape (n,) treated row vector shape (1, n), similarly last tensor 1D vector shape (n,) treated column vector shape (n, 1). first last tensors matrices, output matrix. However, either 1D vector, output 1D vector.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"function implemented chaining torch_mm() calls computing optimal matrix multiplication order. cost multiplying two matrices shapes (, b) (b, c) * b * c. Given matrices , B, C shapes (10, 100), (100, 5), (5, 50) respectively, can calculate cost different multiplication orders follows: cost⁡((AB)C)=10×100×5+10×5×50=7500 cost⁡((BC))=10×100×50+100×5×50=75000 \\begin{align*} \\operatorname{cost}((AB)C) &= 10 \\times 100 \\times 5 + 10 \\times 5 \\times 50 = 7500 \\ \\operatorname{cost}((BC)) &= 10 \\times 100 \\times 50 + 100 \\times 5 \\times 50 = 75000 \\end{align*} cost((AB)C)​=10×100×5+10×5×50=7500 cost((BC))​=10×100×50+100×5×50=75000​ case, multiplying B first followed C 10 times faster.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_multi_dot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Efficiently multiplies two or more matrices — linalg_multi_dot","text":"","code":"if (torch_is_installed()) {  linalg_multi_dot(list(torch_tensor(c(1, 2)), torch_tensor(c(2, 3)))) } #> torch_tensor #> 8 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a vector or matrix norm. — linalg_norm","title":"Computes a vector or matrix norm. — linalg_norm","text":"complex valued, computes norm $abs() Supports input float, double, cfloat cdouble dtypes. Whether function computes vector matrix norm determined follows:","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a vector or matrix norm. — linalg_norm","text":"","code":"linalg_norm(A, ord = NULL, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a vector or matrix norm. — linalg_norm","text":"(Tensor): tensor shape (*, n) (*, m, n) * zero batch dimensions ord (int, float, inf, -inf, 'fro', 'nuc', optional): order norm. Default: NULL dim (int, Tuple[int], optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a vector or matrix norm. — linalg_norm","text":"dim int, vector norm computed. dim 2-tuple, matrix norm computed. dim=NULL ord=NULL, flattened 1D 2-norm resulting vector computed. dim=NULL ord!=NULL, must 1D 2D. ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a vector or matrix norm. — linalg_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float()) - 4 a b <- a$reshape(c(3, 3)) b  linalg_norm(a) linalg_norm(b) } #> torch_tensor #> 7.74597 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"pseudoinverse may defined algebraically_ computationally convenient understand SVD_ Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"","code":"linalg_pinv(A, rcond = NULL, hermitian = FALSE, atol = NULL, rtol = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. rcond (float Tensor, optional): tolerance value determine singular value zero torch_Tensor, shape must broadcastable singular values returned linalg_svd(). Alias rtol. Default: 0. hermitian (bool, optional): indicates whether Hermitian complex symmetric real. Default: FALSE. atol absolute tolerance value. NULL ’s considered zero. rtol relative tolerance value. See value takes NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"hermitian= TRUE, assumed Hermitian complex symmetric real, checked internally. Instead, just lower triangular part matrix used computations. singular values (norm eigenvalues hermitian= TRUE) specified rcond threshold treated zero discarded computation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"function uses linalg_svd() hermitian= FALSE linalg_eigh() hermitian= TRUE. CUDA inputs, function synchronizes device CPU. Consider using linalg_lstsq() possible multiplying matrix left pseudoinverse, linalg_lstsq(, B)$solution == $pinv() %*% B always prefered use linalg_lstsq() possible, faster numerically stable computing pseudoinverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_pinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the pseudoinverse (Moore-Penrose inverse) of a matrix. — linalg_pinv","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 5) linalg_pinv(A) } #> torch_tensor #> -0.3203 -0.5515 -0.4246 #>  0.0289 -0.0081  0.2335 #>  0.3591 -0.0429 -0.9052 #> -0.4663 -1.0388  1.8590 #> -0.1553  0.1277  0.7656 #> [ CPUFloatType{5,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the QR decomposition of a matrix. — linalg_qr","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"Letting K R C, full QR decomposition matrix  K^m  n defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"","code":"linalg_qr(A, mode = \"reduced\")"},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. mode (str, optional): one 'reduced', 'complete', 'r'. Controls shape returned tensors. Default: 'reduced'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"list (Q, R).","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"=QRQ∈Km×m,R∈Km×n = QR\\mathrlap{\\qquad Q \\\\mathbb{K}^{m \\times m}, R \\\\mathbb{K}^{m \\times n}} =QRQ∈Km×m,R∈Km×n Q orthogonal real case unitary complex case, R upper triangular. m > n (tall matrix), R upper triangular, last m - n rows zero. case, can drop last m - n columns Q form reduced QR decomposition: =QRQ∈Km×n,R∈Kn×n = QR\\mathrlap{\\qquad Q \\\\mathbb{K}^{m \\times n}, R \\\\mathbb{K}^{n \\times n}} =QRQ∈Km×n,R∈Kn×n reduced QR decomposition agrees full QR decomposition n >= m (wide matrix). Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. parameter mode chooses full reduced QR decomposition. shape (*, m, n), denoting k = min(m, n) mode = 'reduced' (default): Returns (Q, R) shapes (*, m, k), (*, k, n) respectively. mode = 'complete': Returns (Q, R) shapes (*, m, m), (*, m, n) respectively. mode = 'r': Computes reduced R. Returns (Q, R) Q empty R shape (*, k, n).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_qr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the QR decomposition of a matrix. — linalg_qr","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(rbind(c(12., -51, 4), c(6, 167, -68), c(-4, 24, -41))) qr <- linalg_qr(a)  torch_mm(qr[[1]], qr[[2]])$round() torch_mm(qr[[1]]$t(), qr[[1]])$round() } #> torch_tensor #>  1 -0  0 #> -0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"complex , returns angle natural logarithm modulus determinant, , logarithmic polar decomposition determinant. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"","code":"linalg_slogdet(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"list (sign, logabsdet). logabsdet always real-valued, even complex. sign dtype .","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"notes","dir":"Reference","previous_headings":"","what":"Notes","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"determinant can recovered sign * exp(logabsdet). matrix determinant zero, returns (0, -Inf).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_slogdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the sign and natural logarithm of the absolute value of the determinant of a square matrix. — linalg_slogdet","text":"","code":"if (torch_is_installed()) { a <- torch_randn(3, 3) linalg_slogdet(a) } #> [[1]] #> torch_tensor #> -1 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 0.649284 #> [ CPUFloatType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"Letting K R C, function computes solution X  K^n  k linear system associated  K^n  n, B  K^m  k, defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"","code":"linalg_solve(A, B)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"(Tensor): tensor shape (*, n, n) * zero batch dimensions. B (Tensor): right-hand side tensor shape (*, n)  (*, n, k) (n,) (n, k) according rules described ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"$$   AX = B $$ system linear equations one solution invertible_. function assumes invertible. Supports inputs float, double, cfloat cdouble dtypes. Also supports batches matrices, inputs batches matrices output batch dimensions. Letting * zero batch dimensions, shape (*, n, n) B shape (*, n) (batch vectors) shape (*, n, k) (batch matrices \"multiple right-hand sides\"), function returns X shape (*, n) (*, n, k) respectively. Otherwise, shape (*, n, n)  B shape (n,)  (n, k), B broadcasted shape (*, n) (*, n, k) respectively. function returns solution resulting batch systems linear equations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"function computes X = $inverse() @ B faster numerically stable way performing computations separately.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the solution of a square system of linear equations with a unique solution. — linalg_solve","text":"","code":"if (torch_is_installed()) { A <- torch_randn(3, 3) b <- torch_randn(3) x <- linalg_solve(A, b) torch_allclose(torch_matmul(A, x), b) } #> [1] FALSE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular solve — linalg_solve_triangular","title":"Triangular solve — linalg_solve_triangular","text":"Triangular solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular solve — linalg_solve_triangular","text":"","code":"linalg_solve_triangular(A, B, ..., upper, left = TRUE, unitriangular = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_solve_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular solve — linalg_solve_triangular","text":"tensor shape (*, n, n) (*, k, k) left=TRUE) * zero batch dimensions. B right-hand side tensor shape (*, n, k) ... Currently ignored. upper whether upper lower triangular matrix. left wheter solve system AX=B XA=B unitriangular TRUE, diagonal elements assumed equal 1.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"Letting K R C, full SVD matrix  K^m  n, k = min(m,n), defined ","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"","code":"linalg_svd(A, full_matrices = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions. full_matrices (bool, optional): controls whether compute full reduced SVD, consequently, shape returned tensors U V. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"list (U, S, V) corresponds U, S, V^H . S always real-valued, even complex. also ordered descending order. U V dtype . left / right singular vectors given columns U rows V respectively.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"=Udiag⁡(S)VHU∈Km×m,S∈Rk,V∈Kn×n = U \\operatorname{diag}(S) V^{H} \\mathrlap{\\qquad U \\\\mathbb{K}^{m \\times m}, S \\\\mathbb{R}^k, V \\\\mathbb{K}^{n \\times n}} =Udiag(S)VHU∈Km×m,S∈Rk,V∈Kn×n diag(S)  K^m  n, V^H conjugate transpose V complex, transpose V real-valued. matrices  U, V (thus V^H) orthogonal real case, unitary complex case. m > n (resp. m < n) can drop last m - n (resp. n - m) columns U (resp. V) form reduced SVD: =Udiag⁡(S)VHU∈Km×k,S∈Rk,V∈Kk×n = U \\operatorname{diag}(S) V^{H} \\mathrlap{\\qquad U \\\\mathbb{K}^{m \\times k}, S \\\\mathbb{R}^k, V \\\\mathbb{K}^{k \\times n}} =Udiag(S)VHU∈Km×k,S∈Rk,V∈Kk×n diag(S)  K^k  k. case, U V also orthonormal columns. Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. returned decomposition named tuple (U, S, V) corresponds U, S, V^H . singular values returned descending order. parameter full_matrices chooses full (default) reduced SVD.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"full_matrices=TRUE, gradients respect U[..., :, min(m, n):] Vh[..., min(m, n):, :] ignored, vectors can arbitrary bases corresponding subspaces.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"warnings","dir":"Reference","previous_headings":"","what":"Warnings","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"returned tensors U V unique, continuous respect . Due lack uniqueness, different hardware software may compute different singular vectors. non-uniqueness caused fact multiplying pair singular vectors u_k, v_k -1 real case e^,   R complex case produces another two valid singular vectors matrix. non-uniqueness problem even worse matrix repeated singular values. case, one may multiply associated singular vectors U V spanning subspace rotation matrix resulting vectors span subspace. Gradients computed using U V finite zero singular value repeated singular values. Furthermore, distance two singular values close zero, gradient numerically unstable, depends singular values _i computation 1_i  j _i^2 - _j^2. gradient also numerically unstable small singular values, also depends computaiton 1_i.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the singular value decomposition (SVD) of a matrix. — linalg_svd","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(5, 3) linalg_svd(a, full_matrices = FALSE) } #> [[1]] #> torch_tensor #> -0.6009  0.1289  0.0304 #> -0.0997 -0.1008  0.6480 #> -0.4880  0.0750 -0.6420 #> -0.6010 -0.4268  0.3059 #> -0.1721  0.8863  0.2709 #> [ CPUFloatType{5,3} ] #>  #> [[2]] #> torch_tensor #>  3.0966 #>  2.3732 #>  0.8830 #> [ CPUFloatType{3} ] #>  #> [[3]] #> torch_tensor #> -0.0631 -0.9846 -0.1630 #> -0.2267 -0.1449  0.9631 #> -0.9719  0.0977 -0.2141 #> [ CPUFloatType{3,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the singular values of a matrix. — linalg_svdvals","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"Supports input float, double, cfloat cdouble dtypes. Also supports batches matrices, batch matrices output batch dimensions. singular values returned descending order.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"","code":"linalg_svdvals(A)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"(Tensor): tensor shape (*, m, n) * zero batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"real-valued tensor, even complex.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_svdvals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the singular values of a matrix. — linalg_svdvals","text":"","code":"if (torch_is_installed()) { A <- torch_randn(5, 3) S <- linalg_svdvals(A) S } #> torch_tensor #>  2.8025 #>  2.6571 #>  1.4364 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"m product first ind dimensions n product rest dimensions, function expects m n equal. case, computes tensor X tensordot(, X, ind) identity matrix dimension m.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"","code":"linalg_tensorinv(A, ind = 3L)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"(Tensor): tensor invert. ind (int): index compute inverse torch_tensordot(). Default: 3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"Supports input float, double, cfloat cdouble dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"Consider using linalg_tensorsolve() possible multiplying tensor left tensor inverse linalg_tensorsolve(, B) == torch_tensordot(linalg_tensorinv(), B)) always prefered use linalg_tensorsolve() possible, faster numerically stable computing pseudoinverse explicitly.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the multiplicative inverse of torch_tensordot() — linalg_tensorinv","text":"","code":"if (torch_is_installed()) { A <- torch_eye(4 * 6)$reshape(c(4, 6, 8, 3)) Ainv <- linalg_tensorinv(A, ind = 3) Ainv$shape B <- torch_randn(4, 6) torch_allclose(torch_tensordot(Ainv, B), linalg_tensorsolve(A, B))  A <- torch_randn(4, 4) Atensorinv <- linalg_tensorinv(A, 2) Ainv <- linalg_inv(A) torch_allclose(Atensorinv, Ainv) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"m product first B\\ .ndim  dimensions n product rest dimensions, function expects m n equal. returned tensor x satisfies tensordot(, x, dims=x$ndim) == B.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"","code":"linalg_tensorsolve(A, B, dims = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"(Tensor): tensor solve . B (Tensor): solution dims (Tuple[int], optional): dimensions moved. NULL, dimensions moved. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"dims specified, reshaped = movedim(, dims, seq(len(dims) - $ndim + 1, 0)) Supports inputs float, double, cfloat cdouble dtypes.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_tensorsolve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the solution X to the system torch_tensordot(A, X) = B. — linalg_tensorsolve","text":"","code":"if (torch_is_installed()) { A <- torch_eye(2 * 3 * 4)$reshape(c(2 * 3, 4, 2, 3, 4)) B <- torch_randn(2 * 3, 4) X <- linalg_tensorsolve(A, B) X$shape torch_allclose(torch_tensordot(A, X, dims = X$ndim), B)  A <- torch_randn(6, 4, 4, 3, 2) B <- torch_randn(4, 3, 2) X <- linalg_tensorsolve(A, B, dims = c(1, 3)) A <- A$permute(c(2, 4, 5, 1, 3)) torch_allclose(torch_tensordot(A, X, dims = X$ndim), B, atol = 1e-6) } #> [1] FALSE"},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a vector norm. — linalg_vector_norm","title":"Computes a vector norm. — linalg_vector_norm","text":"complex valued, computes norm $abs() Supports input float, double, cfloat cdouble dtypes. function necessarily treat multidimensonal batch vectors, instead:","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a vector norm. — linalg_vector_norm","text":"","code":"linalg_vector_norm(A, ord = 2, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a vector norm. — linalg_vector_norm","text":"(Tensor): tensor, flattened default, behavior can controlled using dim. ord (int, float, inf, -inf, 'fro', 'nuc', optional): order norm. Default: 2 dim (int, Tuple[int], optional): dimensions compute vector matrix norm. See behavior dim=NULL. Default: NULL keepdim (bool, optional): set TRUE, reduced dimensions retained result dimensions size one. Default: FALSE dtype dtype (torch_dtype, optional): specified, input tensor cast dtype performing operation, returned tensor's type dtype. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes a vector norm. — linalg_vector_norm","text":"dim=NULL, flattened norm computed. dim int tuple, norm computed dimensions dimensions treated batch dimensions. behavior consistency linalg_norm(). ord defines norm computed. following norms supported:","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/linalg_vector_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a vector norm. — linalg_vector_norm","text":"","code":"if (torch_is_installed()) { a <- torch_arange(0, 8, dtype = torch_float()) - 4 a b <- a$reshape(c(3, 3)) b  linalg_vector_norm(a, ord = 3.5) linalg_vector_norm(b, ord = 3.5) } #> torch_tensor #> 5.43449 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":null,"dir":"Reference","previous_headings":"","what":"Load a state dict file — load_state_dict","title":"Load a state dict file — load_state_dict","text":"function used load models saved python. work correctly need use torch.save flag: _use_new_zipfile_serialization=True also remove nn.Parameter classes tensors dict.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load a state dict file — load_state_dict","text":"","code":"load_state_dict(path, ..., legacy_stream = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load a state dict file — load_state_dict","text":"path state dict file ... additional arguments currently used. legacy_stream TRUE state dict loaded using legacy way handling streams.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load a state dict file — load_state_dict","text":"named list tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/load_state_dict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load a state dict file — load_state_dict","text":"might change development pytorch's C++ api.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":null,"dir":"Reference","previous_headings":"","what":"Autocast context manager — local_autocast","title":"Autocast context manager — local_autocast","text":"Allow regions code run mixed precision. regions, ops run op-specific dtype chosen autocast improve performance maintaining accuracy.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autocast context manager — local_autocast","text":"","code":"local_autocast(   device_type,   dtype = NULL,   enabled = TRUE,   cache_enabled = NULL,   ...,   .env = parent.frame() )  with_autocast(   code,   ...,   device_type,   dtype = NULL,   enabled = TRUE,   cache_enabled = NULL )  set_autocast(device_type, dtype = NULL, enabled = TRUE, cache_enabled = NULL)  unset_autocast(context)"},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autocast context manager — local_autocast","text":"device_type character string indicating whether use 'cuda' 'cpu' device dtype torch data type indicating whether use torch_float16() torch_bfloat16(). enabled logical value indicating whether autocasting enabled region. Default: TRUE cache_enabled logical value indicating whether weight cache inside autocast enabled. ... currently unused. .env environment use scoping. code code executed gradient recording. context Returned set_autocast passed unsetting .","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Autocast context manager — local_autocast","text":"entering autocast-enabled region, Tensors may type. call half() bfloat16() model(s) inputs using autocasting. autocast enabled forward pass(es) network, including loss computation(s).  Backward passes autocast recommended. Backward ops run type autocast used corresponding forward ops.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Autocast context manager — local_autocast","text":"with_autocast(): context automatic mixed precision. set_autocast(): Set autocast context. advanced users . unset_autocast(): Unset autocast context.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/local_autocast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autocast context manager — local_autocast","text":"","code":"if (torch_is_installed()) { x <- torch_randn(5, 5, dtype = torch_float32()) y <- torch_randn(5, 5, dtype = torch_float32())  foo <- function(x, y) {   local_autocast(device = \"cpu\")   z <- torch_mm(x, y)   w <- torch_mm(z, x)   w }  out <- foo(x, y) }"},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Device contexts — local_device","title":"Device contexts — local_device","text":"Device contexts","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Device contexts — local_device","text":"","code":"local_device(device, ..., .env = parent.frame())  with_device(code, ..., device)"},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Device contexts — local_device","text":"device torch device used default creating new tensors. ... currently unused. .env environment use scoping. code code evaluated modified environment.","code":""},{"path":"https://torch.mlverse.org/docs/reference/local_device.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Device contexts — local_device","text":"with_device(): Modifies default device selected context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_cosine_annealing.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","title":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","text":"Set learning rate parameter group using cosine annealing schedule","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_cosine_annealing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","text":"","code":"lr_cosine_annealing(   optimizer,   T_max,   eta_min = 0,   last_epoch = -1,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_cosine_annealing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the learning rate of each parameter group using a cosine annealing schedule — lr_cosine_annealing","text":"optimizer (Optimizer): Wrapped optimizer. T_max Maximum number iterations eta_min Minimum learning rate. Default: 0. last_epoch index last epoch verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"Sets learning rate parameter group initial lr times given function. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"","code":"lr_lambda(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"optimizer (Optimizer): Wrapped optimizer. lr_lambda (function list): function computes multiplicative factor given integer parameter epoch, list functions, one group optimizer.param_groups. last_epoch (int): index last epoch. Default: -1. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_lambda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr. — lr_lambda","text":"","code":"if (torch_is_installed()) { # Assuming optimizer has two groups. lambda1 <- function(epoch) epoch %/% 30 lambda2 <- function(epoch) 0.95^epoch if (FALSE) { # \\dontrun{ scheduler <- lr_lambda(optimizer, lr_lambda = list(lambda1, lambda2)) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"Multiply learning rate parameter group factor given specified function. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"","code":"lr_multiplicative(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"optimizer (Optimizer): Wrapped optimizer. lr_lambda (function list): function computes multiplicative factor given integer parameter epoch, list functions, one group optimizer.param_groups. last_epoch (int): index last epoch. Default: -1. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_multiplicative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr. — lr_multiplicative","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ lmbda <- function(epoch) 0.95 scheduler <- lr_multiplicative(optimizer, lr_lambda = lmbda) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":null,"dir":"Reference","previous_headings":"","what":"Once cycle learning rate — lr_one_cycle","title":"Once cycle learning rate — lr_one_cycle","text":"Sets learning rate parameter group according 1cycle learning rate policy. 1cycle policy anneals learning rate initial learning rate maximum learning rate maximum learning rate minimum learning rate much lower initial learning rate.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Once cycle learning rate — lr_one_cycle","text":"","code":"lr_one_cycle(   optimizer,   max_lr,   total_steps = NULL,   epochs = NULL,   steps_per_epoch = NULL,   pct_start = 0.3,   anneal_strategy = \"cos\",   cycle_momentum = TRUE,   base_momentum = 0.85,   max_momentum = 0.95,   div_factor = 25,   final_div_factor = 10000,   last_epoch = -1,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Once cycle learning rate — lr_one_cycle","text":"optimizer (Optimizer): Wrapped optimizer. max_lr (float list): Upper learning rate boundaries cycle parameter group. total_steps (int): total number steps cycle. Note value provided , must inferred providing value epochs steps_per_epoch. Default: NULL epochs (int): number epochs train . used along steps_per_epoch order infer total number steps cycle value total_steps provided. Default: NULL steps_per_epoch (int): number steps per epoch train . used along epochs order infer total number steps cycle value total_steps provided. Default: NULL pct_start (float): percentage cycle (number steps) spent increasing learning rate. Default: 0.3 anneal_strategy (str): {'cos', 'linear'} Specifies annealing strategy: \"cos\" cosine annealing, \"linear\" linear annealing. Default: 'cos' cycle_momentum (bool): TRUE, momentum cycled inversely learning rate 'base_momentum' 'max_momentum'. Default: TRUE base_momentum (float list): Lower momentum boundaries cycle parameter group. Note momentum cycled inversely learning rate; peak cycle, momentum 'base_momentum' learning rate 'max_lr'. Default: 0.85 max_momentum (float list): Upper momentum boundaries cycle parameter group. Functionally, defines cycle amplitude (max_momentum - base_momentum). Note momentum cycled inversely learning rate; start cycle, momentum 'max_momentum' learning rate 'base_lr' Default: 0.95 div_factor (float): Determines initial learning rate via initial_lr = max_lr/div_factor Default: 25 final_div_factor (float): Determines minimum learning rate via min_lr = initial_lr/final_div_factor Default: 1e4 last_epoch (int): index last batch. parameter used resuming training job. Since step() invoked batch instead epoch, number represents total number batches computed, total number epochs computed. last_epoch=-1, schedule started beginning. Default: -1 verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Once cycle learning rate — lr_one_cycle","text":"policy initially described paper Super-Convergence: Fast Training Neural Networks Using Large Learning Rates. 1cycle learning rate policy changes learning rate every batch. step called batch used training. scheduler chainable. Note also total number steps cycle can determined one two ways (listed order precedence): value total_steps explicitly provided. number epochs (epochs) number steps per epoch (steps_per_epoch) provided. case, number total steps inferred total_steps = epochs * steps_per_epoch must either provide value total_steps provide value epochs steps_per_epoch.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_one_cycle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Once cycle learning rate — lr_one_cycle","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ data_loader <- dataloader(...) optimizer <- optim_sgd(model$parameters, lr = 0.1, momentum = 0.9) scheduler <- lr_one_cycle(optimizer,   max_lr = 0.01, steps_per_epoch = length(data_loader),   epochs = 10 )  for (i in 1:epochs) {   coro::loop(for (batch in data_loader) {     train_batch(...)     scheduler$step()   }) } } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce learning rate on plateau — lr_reduce_on_plateau","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"Reduce learning rate metric stopped improving. Models often benefit reducing learning rate factor 2-10 learning stagnates. scheduler reads metrics quantity improvement seen 'patience' number epochs, learning rate reduced.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"","code":"lr_reduce_on_plateau(   optimizer,   mode = \"min\",   factor = 0.1,   patience = 10,   threshold = 1e-04,   threshold_mode = \"rel\",   cooldown = 0,   min_lr = 0,   eps = 1e-08,   verbose = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"optimizer (Optimizer): Wrapped optimizer. mode (str): One min, max. min mode, lr reduced quantity monitored stopped decreasing; max mode reduced quantity monitored stopped increasing. Default: 'min'. factor (float): Factor learning rate reduced. new_lr <- lr * factor. Default: 0.1. patience (int): Number epochs improvement learning rate reduced. example, patience = 2, ignore first 2 epochs improvement, decrease LR 3rd epoch loss still improved . Default: 10. threshold (float):Threshold measuring new optimum, focus significant changes. Default: 1e-4. threshold_mode (str): One rel, abs. rel mode, dynamic_threshold <- best * ( 1 + threshold ) 'max' mode best * ( 1 - threshold ) min mode. abs mode, dynamic_threshold <- best + threshold max mode best - threshold min mode. Default: 'rel'. cooldown (int): Number epochs wait resuming normal operation lr reduced. Default: 0. min_lr (float list): scalar list scalars. lower bound learning rate param groups group respectively. Default: 0. eps (float): Minimal decay applied lr. difference new old lr smaller eps, update ignored. Default: 1e-8. verbose (bool): TRUE, prints message stdout update. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_reduce_on_plateau.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reduce learning rate on plateau — lr_reduce_on_plateau","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{  optimizer <- optim_sgd(model$parameters(), lr=0.1, momentum=0.9) scheduler <- lr_reduce_on_plateau(optimizer, 'min') for (epoch in 1:10) {  train(...)  val_loss <- validate(...)  # note that step should be called after validate  scheduler$step(val_loss) } } # } }"},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates learning rate schedulers — lr_scheduler","title":"Creates learning rate schedulers — lr_scheduler","text":"Creates learning rate schedulers","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates learning rate schedulers — lr_scheduler","text":"","code":"lr_scheduler(   classname = NULL,   inherit = LRScheduler,   ...,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/lr_scheduler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates learning rate schedulers — lr_scheduler","text":"classname optional name learning rate scheduler inherit optional learning rate scheduler inherit ... named list methods. must implement get_lr() method take argument returns learning rates param_group optimizer. parent_env passed R6::R6Class().","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Step learning rate decay — lr_step","title":"Step learning rate decay — lr_step","text":"Decays learning rate parameter group gamma every step_size epochs. Notice decay can happen simultaneously changes learning rate outside scheduler. last_epoch=-1, sets initial lr lr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Step learning rate decay — lr_step","text":"","code":"lr_step(optimizer, step_size, gamma = 0.1, last_epoch = -1)"},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Step learning rate decay — lr_step","text":"optimizer (Optimizer): Wrapped optimizer. step_size (int): Period learning rate decay. gamma (float): Multiplicative factor learning rate decay. Default: 0.1. last_epoch (int): index last epoch. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/lr_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Step learning rate decay — lr_step","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ # Assuming optimizer uses lr = 0.05 for all groups # lr = 0.05     if epoch < 30 # lr = 0.005    if 30 <= epoch < 60 # lr = 0.0005   if 60 <= epoch < 90 # ... scheduler <- lr_step(optimizer, step_size = 30, gamma = 0.1) for (epoch in 1:100) {   train(...)   validate(...)   scheduler$step() } } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"output size H, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"","code":"nn_adaptive_avg_pool1d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"output_size target output size H","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool1d","text":"","code":"if (torch_is_installed()) { # target output size of 5 m <- nn_adaptive_avg_pool1d(5) input <- torch_randn(1, 64, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"output size H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"","code":"nn_adaptive_avg_pool2d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"output_size target output size image form H x W. Can tuple (H, W) single H square image H x H. H W can either int, NULL means size input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool2d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7 m <- nn_adaptive_avg_pool2d(c(5, 7)) input <- torch_randn(1, 64, 8, 9) output <- m(input) # target output size of 7x7 (square) m <- nn_adaptive_avg_pool2d(7) input <- torch_randn(1, 64, 10, 9) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"output size D x H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"","code":"nn_adaptive_avg_pool3d(output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"output_size target output size form D x H x W. Can tuple (D, H, W) single number D cube D x D x D. D, H W can either int, None means size input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_avg_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D adaptive average pooling over an input signal composed of several input planes. — nn_adaptive_avg_pool3d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7x9 m <- nn_adaptive_avg_pool3d(c(5, 7, 9)) input <- torch_randn(1, 64, 8, 9, 10) output <- m(input) # target output size of 7x7x7 (cube) m <- nn_adaptive_avg_pool3d(7) input <- torch_randn(1, 64, 10, 9, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Efficient softmax approximation described Efficient softmax approximation GPUs Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, Hervé Jégou","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"","code":"nn_adaptive_log_softmax_with_loss(   in_features,   n_classes,   cutoffs,   div_value = 4,   head_bias = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"in_features (int): Number features input tensor n_classes (int): Number classes dataset cutoffs (Sequence): Cutoffs used assign targets buckets div_value (float, optional): value used exponent compute sizes clusters. Default: 4.0 head_bias (bool, optional): True, adds bias term 'head' adaptive softmax. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"NamedTuple output loss fields: output Tensor size N containing computed target log probabilities example loss Scalar representing computed negative log likelihood loss","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Adaptive softmax approximate strategy training models large output spaces. effective label distribution highly imbalanced, example natural language modelling, word frequency distribution approximately follows Zipf's law. Adaptive softmax partitions labels several clusters, according frequency. clusters may contain different number targets . Additionally, clusters containing less frequent labels assign lower dimensional embeddings labels, speeds computation. minibatch, clusters least one target present evaluated. idea clusters accessed frequently (like first one, containing frequent labels), also cheap compute – , contain small number assigned labels. highly recommend taking look original paper details. cutoffs ordered Sequence integers sorted increasing order. controls number clusters partitioning targets clusters. example setting cutoffs = c(10, 100, 1000) means first 10 targets assigned 'head' adaptive softmax, targets 11, 12, ..., 100 assigned first cluster, targets 101, 102, ..., 1000 assigned second cluster, targets 1001, 1002, ..., n_classes - 1 assigned last, third cluster. div_value used compute size additional cluster, given \\(\\left\\lfloor\\frac{\\mbox{\\_features}}{\\mbox{div\\_value}^{idx}}\\right\\rfloor\\), \\(idx\\) cluster index (clusters less frequent words larger indices, indices starting \\(1\\)). head_bias set True, adds bias term 'head' adaptive softmax. See paper details. Set False official implementation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"module returns NamedTuple output loss fields. See documentation details. compute log-probabilities classes, log_prob method can used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"Labels passed inputs module sorted according frequency. means frequent label represented index 0, least frequent label represented index n_classes - 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_log_softmax_with_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss","text":"input: \\((N, \\mbox{\\_features})\\) target: \\((N)\\) value satisfies \\(0 <= \\mbox{target[]} <= \\mbox{n\\_classes}\\) output1: \\((N)\\) output2: Scalar","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"output size H, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"","code":"nn_adaptive_max_pool1d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"output_size target output size H return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool1d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool1d","text":"","code":"if (torch_is_installed()) { # target output size of 5 m <- nn_adaptive_max_pool1d(5) input <- torch_randn(1, 64, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"output size H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"","code":"nn_adaptive_max_pool2d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"output_size target output size image form H x W. Can tuple (H, W) single H square image H x H. H W can either int, None means size input. return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool2d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool2d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7 m <- nn_adaptive_max_pool2d(c(5, 7)) input <- torch_randn(1, 64, 8, 9) output <- m(input) # target output size of 7x7 (square) m <- nn_adaptive_max_pool2d(7) input <- torch_randn(1, 64, 10, 9) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"output size D x H x W, input size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"","code":"nn_adaptive_max_pool3d(output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"output_size target output size image form D x H x W. Can tuple (D, H, W) single D cube D x D x D. D, H W can either int, None means size input. return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool3d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_adaptive_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D adaptive max pooling over an input signal composed of several input planes. — nn_adaptive_max_pool3d","text":"","code":"if (torch_is_installed()) { # target output size of 5x7x9 m <- nn_adaptive_max_pool3d(c(5, 7, 9)) input <- torch_randn(1, 64, 8, 9, 10) output <- m(input) # target output size of 7x7x7 (cube) m <- nn_adaptive_max_pool3d(7) input <- torch_randn(1, 64, 10, 9, 8) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"simplest case, output value layer input size \\((N, C, L)\\), output \\((N, C, L_{})\\) kernel_size \\(k\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"","code":"nn_avg_pool1d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"$$   \\mbox{}(N_i, C_j, l) = \\frac{1}{k} \\sum_{m=0}^{k-1} \\mbox{input}(N_i, C_j, \\mbox{stride} \\times l + m) $$ padding non-zero, input implicitly zero-padded sides padding number points. parameters kernel_size, stride, padding can int one-element tuple.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor \\frac{L_{} +       2 \\times \\mbox{padding} - \\mbox{kernel\\_size}}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D average pooling over an input signal composed of several input planes. — nn_avg_pool1d","text":"","code":"if (torch_is_installed()) {  # pool with window of size=3, stride=2 m <- nn_avg_pool1d(3, stride = 2) m(torch_randn(1, 1, 8)) } #> torch_tensor #> (1,.,.) =  #>   0.7407  0.0182 -0.5100 #> [ CPUFloatType{1,1,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"simplest case, output value layer input size \\((N, C, H, W)\\), output \\((N, C, H_{}, W_{})\\) kernel_size \\((kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"","code":"nn_avg_pool2d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation divisor_override specified, used divisor, otherwise kernel_size used","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"$$   (N_i, C_j, h, w)  = \\frac{1}{kH * kW} \\sum_{m=0}^{kH-1} \\sum_{n=0}^{kW-1} input(N_i, C_j, stride[0] \\times h + m, stride[1] \\times w + n) $$ padding non-zero, input implicitly zero-padded sides padding number points. parameters kernel_size, stride, padding can either : single int – case value used height width dimension tuple two ints – case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{}  + 2 \\times \\mbox{padding}[0] -       \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{}  + 2 \\times \\mbox{padding}[1] -       \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D average pooling over an input signal composed of several input planes. — nn_avg_pool2d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 m <- nn_avg_pool2d(3, stride = 2) # pool of non-square window m <- nn_avg_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"simplest case, output value layer input size \\((N, C, D, H, W)\\), output \\((N, C, D_{}, H_{}, W_{})\\) kernel_size \\((kD, kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"","code":"nn_avg_pool3d(   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"kernel_size size window stride stride window. Default value kernel_size padding implicit zero padding added three sides ceil_mode TRUE, use ceil instead floor compute output shape count_include_pad TRUE, include zero-padding averaging calculation divisor_override specified, used divisor, otherwise kernel_size used","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"$$ \\begin{array}{ll} \\mbox{}(N_i, C_j, d, h, w) = & \\sum_{k=0}^{kD-1} \\sum_{m=0}^{kH-1} \\sum_{n=0}^{kW-1} \\\\ & \\frac{\\mbox{input}(N_i, C_j, \\mbox{stride}[0] \\times d + k, \\mbox{stride}[1] \\times h + m, \\mbox{stride}[2] \\times w + n)}{kD \\times kH \\times kW} \\end{array} $$ padding non-zero, input implicitly zero-padded three sides padding number points. parameters kernel_size, stride can either : single int – case value used depth, height width dimension tuple three ints – case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] -       \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] -       \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] -       \\mbox{kernel\\_size}[2]}{\\mbox{stride}[2]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_avg_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D average pooling over an input signal composed of several input planes. — nn_avg_pool3d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 m <- nn_avg_pool3d(3, stride = 2) # pool of non-square window m <- nn_avg_pool3d(c(3, 2, 2), stride = c(2, 1, 2)) input <- torch_randn(20, 16, 50, 44, 31) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm1D module — nn_batch_norm1d","title":"BatchNorm1D module — nn_batch_norm1d","text":"Applies Batch Normalization 2D 3D input (mini-batch 1D inputs optional additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm1D module — nn_batch_norm1d","text":"","code":"nn_batch_norm1d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm1D module — nn_batch_norm1d","text":"num_features \\(C\\) expected input size \\((N, C, L)\\) \\(L\\) input size \\((N, L)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set NULL cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics always uses batch statistics training eval modes. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm1D module — nn_batch_norm1d","text":"$$ y = \\frac{x - \\mathrm{E}[x]}{\\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default :attr:momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm1D module — nn_batch_norm1d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, L) slices, common terminology call Temporal Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm1D module — nn_batch_norm1d","text":"Input: \\((N, C)\\) \\((N, C, L)\\) Output: \\((N, C)\\) \\((N, C, L)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm1D module — nn_batch_norm1d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm1d(100) # Without Learnable Parameters m <- nn_batch_norm1d(100, affine = FALSE) input <- torch_randn(20, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm2D — nn_batch_norm2d","title":"BatchNorm2D — nn_batch_norm2d","text":"Applies Batch Normalization 4D input (mini-batch 2D inputs additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm2D — nn_batch_norm2d","text":"","code":"nn_batch_norm2d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm2D — nn_batch_norm2d","text":"num_features \\(C\\) expected input size \\((N, C, H, W)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics uses batch statistics instead training eval modes running mean variance None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm2D — nn_batch_norm2d","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE). Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm2D — nn_batch_norm2d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, H, W) slices, common terminology call Spatial Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm2D — nn_batch_norm2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm2D — nn_batch_norm2d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm2d(100) # Without Learnable Parameters m <- nn_batch_norm2d(100, affine = FALSE) input <- torch_randn(20, 100, 35, 45) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":null,"dir":"Reference","previous_headings":"","what":"BatchNorm3D — nn_batch_norm3d","title":"BatchNorm3D — nn_batch_norm3d","text":"Applies Batch Normalization 5D input (mini-batch 3D inputs additional channel dimension) described paper Batch Normalization: Accelerating Deep Network Training Reducing Internal Covariate Shift.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BatchNorm3D — nn_batch_norm3d","text":"","code":"nn_batch_norm3d(   num_features,   eps = 1e-05,   momentum = 0.1,   affine = TRUE,   track_running_stats = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BatchNorm3D — nn_batch_norm3d","text":"num_features \\(C\\) expected input size \\((N, C, D, H, W)\\) eps value added denominator numerical stability. Default: 1e-5 momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 affine boolean value set TRUE, module learnable affine parameters. Default: TRUE track_running_stats boolean value set TRUE, module tracks running mean variance, set FALSE, module track statistics uses batch statistics instead training eval modes running mean variance None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BatchNorm3D — nn_batch_norm3d","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated per-dimension mini-batches \\(\\gamma\\) \\(\\beta\\) learnable parameter vectors size C (C input size). default, elements \\(\\gamma\\) set 1 elements \\(\\beta\\) set 0. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased = FALSE). Also default, training layer keeps running estimates computed mean variance, used normalization evaluation. running estimates kept default momentum 0.1. track_running_stats set FALSE, layer keep running estimates, batch statistics instead used evaluation time well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"BatchNorm3D — nn_batch_norm3d","text":"momentum argument different one used optimizer classes conventional notion momentum. Mathematically, update rule running statistics : \\(\\hat{x}_{\\mbox{new}} = (1 - \\mbox{momentum}) \\times \\hat{x} + \\mbox{momentum} \\times x_t\\), \\(\\hat{x}\\) estimated statistic \\(x_t\\) new observed value. Batch Normalization done C dimension, computing statistics (N, D, H, W) slices, common terminology call Volumetric Batch Normalization Spatio-temporal Batch Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BatchNorm3D — nn_batch_norm3d","text":"Input: \\((N, C, D, H, W)\\) Output: \\((N, C, D, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_batch_norm3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BatchNorm3D — nn_batch_norm3d","text":"","code":"if (torch_is_installed()) { # With Learnable Parameters m <- nn_batch_norm3d(100) # Without Learnable Parameters m <- nn_batch_norm3d(100, affine = FALSE) input <- torch_randn(20, 100, 35, 45, 55) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary cross entropy loss — nn_bce_loss","title":"Binary cross entropy loss — nn_bce_loss","text":"Creates criterion measures Binary Cross Entropy target output:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary cross entropy loss — nn_bce_loss","text":"","code":"nn_bce_loss(weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary cross entropy loss — nn_bce_loss","text":"weight (Tensor, optional): manual rescaling weight given loss batch element. given, Tensor size nbatch. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Binary cross entropy loss — nn_bce_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_n \\left[ y_n \\cdot \\log x_n + (1 - y_n) \\cdot \\log (1 - x_n) \\right] $$ \\(N\\) batch size. reduction 'none' (default 'mean'), $$   \\ell(x, y) = \\left\\{ \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} \\right. $$ used measuring error reconstruction example auto-encoder. Note targets \\(y\\) numbers 0 1. Notice \\(x_n\\) either 0 1, one log terms mathematically undefined loss equation. PyTorch chooses set \\(\\log (0) = -\\infty\\), since \\(\\lim_{x\\0} \\log (x) = -\\infty\\). However, infinite term loss equation desirable several reasons. one, either \\(y_n = 0\\) \\((1 - y_n) = 0\\), multiplying 0 infinity. Secondly, infinite loss value, also infinite term gradient, since \\(\\lim_{x\\0} \\frac{d}{dx} \\log (x) = \\infty\\). make BCELoss's backward method nonlinear respect \\(x_n\\), using things like linear regression straight-forward. solution BCELoss clamps log function outputs greater equal -100. way, can always finite loss value linear backward method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Binary cross entropy loss — nn_bce_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Binary cross entropy loss — nn_bce_loss","text":"","code":"if (torch_is_installed()) { m <- nn_sigmoid() loss <- nn_bce_loss() input <- torch_randn(3, requires_grad = TRUE) target <- torch_rand(3) output <- loss(m(input), target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"BCE with logits loss — nn_bce_with_logits_loss","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"loss combines Sigmoid layer BCELoss one single class. version numerically stable using plain Sigmoid followed BCELoss , combining operations one layer, take advantage log-sum-exp trick numerical stability.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"","code":"nn_bce_with_logits_loss(weight = NULL, reduction = \"mean\", pos_weight = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"weight (Tensor, optional): manual rescaling weight given loss batch element. given, Tensor size nbatch. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. pos_weight (Tensor, optional): weight positive examples. Must vector length equal number classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_n \\left[ y_n \\cdot \\log \\sigma(x_n)                    + (1 - y_n) \\cdot \\log (1 - \\sigma(x_n)) \\right], $$ \\(N\\) batch size. reduction 'none' (default 'mean'), $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ used measuring error reconstruction example auto-encoder. Note targets t[] numbers 0 1. possible trade recall precision adding weights positive examples. case multi-label classification loss can described : $$ \\ell_c(x, y) = L_c = \\{l_{1,c},\\dots,l_{N,c}\\}^\\top, \\quad l_{n,c} = - w_{n,c} \\left[ p_c y_{n,c} \\cdot \\log \\sigma(x_{n,c}) + (1 - y_{n,c}) \\cdot \\log (1 - \\sigma(x_{n,c})) \\right], $$ \\(c\\) class number (\\(c > 1\\) multi-label binary classification, \\(c = 1\\) single-label binary classification), \\(n\\) number sample batch \\(p_c\\) weight positive answer class \\(c\\). \\(p_c > 1\\) increases recall, \\(p_c < 1\\) increases precision. example, dataset contains 100 positive 300 negative examples single class, pos_weight class equal \\(\\frac{300}{100}=3\\). loss act dataset contains \\(3\\times 100=300\\) positive examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bce_with_logits_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BCE with logits loss — nn_bce_with_logits_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_bce_with_logits_loss() input <- torch_randn(3, requires_grad = TRUE) target <- torch_empty(3)$random_(1, 2) output <- loss(input, target) output$backward()  target <- torch_ones(10, 64, dtype = torch_float32()) # 64 classes, batch size = 10 output <- torch_full(c(10, 64), 1.5) # A prediction (logit) pos_weight <- torch_ones(64) # All weights are equal to 1 criterion <- nn_bce_with_logits_loss(pos_weight = pos_weight) criterion(output, target) # -log(sigmoid(1.5)) } #> torch_tensor #> 0.201413 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":null,"dir":"Reference","previous_headings":"","what":"Bilinear module — nn_bilinear","title":"Bilinear module — nn_bilinear","text":"Applies bilinear transformation incoming data \\(y = x_1^T x_2 + b\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bilinear module — nn_bilinear","text":"","code":"nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bilinear module — nn_bilinear","text":"in1_features size first input sample in2_features size second input sample out_features size output sample bias set FALSE, layer learn additive bias. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Bilinear module — nn_bilinear","text":"Input1: \\((N, *, H_{in1})\\) \\(H_{in1}=\\mbox{in1\\_features}\\) \\(*\\) means number additional dimensions. last dimension inputs . Input2: \\((N, *, H_{in2})\\) \\(H_{in2}=\\mbox{in2\\_features}\\). Output: \\((N, *, H_{})\\) \\(H_{}=\\mbox{\\_features}\\) last dimension shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Bilinear module — nn_bilinear","text":"weight: learnable weights module shape \\((\\mbox{\\_features}, \\mbox{in1\\_features}, \\mbox{in2\\_features})\\). values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\), \\(k = \\frac{1}{\\mbox{in1\\_features}}\\) bias: learnable bias module shape \\((\\mbox{\\_features})\\). bias TRUE, values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\), \\(k = \\frac{1}{\\mbox{in1\\_features}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_bilinear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bilinear module — nn_bilinear","text":"","code":"if (torch_is_installed()) { m <- nn_bilinear(20, 30, 50) input1 <- torch_randn(128, 20) input2 <- torch_randn(128, 30) output <- m(input1, input2) print(output$size()) } #> [1] 128  50"},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a nn_buffer — nn_buffer","title":"Creates a nn_buffer — nn_buffer","text":"Indicates tensor buffer nn_module","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a nn_buffer — nn_buffer","text":"","code":"nn_buffer(x, persistent = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a nn_buffer — nn_buffer","text":"x tensor converted nn_buffer persistent whether buffer persistent .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"CELU module — nn_celu","title":"CELU module — nn_celu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CELU module — nn_celu","text":"","code":"nn_celu(alpha = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CELU module — nn_celu","text":"alpha \\(\\alpha\\) value CELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CELU module — nn_celu","text":"$$   \\mbox{CELU}(x) = \\max(0,x) + \\min(0, \\alpha * (\\exp(x/\\alpha) - 1)) $$ details can found paper Continuously Differentiable Exponential Linear Units.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"CELU module — nn_celu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_celu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CELU module — nn_celu","text":"","code":"if (torch_is_installed()) { m <- nn_celu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparsemax activation — nn_contrib_sparsemax","title":"Sparsemax activation — nn_contrib_sparsemax","text":"Sparsemax activation module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparsemax activation — nn_contrib_sparsemax","text":"","code":"nn_contrib_sparsemax(dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparsemax activation — nn_contrib_sparsemax","text":"dim dimension apply sparsemax function. (-1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_contrib_sparsemax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sparsemax activation — nn_contrib_sparsemax","text":"SparseMax activation described 'Softmax Sparsemax: Sparse Model Attention Multi-Label Classification' implementation based aced125/sparsemax","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1D module — nn_conv1d","title":"Conv1D module — nn_conv1d","text":"Applies 1D convolution input signal composed several input planes. simplest case, output value layer input size \\((N, C_{\\mbox{}}, L)\\) output \\((N, C_{\\mbox{}}, L_{\\mbox{}})\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1D module — nn_conv1d","text":"","code":"nn_conv1d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1D module — nn_conv1d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int, tuple str, optional) – Padding added sides input. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv1D module — nn_conv1d","text":"$$ \\mbox{}(N_i, C_{\\mbox{}_j}) = \\mbox{bias}(C_{\\mbox{}_j}) +   \\sum_{k = 0}^{C_{} - 1} \\mbox{weight}(C_{\\mbox{}_j}, k) \\star \\mbox{input}(N_i, k) $$ \\(\\star\\) valid cross-correlation operator, \\(N\\) batch size, \\(C\\) denotes number channels, \\(L\\) length signal sequence. stride controls stride cross-correlation, single number one-element tuple. padding controls amount implicit zero-paddings sides padding number points. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv1D module — nn_conv1d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size \\((N, C_{}, L_{})\\), depthwise convolution depthwise multiplier K, can constructed arguments \\((C_{\\mbox{}}=C_{}, C_{\\mbox{}}=C_{} \\times K, ..., \\mbox{groups}=C_{})\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv1D module — nn_conv1d","text":"Input: \\((N, C_{}, L_{})\\) Output: \\((N, C_{}, L_{})\\) $$   L_{} = \\left\\lfloor\\frac{L_{} + 2 \\times \\mbox{padding} - \\mbox{dilation}     \\times (\\mbox{kernel\\_size} - 1) - 1}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv1D module — nn_conv1d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}}, \\mbox{kernel\\_size})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\) bias (Tensor): learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv1D module — nn_conv1d","text":"","code":"if (torch_is_installed()) { m <- nn_conv1d(16, 33, 3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2D module — nn_conv2d","title":"Conv2D module — nn_conv2d","text":"Applies 2D convolution input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2D module — nn_conv2d","text":"","code":"nn_conv2d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2D module — nn_conv2d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple string, optional): Zero-padding added sides input. controls amount padding applied input. can either string 'valid', '' tuple ints giving amount implicit padding applied sides. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv2D module — nn_conv2d","text":"simplest case, output value layer input size \\((N, C_{\\mbox{}}, H, W)\\) output \\((N, C_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}})\\) can precisely described : $$ \\mbox{}(N_i, C_{\\mbox{}_j}) = \\mbox{bias}(C_{\\mbox{}_j}) +   \\sum_{k = 0}^{C_{\\mbox{}} - 1} \\mbox{weight}(C_{\\mbox{}_j}, k) \\star \\mbox{input}(N_i, k) $$ \\(\\star\\) valid 2D cross-correlation operator, \\(N\\) batch size, \\(C\\) denotes number channels, \\(H\\) height input planes pixels, \\(W\\) width pixels. stride controls stride cross-correlation, single number tuple. padding controls amount implicit zero-paddings sides padding number points dimension. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size: \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\). parameters kernel_size, stride, padding, dilation can either : single int – case value used height width dimension tuple two ints – case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv2D module — nn_conv2d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size :math:(N, C_{}, H_{}, W_{}), depthwise convolution depthwise multiplier K, can constructed arguments \\((\\_channels=C_{}, \\_channels=C_{} \\times K, ..., groups=C_{})\\). circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting backends_cudnn_deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv2D module — nn_conv2d","text":"Input: \\((N, C_{}, H_{}, W_{})\\) Output: \\((N, C_{}, H_{}, W_{})\\) $$   H_{} = \\left\\lfloor\\frac{H_{}  + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0]     \\times (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{}  + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1]     \\times (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv2D module — nn_conv2d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}}\\), \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\) bias (Tensor): learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv2D module — nn_conv2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride m <- nn_conv2d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2)) # non-square kernels and unequal stride and with padding and dilation m <- nn_conv2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2), dilation = c(3, 1)) input <- torch_randn(20, 16, 50, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3D module — nn_conv3d","title":"Conv3D module — nn_conv3d","text":"Applies 3D convolution input signal composed several input planes. simplest case, output value layer input size \\((N, C_{}, D, H, W)\\) output \\((N, C_{}, D_{}, H_{}, W_{})\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3D module — nn_conv3d","text":"","code":"nn_conv3d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   dilation = 1,   groups = 1,   bias = TRUE,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3D module — nn_conv3d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int, tuple str, optional): padding added six sides input. Default: 0 dilation (int tuple, optional): Spacing kernel elements. Default: 1 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): TRUE, adds learnable bias output. Default: TRUE padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conv3D module — nn_conv3d","text":"$$   (N_i, C_{out_j}) = bias(C_{out_j}) +   \\sum_{k = 0}^{C_{} - 1} weight(C_{out_j}, k) \\star input(N_i, k) $$ \\(\\star\\) valid 3D cross-correlation operator stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides padding number points dimension. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters, size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\). parameters kernel_size, stride, padding, dilation can either : single int – case value used depth, height width dimension tuple three ints – case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Conv3D module — nn_conv3d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. groups == in_channels out_channels == K * in_channels, K positive integer, operation also termed literature depthwise convolution. words, input size \\((N, C_{}, D_{}, H_{}, W_{})\\), depthwise convolution depthwise multiplier K, can constructed arguments \\((\\_channels=C_{}, \\_channels=C_{} \\times K, ..., groups=C_{})\\). circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE. Please see notes :doc:/notes/randomness background.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Conv3D module — nn_conv3d","text":"Input: \\((N, C_{}, D_{}, H_{}, W_{})\\) Output: \\((N, C_{}, D_{}, H_{}, W_{})\\) $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0]     \\times (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor  $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1]     \\times (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor  $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] - \\mbox{dilation}[2]     \\times (\\mbox{kernel\\_size}[2] - 1) - 1}{\\mbox{stride}[2]} + 1\\right\\rfloor  $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Conv3D module — nn_conv3d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]}, \\mbox{kernel\\_size[2]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels). bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv3D module — nn_conv3d","text":"","code":"if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv3d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(4, 2, 0)) input <- torch_randn(20, 16, 10, 50, 100) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranspose1D — nn_conv_transpose1d","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Applies 1D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranspose1D — nn_conv_transpose1d","text":"","code":"nn_conv_transpose1d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranspose1D — nn_conv_transpose1d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides input. Default: 0 output_padding (int tuple, optional): Additional size added one side output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: TRUE dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranspose1D — nn_conv_transpose1d","text":"module can seen gradient Conv1d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set ~torch.nn.Conv1d ~torch.nn.ConvTranspose1d initialized parameters, inverses regard input output shapes. However, stride > 1, ~torch.nn.Conv1d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranspose1D — nn_conv_transpose1d","text":"Input: \\((N, C_{}, L_{})\\) Output: \\((N, C_{}, L_{})\\) $$   L_{} = (L_{} - 1) \\times \\mbox{stride} - 2 \\times \\mbox{padding} + \\mbox{dilation} \\times (\\mbox{kernel\\_size} - 1) + \\mbox{output\\_padding} + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranspose1D — nn_conv_transpose1d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\) bias (Tensor):   learnable bias module shape (out_channels). bias TRUE, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\mbox{kernel\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranspose1D — nn_conv_transpose1d","text":"","code":"if (torch_is_installed()) { m <- nn_conv_transpose1d(32, 16, 2) input <- torch_randn(10, 32, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranpose2D module — nn_conv_transpose2d","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"","code":"nn_conv_transpose2d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides dimension input. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: True dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"module can seen gradient Conv2d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)). parameters kernel_size, stride, padding, output_padding can either : single int – case value used height width dimensions tuple two ints – case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation_, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set nn_conv2d nn_conv_transpose2d initialized parameters, inverses regard input output shapes. However, stride > 1, nn_conv2d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"Input: \\((N, C_{}, H_{}, W_{})\\) Output: \\((N, C_{}, H_{}, W_{})\\) $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{dilation}[0] \\times (\\mbox{kernel\\_size}[0] - 1) + \\mbox{output\\_padding}[0] + 1 $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride}[1] - 2 \\times \\mbox{padding}[1] + \\mbox{dilation}[1] \\times (\\mbox{kernel\\_size}[1] - 1) + \\mbox{output\\_padding}[1] + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels) bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{1}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranpose2D module — nn_conv_transpose2d","text":"","code":"if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv_transpose2d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose2d(16, 33, c(3, 5), stride = c(2, 1), padding = c(4, 2)) input <- torch_randn(20, 16, 50, 100) output <- m(input) # exact output size can be also specified as an argument input <- torch_randn(1, 16, 12, 12) downsample <- nn_conv2d(16, 16, 3, stride = 2, padding = 1) upsample <- nn_conv_transpose2d(16, 16, 3, stride = 2, padding = 1) h <- downsample(input) h$size() output <- upsample(h, output_size = input$size()) output$size() } #> [1]  1 16 12 12"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"ConvTranpose3D module — nn_conv_transpose3d","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"","code":"nn_conv_transpose3d(   in_channels,   out_channels,   kernel_size,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   bias = TRUE,   dilation = 1,   padding_mode = \"zeros\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"in_channels (int): Number channels input image out_channels (int): Number channels produced convolution kernel_size (int tuple): Size convolving kernel stride (int tuple, optional): Stride convolution. Default: 1 padding (int tuple, optional): dilation * (kernel_size - 1) - padding zero-padding added sides dimension input. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 output_padding (int tuple, optional): Additional size added one side dimension output shape. Default: 0 groups (int, optional): Number blocked connections input channels output channels. Default: 1 bias (bool, optional): True, adds learnable bias output. Default: True dilation (int tuple, optional): Spacing kernel elements. Default: 1 padding_mode (string, optional): 'zeros', 'reflect', 'replicate' 'circular'. Default: 'zeros'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"transposed convolution operator multiplies input value element-wise learnable kernel, sums outputs input feature planes. module can seen gradient Conv3d respect input. also known fractionally-strided convolution deconvolution (although actual deconvolution operation). stride controls stride cross-correlation. padding controls amount implicit zero-paddings sides dilation * (kernel_size - 1) - padding number points. See note details. output_padding controls additional size added one side output shape. See note details. dilation controls spacing kernel points; also known à trous algorithm. harder describe, link_ nice visualization dilation . groups controls connections inputs outputs. in_channels out_channels must divisible groups. example, groups=1, inputs convolved outputs. groups=2, operation becomes equivalent two conv layers side side, seeing half input channels, producing half output channels, subsequently concatenated. groups= in_channels, input channel convolved set filters (size \\(\\left\\lfloor\\frac{\\_channels}{\\_channels}\\right\\rfloor\\)). parameters kernel_size, stride, padding, output_padding can either : single int – case value used depth, height width dimensions tuple three ints – case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Depending size kernel, several (last) columns input might lost, valid cross-correlation, full cross-correlation. user add proper padding. padding argument effectively adds dilation * (kernel_size - 1) - padding amount zero padding sizes input. set ~torch.nn.Conv3d ~torch.nn.ConvTranspose3d initialized parameters, inverses regard input output shapes. However, stride > 1, ~torch.nn.Conv3d maps multiple input shapes output shape. output_padding provided resolve ambiguity effectively increasing calculated output shape one side. Note output_padding used find output shape, actually add zero-padding output. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"Input: \\((N, C_{}, D_{}, H_{}, W_{})\\) Output: \\((N, C_{}, D_{}, H_{}, W_{})\\) $$   D_{} = (D_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{dilation}[0] \\times (\\mbox{kernel\\_size}[0] - 1) + \\mbox{output\\_padding}[0] + 1 $$ $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[1] - 2 \\times \\mbox{padding}[1] + \\mbox{dilation}[1] \\times (\\mbox{kernel\\_size}[1] - 1) + \\mbox{output\\_padding}[1] + 1 $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride}[2] - 2 \\times \\mbox{padding}[2] + \\mbox{dilation}[2] \\times (\\mbox{kernel\\_size}[2] - 1) + \\mbox{output\\_padding}[2] + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"weight (Tensor): learnable weights module shape \\((\\mbox{\\_channels}, \\frac{\\mbox{\\_channels}}{\\mbox{groups}},\\) \\(\\mbox{kernel\\_size[0]}, \\mbox{kernel\\_size[1]}, \\mbox{kernel\\_size[2]})\\). values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\) bias (Tensor):   learnable bias module shape (out_channels) bias True, values weights sampled \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{groups}{C_{\\mbox{}} * \\prod_{=0}^{2}\\mbox{kernel\\_size}[]}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_conv_transpose3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ConvTranpose3D module — nn_conv_transpose3d","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ # With square kernels and equal stride m <- nn_conv_transpose3d(16, 33, 3, stride = 2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose3d(16, 33, c(3, 5, 2), stride = c(2, 1, 1), padding = c(0, 4, 2)) input <- torch_randn(20, 16, 10, 50, 100) output <- m(input) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine embedding loss — nn_cosine_embedding_loss","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"Creates criterion measures loss given input tensors \\(x_1\\), \\(x_2\\) Tensor label \\(y\\) values 1 -1. used measuring whether two inputs similar dissimilar, using cosine distance, typically used learning nonlinear embeddings semi-supervised learning. loss function sample :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"","code":"nn_cosine_embedding_loss(margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"margin (float, optional): number \\(-1\\) \\(1\\), \\(0\\) \\(0.5\\) suggested. margin missing, default value \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cosine_embedding_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cosine embedding loss — nn_cosine_embedding_loss","text":"$$   \\mbox{loss}(x, y) =   \\begin{array}{ll} 1 - \\cos(x_1, x_2), & \\mbox{} y = 1 \\\\ \\max(0, \\cos(x_1, x_2) - \\mbox{margin}), & \\mbox{} y = -1 \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"CrossEntropyLoss module — nn_cross_entropy_loss","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"criterion combines nn_log_softmax() nn_nll_loss() one single class. useful training classification problem C classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"","code":"nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C ignore_index (int, optional): Specifies target value ignored contribute input gradient. size_average TRUE, loss averaged non-ignored targets. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"provided, optional argument weight 1D Tensor assigning weight classes. particularly useful unbalanced training set. input expected contain raw, unnormalized scores class. input Tensor size either \\((minibatch, C)\\) \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional case (described later). criterion expects class index range \\([0, C-1]\\) target value 1D tensor size minibatch; ignore_index specified, criterion also accepts class index (index may necessarily class range). loss can described : $$   \\mbox{loss}(x, class) = -\\log\\left(\\frac{\\exp(x[class])}{\\sum_j \\exp(x[j])}\\right) = -x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right) $$ case weight argument specified: $$   \\mbox{loss}(x, class) = weight[class] \\left(-x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right)\\right) $$ losses averaged across observations minibatch. Can also used higher dimension inputs, 2D images, providing input size \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\), \\(K\\) number dimensions, target appropriate shape (see ).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"Input: \\((N, C)\\) C = number classes, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Target: \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Output: scalar. reduction 'none', size target: \\((N)\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_cross_entropy_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CrossEntropyLoss module — nn_cross_entropy_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_cross_entropy_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long()) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"The Connectionist Temporal Classification loss. — nn_ctc_loss","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"Calculates loss continuous (unsegmented) time series target sequence. CTCLoss sums probability possible alignments input target, producing loss value differentiable respect input node. alignment input target assumed \"many--one\", limits length target sequence must \\(\\leq\\) input length.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"","code":"nn_ctc_loss(blank = 0, reduction = \"mean\", zero_infinity = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"blank (int, optional): blank label. Default \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': output losses divided target lengths mean batch taken. Default: 'mean' zero_infinity (bool, optional): Whether zero infinite losses associated gradients. Default: FALSE Infinite losses mainly occur inputs short aligned targets.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"order use CuDNN, following must satisfied: targets must concatenated format, input_lengths must T.  \\(blank=0\\), target_lengths \\(\\leq 256\\), integer arguments must regular implementation uses (common PyTorch) torch_long dtype. dtype torch_int32. circumstances using CUDA backend CuDNN, operator may select nondeterministic algorithm increase performance. undesirable, can try make operation deterministic (potentially performance cost) setting torch.backends.cudnn.deterministic = TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"Log_probs: Tensor size \\((T, N, C)\\), \\(T = \\mbox{input length}\\), \\(N = \\mbox{batch size}\\), \\(C = \\mbox{number classes (including blank)}\\). logarithmized probabilities outputs (e.g. obtained [nnf)log_softmax()]). Targets: Tensor size \\((N, S)\\) \\((\\mbox{sum}(\\mbox{target\\_lengths}))\\), \\(N = \\mbox{batch size}\\) \\(S = \\mbox{max target length, shape } (N, S)\\). represent target sequences. element target sequence class index. target index blank (default=0). \\((N, S)\\) form, targets padded length longest sequence, stacked. \\((\\mbox{sum}(\\mbox{target\\_lengths}))\\) form, targets assumed un-padded concatenated within 1 dimension. Input_lengths: Tuple tensor size \\((N)\\), \\(N = \\mbox{batch size}\\). represent lengths inputs (must \\(\\leq T\\)). lengths specified sequence achieve masking assumption sequences padded equal lengths. Target_lengths: Tuple tensor size \\((N)\\), \\(N = \\mbox{batch size}\\). represent lengths targets. Lengths specified sequence achieve masking assumption sequences padded equal lengths. target shape \\((N,S)\\), target_lengths effectively stop index \\(s_n\\) target sequence, target_n = targets[n,0:s_n] target batch. Lengths must \\(\\leq S\\) targets given 1d tensor concatenation individual targets, target_lengths must add total length tensor. Output: scalar. reduction 'none', \\((N)\\), \\(N = \\mbox{batch size}\\). [nnf)log_softmax()]: R:nnf)log_softmax() [n,0:s_n]: R:n,0:s_n","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":". Graves et al.: Connectionist Temporal Classification: Labelling Unsegmented Sequence Data Recurrent Neural Networks: https://www.cs.toronto.edu/~graves/icml_2006.pdf","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_ctc_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Connectionist Temporal Classification loss. — nn_ctc_loss","text":"","code":"if (torch_is_installed()) { # Target are to be padded T <- 50 # Input sequence length C <- 20 # Number of classes (including blank) N <- 16 # Batch size S <- 30 # Target sequence length of longest target in batch (padding length) S_min <- 10 # Minimum target length, for demonstration purposes  # Initialize random batch of input vectors, for *size = (T,N,C) input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_()  # Initialize random batch of targets (0 = blank, 1:C = classes) target <- torch_randint(low = 1, high = C, size = c(N, S), dtype = torch_long())  input_lengths <- torch_full(size = c(N), fill_value = TRUE, dtype = torch_long()) target_lengths <- torch_randint(low = S_min, high = S, size = c(N), dtype = torch_long()) ctc_loss <- nn_ctc_loss() loss <- ctc_loss(input, target, input_lengths, target_lengths) loss$backward()   # Target are to be un-padded T <- 50 # Input sequence length C <- 20 # Number of classes (including blank) N <- 16 # Batch size  # Initialize random batch of input vectors, for *size = (T,N,C) input <- torch_randn(T, N, C)$log_softmax(2)$detach()$requires_grad_() input_lengths <- torch_full(size = c(N), fill_value = TRUE, dtype = torch_long())  # Initialize random batch of targets (0 = blank, 1:C = classes) target_lengths <- torch_randint(low = 1, high = T, size = c(N), dtype = torch_long()) target <- torch_randint(   low = 1, high = C, size = as.integer(sum(target_lengths)),   dtype = torch_long() ) ctc_loss <- nn_ctc_loss() loss <- ctc_loss(input, target, input_lengths, target_lengths) loss$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout module — nn_dropout","title":"Dropout module — nn_dropout","text":"training, randomly zeroes elements input tensor probability p using samples Bernoulli distribution. channel zeroed independently every forward call.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout module — nn_dropout","text":"","code":"nn_dropout(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout module — nn_dropout","text":"p probability element zeroed. Default: 0.5 inplace set TRUE, operation -place. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout module — nn_dropout","text":"proven effective technique regularization preventing co-adaptation neurons described paper Improving neural networks preventing co-adaptation feature detectors. Furthermore, outputs scaled factor :math:\\frac{1}{1-p} training. means evaluation module simply computes identity function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout module — nn_dropout","text":"Input: \\((*)\\). Input can shape Output: \\((*)\\). Output shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout module — nn_dropout","text":"","code":"if (torch_is_installed()) { m <- nn_dropout(p = 0.2) input <- torch_randn(20, 16) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout2D module — nn_dropout2d","title":"Dropout2D module — nn_dropout2d","text":"Randomly zero entire channels (channel 2D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 2D tensor \\(\\mbox{input}[, j]\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout2D module — nn_dropout2d","text":"","code":"nn_dropout2d(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout2D module — nn_dropout2d","text":"p (float, optional): probability element zero-ed. inplace (bool, optional): set TRUE, operation -place","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout2D module — nn_dropout2d","text":"channel zeroed independently every forward call probability p using samples Bernoulli distribution. Usually input comes nn_conv2d modules. described paper Efficient Object Localization Using Convolutional Networks , adjacent pixels within feature maps strongly correlated (normally case early convolution layers) ..d. dropout regularize activations otherwise just result effective learning rate decrease. case, nn_dropout2d help promote independence feature maps used instead.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout2D module — nn_dropout2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout2D module — nn_dropout2d","text":"","code":"if (torch_is_installed()) { m <- nn_dropout2d(p = 0.2) input <- torch_randn(20, 16, 32, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout3D module — nn_dropout3d","title":"Dropout3D module — nn_dropout3d","text":"Randomly zero entire channels (channel 3D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 3D tensor \\(\\mbox{input}[, j]\\)).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout3D module — nn_dropout3d","text":"","code":"nn_dropout3d(p = 0.5, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout3D module — nn_dropout3d","text":"p (float, optional): probability element zeroed. inplace (bool, optional): set TRUE, operation -place","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dropout3D module — nn_dropout3d","text":"channel zeroed independently every forward call probability p using samples Bernoulli distribution. Usually input comes nn_conv2d modules. described paper Efficient Object Localization Using Convolutional Networks , adjacent pixels within feature maps strongly correlated (normally case early convolution layers) ..d. dropout regularize activations otherwise just result effective learning rate decrease. case, nn_dropout3d help promote independence feature maps used instead.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Dropout3D module — nn_dropout3d","text":"Input: \\((N, C, D, H, W)\\) Output: \\((N, C, D, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_dropout3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dropout3D module — nn_dropout3d","text":"","code":"if (torch_is_installed()) { m <- nn_dropout3d(p = 0.2) input <- torch_randn(20, 16, 4, 32, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"ELU module — nn_elu","title":"ELU module — nn_elu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ELU module — nn_elu","text":"","code":"nn_elu(alpha = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ELU module — nn_elu","text":"alpha \\(\\alpha\\) value ELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ELU module — nn_elu","text":"$$   \\mbox{ELU}(x) = \\max(0,x) + \\min(0, \\alpha * (\\exp(x) - 1)) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ELU module — nn_elu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_elu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ELU module — nn_elu","text":"","code":"if (torch_is_installed()) { m <- nn_elu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding module — nn_embedding","title":"Embedding module — nn_embedding","text":"simple lookup table stores embeddings fixed dictionary size. module often used store word embeddings retrieve using indices. input module list indices, output corresponding word embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding module — nn_embedding","text":"","code":"nn_embedding(   num_embeddings,   embedding_dim,   padding_idx = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   sparse = FALSE,   .weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding module — nn_embedding","text":"num_embeddings (int): size dictionary embeddings embedding_dim (int): size embedding vector padding_idx (int, optional): given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. max_norm (float, optional): given, embedding vector norm larger max_norm renormalized norm max_norm. norm_type (float, optional): p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional): given, scale gradients inverse frequency words mini-batch. Default False. sparse (bool, optional): True, gradient w.r.t. weight matrix sparse tensor. .weight (Tensor) embeddings weights (case want set manually) See Notes details regarding sparse gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Embedding module — nn_embedding","text":"Keep mind limited number optimizers support sparse gradients: currently optim.SGD (CUDA CPU), optim.SparseAdam (CUDA CPU) optim.Adagrad (CPU) padding_idx set, embedding vector padding_idx initialized zeros. However, note vector can modified afterwards, e.g., using customized initialization method, thus changing vector used pad output. gradient vector nn_embedding always zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Embedding module — nn_embedding","text":"weight (Tensor): learnable weights module shape (num_embeddings, embedding_dim) initialized \\(\\mathcal{N}(0, 1)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Embedding module — nn_embedding","text":"Input: \\((*)\\), LongTensor arbitrary shape containing indices extract Output: \\((*, H)\\), * input shape \\(H=\\mbox{embedding\\_dim}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Embedding module — nn_embedding","text":"","code":"if (torch_is_installed()) { # an Embedding module containing 10 tensors of size 3 embedding <- nn_embedding(10, 3) # a batch of 2 samples of 4 indices each input <- torch_tensor(rbind(c(1, 2, 4, 5), c(4, 3, 2, 9)), dtype = torch_long()) embedding(input) # example with padding_idx embedding <- nn_embedding(10, 3, padding_idx = 1) input <- torch_tensor(matrix(c(1, 3, 1, 6), nrow = 1), dtype = torch_long()) embedding(input) } #> torch_tensor #> (1,.,.) =  #>   0.0000  0.0000  0.0000 #>   1.7786  0.6836  0.6713 #>   0.0000  0.0000  0.0000 #>   0.0914 -0.3328 -0.6087 #> [ CPUFloatType{1,4,3} ][ grad_fn = <EmbeddingBackward0> ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding bag module — nn_embedding_bag","title":"Embedding bag module — nn_embedding_bag","text":"Computes sums, means maxes bags embeddings, without instantiating intermediate embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding bag module — nn_embedding_bag","text":"","code":"nn_embedding_bag(   num_embeddings,   embedding_dim,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   mode = \"mean\",   sparse = FALSE,   include_last_offset = FALSE,   padding_idx = NULL,   .weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding bag module — nn_embedding_bag","text":"num_embeddings (int): size dictionary embeddings embedding_dim (int): size embedding vector max_norm (float, optional): given, embedding vector norm larger max_norm renormalized norm max_norm. norm_type (float, optional): p p-norm compute max_norm option. Default 2 scale_grad_by_freq (boolean, optional): given, scale gradients inverse frequency words mini-batch. Default False. mode (string, optional): \"sum\", \"mean\" \"max\". Specifies way reduce bag. \"sum\" computes weighted sum, taking per_sample_weights  consideration. \"mean\" computes average values bag, \"max\" computes max value bag. sparse (bool, optional): True, gradient w.r.t. weight matrix sparse tensor. See Notes details regarding sparse gradients. include_last_offset (bool, optional): True, offsets one additional element, last element equivalent size indices. matches CSR format. padding_idx (int, optional):  given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. .weight (Tensor, optional) embeddings weights (case want set manually)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Embedding bag module — nn_embedding_bag","text":"weight (Tensor): learnable weights module shape (num_embeddings, embedding_dim) initialized \\(\\mathcal{N}(0, 1)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_embedding_bag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Embedding bag module — nn_embedding_bag","text":"","code":"if (torch_is_installed()) { # an EmbeddingBag module containing 10 tensors of size 3 embedding_sum <- nn_embedding_bag(10, 3, mode = 'sum') # a batch of 2 samples of 4 indices each input <- torch_tensor(c(1, 2, 4, 5, 4, 3, 2, 9), dtype = torch_long()) offsets <- torch_tensor(c(0, 4), dtype = torch_long()) embedding_sum(input, offsets) # example with padding_idx embedding_sum <- nn_embedding_bag(10, 3, mode = 'sum', padding_idx = 1) input <- torch_tensor(c(2, 2, 2, 2, 4, 3, 2, 9), dtype = torch_long()) offsets <- torch_tensor(c(0, 4), dtype = torch_long()) embedding_sum(input, offsets) # An EmbeddingBag can be loaded from an Embedding like so embedding <- nn_embedding(10, 3, padding_idx = 2) embedding_sum <- nn_embedding_bag$from_pretrained(embedding$weight,                                                  padding_idx = embedding$padding_idx,                                                  mode='sum') }"},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flattens a contiguous range of dims into a tensor. — nn_flatten","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"use nn_sequential.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"","code":"nn_flatten(start_dim = 2, end_dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"start_dim first dim flatten (default = 2). end_dim last dim flatten (default = -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"Input: (*, S_start,..., S_i, ..., S_end, *), S_i size dimension * means number dimensions including none. Output: (*, S_start*...*S_i*...S_end, *).","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_flatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flattens a contiguous range of dims into a tensor. — nn_flatten","text":"","code":"if (torch_is_installed()) { input <- torch_randn(32, 1, 5, 5) m <- nn_flatten() m(input) } #> torch_tensor #> Columns 1 to 10-0.3199 -0.5470  0.6900 -0.4410  0.7617  0.1433 -0.9380  0.3535 -1.2080  0.1720 #>  0.3672  0.3245  2.4987  0.0329 -0.2421  1.7749 -0.3858  0.0299 -1.5719 -1.0935 #>  0.1906  1.1518 -0.8113  0.7621 -0.2383  0.6764 -1.0068  0.3444 -0.9040  0.3299 #>  0.5682  1.0434 -1.4358 -0.6868  0.1968  0.9005 -0.0376  1.1348 -0.4826 -1.2142 #> -0.1885  1.0115  0.0010  0.3205 -0.9197 -2.8422  0.0036 -1.0949 -0.4103 -2.4228 #> -0.1616  0.9570 -0.9730 -0.5115 -0.7795 -0.6466  1.2477  1.9586  1.5921  0.3300 #> -0.2595  1.1271 -0.5637 -0.2949 -1.3485  0.6355  1.6048  0.0823 -1.3432  1.5985 #> -0.0718 -0.4439  1.4137  0.3199  0.4508 -1.1021 -0.7063  0.1385  0.1935 -0.0515 #>  0.4836 -1.0455 -0.0227  0.0323  2.5342  0.5683  0.6712  1.1591 -1.5967  0.7863 #>  0.0818 -1.7147 -0.4228 -0.6275  0.3657 -1.3283  0.2849  1.2881  0.0971  1.0030 #> -0.1574  0.3463 -0.1375  0.8296 -1.7232 -0.7998 -1.2898  0.7290 -0.3915 -0.0916 #>  1.4269 -1.1700  1.5849  1.6556 -0.2754  2.1463 -0.4575  0.1554  1.1298 -1.4076 #> -0.2696 -0.2911  1.5270  0.0218 -0.1632  0.6899 -0.5760 -0.4658  1.7844 -0.7699 #>  0.9222 -0.6399 -1.0585  1.7109 -0.5417  0.5917 -0.0074  1.2141 -0.0365 -0.6880 #> -0.5276 -0.2794 -1.1153 -1.7366  0.0878  2.1912  0.1281 -0.2582  0.3688 -0.6942 #>  0.1641 -1.8687  0.2645 -1.0567  0.8558  1.7184  0.1966 -0.4893  1.3731 -0.2916 #> -0.2796 -0.0801 -2.6216  1.2877 -0.7383  1.4559 -0.7376 -0.8191  0.9838 -0.2870 #> -0.5289  0.2997  0.3484  0.4236  2.0946 -0.5954 -0.9860  1.9025  1.1765  2.6436 #> -0.6257  0.7267 -1.3255 -1.3947  0.3862  0.4103  0.1376 -0.3802 -1.6978 -0.8255 #>  0.1994  1.1267 -0.5165  0.2751  0.3473 -0.2350 -2.8236  0.4303  0.1155  0.1129 #> -1.1642 -0.2489  0.1635 -1.0925  0.6888 -0.7258 -0.4762 -0.4694 -0.3709 -0.4298 #>  0.7046 -0.1109 -0.0571  0.2755  1.1823 -0.2505  0.4844 -0.0388  0.8161  0.0671 #>  0.1539  1.1621 -0.4768 -0.2405  1.7020  1.4349  1.3076 -0.0111  0.6385  1.2546 #>  0.2940 -0.5328  0.2567 -1.5142 -2.0561 -0.1865 -0.1219  0.9133 -0.2866 -0.7963 #> -1.6937 -0.0177 -0.7141 -0.9364 -1.3136 -0.8536 -0.8504 -0.5033  0.1461  0.9744 #>  0.5276 -0.4393  0.8308  1.0943  0.1384 -0.4463  0.8638  0.4720  0.5617 -0.6370 #>  0.4416 -0.0977 -0.4188  0.0910  0.4841 -1.3073 -0.7666 -0.2519 -1.9687 -0.2757 #>  1.4955 -0.4640  0.4704  0.0696  0.7688  2.1967 -0.0799 -1.0043 -1.7932  0.0867 #> -0.5049 -1.3557  0.7561  0.8408 -0.8754  0.2716  0.0061  0.0291 -0.7523 -1.2831 #> -0.1470 -1.1432 -0.5351 -0.7528 -0.4445 -0.5141  0.7747  1.7050  0.7797 -0.8719 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{32,25} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling Ben Graham","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"","code":"nn_fractional_max_pool2d(   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"kernel_size size window take max . Can single number k (square kernel k x k) tuple (kh, kw) output_size target output size image form oH x oW. Can tuple (oH, oW) single number oH square image oH x oH output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool2d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"max-pooling operation applied \\(kH \\times kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool2d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, and target output size 13x12 m <- nn_fractional_max_pool2d(3, output_size = c(13, 12)) # pool of square window and target output size being half of input image size m <- nn_fractional_max_pool2d(3, output_ratio = c(0.5, 0.5)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling Ben Graham","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"","code":"nn_fractional_max_pool3d(   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"kernel_size size window take max . Can single number k (square kernel k x k x k) tuple (kt x kh x kw) output_size target output size image form oT x oH x oW. Can tuple (oT, oH, oW) single number oH square image oH x oH x oH output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices TRUE, return indices along outputs. Useful pass nn_max_unpool3d(). Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"max-pooling operation applied \\(kTxkHxkW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_fractional_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D fractional max pooling over an input signal composed of several input planes. — nn_fractional_max_pool3d","text":"","code":"if (torch_is_installed()) { # pool of cubic window of size=3, and target output size 13x12x11 m <- nn_fractional_max_pool3d(3, output_size = c(13, 12, 11)) # pool of cubic window and target output size being half of input size m <- nn_fractional_max_pool3d(3, output_ratio = c(0.5, 0.5, 0.5)) input <- torch_randn(20, 16, 50, 32, 16) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"GELU module — nn_gelu","title":"GELU module — nn_gelu","text":"Applies Gaussian Error Linear Units function: $$\\mbox{GELU}(x) = x * \\Phi(x)$$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GELU module — nn_gelu","text":"","code":"nn_gelu(approximate = \"none\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GELU module — nn_gelu","text":"approximate gelu approximation algorithm use: 'none' 'tanh'. Default: 'none'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GELU module — nn_gelu","text":"\\(\\Phi(x)\\) Cumulative Distribution Function Gaussian Distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"GELU module — nn_gelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GELU module — nn_gelu","text":"","code":"if (torch_is_installed()) { m <- nn_gelu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":null,"dir":"Reference","previous_headings":"","what":"GLU module — nn_glu","title":"GLU module — nn_glu","text":"Applies gated linear unit function \\({GLU}(, b)= \\otimes \\sigma(b)\\) \\(\\) first half input matrices \\(b\\) second half.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GLU module — nn_glu","text":"","code":"nn_glu(dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GLU module — nn_glu","text":"dim (int): dimension split input. Default: -1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"GLU module — nn_glu","text":"Input: \\((\\ast_1, N, \\ast_2)\\) * means, number additional dimensions Output: \\((\\ast_1, M, \\ast_2)\\) \\(M=N/2\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_glu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GLU module — nn_glu","text":"","code":"if (torch_is_installed()) { m <- nn_glu() input <- torch_randn(4, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Group normalization — nn_group_norm","title":"Group normalization — nn_group_norm","text":"Applies Group Normalization mini-batch inputs described paper Group Normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group normalization — nn_group_norm","text":"","code":"nn_group_norm(num_groups, num_channels, eps = 1e-05, affine = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group normalization — nn_group_norm","text":"num_groups (int): number groups separate channels num_channels (int): number channels expected input eps value added denominator numerical stability. Default: 1e-5 affine boolean value set TRUE, module learnable per-channel affine parameters initialized ones (weights) zeros (biases). Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Group normalization — nn_group_norm","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ input channels separated num_groups groups, containing num_channels / num_groups channels. mean standard-deviation calculated separately group. \\(\\gamma\\) \\(\\beta\\) learnable per-channel affine transform parameter vectors size num_channels affine TRUE. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Group normalization — nn_group_norm","text":"layer uses statistics computed input data training evaluation modes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Group normalization — nn_group_norm","text":"Input: \\((N, C, *)\\) \\(C=\\mbox{num\\_channels}\\) Output: \\((N, C, *)\\)` (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_group_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group normalization — nn_group_norm","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(20, 6, 10, 10) # Separate 6 channels into 3 groups m <- nn_group_norm(3, 6) # Separate 6 channels into 6 groups (equivalent with [nn_instance_morm]) m <- nn_group_norm(6, 6) # Put all 6 channels into a single group (equivalent with [nn_layer_norm]) m <- nn_group_norm(1, 6) # Activating the module output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"element input sequence, layer computes following function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"","code":"nn_gru(   input_size,   hidden_size,   num_layers = 1,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two GRUs together form stacked GRU, second GRU taking outputs first GRU computing final results. Default: 1 bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs GRU layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional GRU. Default: FALSE ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"$$ \\begin{array}{ll} r_t = \\sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\\\ z_t = \\sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\\\ n_t = \\tanh(W_{} x_t + b_{} + r_t (W_{hn} h_{(t-1)}+ b_{hn})) \\\\ h_t = (1 - z_t) n_t + z_t h_{(t-1)} \\end{array} $$ \\(h_t\\) hidden state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0, \\(r_t\\), \\(z_t\\), \\(n_t\\) reset, update, new gates, respectively. \\(\\sigma\\) sigmoid function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"Inputs: input, h_0 input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() details. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. Defaults zero provided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"Outputs: output, h_n output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features h_t last layer GRU, t. PackedSequence given input, output also packed sequence. unpacked case, directions can separated using output$view(c(seq_len, batch, num_directions, hidden_size)), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len Like output, layers can separated using h_n$view(num_layers, num_directions, batch, hidden_size).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"weight_ih_l[k] : learnable input-hidden weights \\(\\mbox{k}^{th}\\) layer (W_ir|W_iz|W_in), shape (3*hidden_size x input_size) weight_hh_l[k] : learnable hidden-hidden weights \\(\\mbox{k}^{th}\\) layer (W_hr|W_hz|W_hn), shape (3*hidden_size x hidden_size) bias_ih_l[k] : learnable input-hidden bias \\(\\mbox{k}^{th}\\) layer (b_ir|b_iz|b_in), shape (3*hidden_size) bias_hh_l[k] : learnable hidden-hidden bias \\(\\mbox{k}^{th}\\) layer (b_hr|b_hz|b_hn), shape (3*hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_gru.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. — nn_gru","text":"","code":"if (torch_is_installed()) {  rnn <- nn_gru(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) output <- rnn(input, h0) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardshwink module — nn_hardshrink","title":"Hardshwink module — nn_hardshrink","text":"Applies hard shrinkage function element-wise:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardshwink module — nn_hardshrink","text":"","code":"nn_hardshrink(lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardshwink module — nn_hardshrink","text":"lambd \\(\\lambda\\) value Hardshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardshwink module — nn_hardshrink","text":"$$   \\mbox{HardShrink}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x > \\lambda \\\\ x, & \\mbox{ } x < -\\lambda \\\\ 0, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardshwink module — nn_hardshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardshwink module — nn_hardshrink","text":"","code":"if (torch_is_installed()) { m <- nn_hardshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardsigmoid module — nn_hardsigmoid","title":"Hardsigmoid module — nn_hardsigmoid","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardsigmoid module — nn_hardsigmoid","text":"","code":"nn_hardsigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardsigmoid module — nn_hardsigmoid","text":"$$ \\mbox{Hardsigmoid}(x) = \\left\\{ \\begin{array}{ll}   0 & \\mbox{~} x \\le -3, \\\\   1 & \\mbox{~} x \\ge +3, \\\\   x / 6 + 1 / 2 & \\mbox{otherwise} \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardsigmoid module — nn_hardsigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardsigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardsigmoid module — nn_hardsigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_hardsigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardswish module — nn_hardswish","title":"Hardswish module — nn_hardswish","text":"Applies hardswish function, element-wise, described paper: Searching MobileNetV3","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardswish module — nn_hardswish","text":"","code":"nn_hardswish()"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardswish module — nn_hardswish","text":"$$ \\mbox{Hardswish}(x) = \\left\\{   \\begin{array}{ll}   0 & \\mbox{} x \\le -3, \\\\   x & \\mbox{} x \\ge +3, \\\\   x \\cdot (x + 3)/6 & \\mbox{otherwise}   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardswish module — nn_hardswish","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardswish.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardswish module — nn_hardswish","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ m <- nn_hardswish() input <- torch_randn(2) output <- m(input) } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardtanh module — nn_hardtanh","title":"Hardtanh module — nn_hardtanh","text":"Applies HardTanh function element-wise HardTanh defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardtanh module — nn_hardtanh","text":"","code":"nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardtanh module — nn_hardtanh","text":"min_val minimum value linear region range. Default: -1 max_val maximum value linear region range. Default: 1 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardtanh module — nn_hardtanh","text":"$$ \\mbox{HardTanh}(x) = \\left\\{ \\begin{array}{ll}   1 & \\mbox{ } x > 1 \\\\   -1 & \\mbox{ } x < -1 \\\\   x & \\mbox{ otherwise } \\\\ \\end{array} \\right. $$ range linear region :math:[-1, 1] can adjusted using min_val max_val.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hardtanh module — nn_hardtanh","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hardtanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hardtanh module — nn_hardtanh","text":"","code":"if (torch_is_installed()) { m <- nn_hardtanh(-2, 2) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Hinge embedding loss — nn_hinge_embedding_loss","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"Measures loss given input tensor \\(x\\) labels tensor \\(y\\) (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"","code":"nn_hinge_embedding_loss(margin = 1, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"margin (float, optional): default value 1. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"usually used measuring whether two inputs similar dissimilar, e.g. using L1 pairwise distance \\(x\\), typically used learning nonlinear embeddings semi-supervised learning. loss function \\(n\\)-th sample mini-batch $$   l_n = \\begin{array}{ll} x_n, & \\mbox{}\\; y_n = 1,\\\\ \\max \\{0, \\Delta - x_n\\}, & \\mbox{}\\; y_n = -1, \\end{array} $$ total loss functions $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(L = \\{l_1,\\dots,l_N\\}^\\top\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_hinge_embedding_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Hinge embedding loss — nn_hinge_embedding_loss","text":"Input: \\((*)\\) \\(*\\) means, number dimensions. sum operation operates elements. Target: \\((*)\\), shape input Output: scalar. reduction 'none', shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Identity module — nn_identity","title":"Identity module — nn_identity","text":"placeholder identity operator argument-insensitive.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Identity module — nn_identity","text":"","code":"nn_identity(...)"},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Identity module — nn_identity","text":"... arguments (unused)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_identity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Identity module — nn_identity","text":"","code":"if (torch_is_installed()) { m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE) input <- torch_randn(128, 20) output <- m(input) print(output$size()) } #> [1] 128  20"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate gain — nn_init_calculate_gain","title":"Calculate gain — nn_init_calculate_gain","text":"Return recommended gain value given nonlinearity function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate gain — nn_init_calculate_gain","text":"","code":"nn_init_calculate_gain(nonlinearity, param = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_calculate_gain.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate gain — nn_init_calculate_gain","text":"nonlinearity non-linear function param optional parameter non-linear function","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":null,"dir":"Reference","previous_headings":"","what":"Constant initialization — nn_init_constant_","title":"Constant initialization — nn_init_constant_","text":"Fills input Tensor value val.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constant initialization — nn_init_constant_","text":"","code":"nn_init_constant_(tensor, val)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constant initialization — nn_init_constant_","text":"tensor n-dimensional Tensor val value fill tensor ","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_constant_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Constant initialization — nn_init_constant_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_constant_(w, 0.3) } #> torch_tensor #>  0.3000  0.3000  0.3000  0.3000  0.3000 #>  0.3000  0.3000  0.3000  0.3000  0.3000 #>  0.3000  0.3000  0.3000  0.3000  0.3000 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":null,"dir":"Reference","previous_headings":"","what":"Dirac initialization — nn_init_dirac_","title":"Dirac initialization — nn_init_dirac_","text":"Fills {3, 4, 5}-dimensional input Tensor Dirac delta function. Preserves identity inputs Convolutional layers, many input channels preserved possible. case groups>1, group channels preserves identity.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dirac initialization — nn_init_dirac_","text":"","code":"nn_init_dirac_(tensor, groups = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dirac initialization — nn_init_dirac_","text":"tensor {3, 4, 5}-dimensional torch.Tensor groups (optional) number groups conv layer (default: 1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_dirac_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dirac initialization — nn_init_dirac_","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ w <- torch_empty(3, 16, 5, 5) nn_init_dirac_(w) } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":null,"dir":"Reference","previous_headings":"","what":"Eye initialization — nn_init_eye_","title":"Eye initialization — nn_init_eye_","text":"Fills 2-dimensional input Tensor identity matrix. Preserves identity inputs Linear layers, many inputs preserved possible.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eye initialization — nn_init_eye_","text":"","code":"nn_init_eye_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eye initialization — nn_init_eye_","text":"tensor 2-dimensional torch tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_eye_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eye initialization — nn_init_eye_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_eye_(w) } #> torch_tensor #>  1  0  0  0  0 #>  0  1  0  0  0 #>  0  0  1  0  0 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiming normal initialization — nn_init_kaiming_normal_","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"Fills input Tensor values according method described Delving deep rectifiers: Surpassing human-level performance ImageNet classification - , K. et al. (2015), using normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"","code":"nn_init_kaiming_normal_(   tensor,   a = 0,   mode = \"fan_in\",   nonlinearity = \"leaky_relu\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"tensor n-dimensional torch.Tensor negative slope rectifier used layer (used 'leaky_relu') mode either 'fan_in' (default) 'fan_out'. Choosing 'fan_in' preserves magnitude variance weights forward pass. Choosing 'fan_out' preserves magnitudes backwards pass. nonlinearity non-linear function. recommended use 'relu' 'leaky_relu' (default).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaiming normal initialization — nn_init_kaiming_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_kaiming_normal_(w, mode = \"fan_in\", nonlinearity = \"leaky_relu\") } #> torch_tensor #>  1.0786 -1.0334  0.2745  0.0530  1.2171 #>  0.8785  0.2499 -0.2100  0.2391 -0.0418 #>  0.3469 -0.3616  0.1409 -0.8057 -0.3413 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiming uniform initialization — nn_init_kaiming_uniform_","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"Fills input Tensor values according method described Delving deep rectifiers: Surpassing human-level performance ImageNet classification - , K. et al. (2015), using uniform distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"","code":"nn_init_kaiming_uniform_(   tensor,   a = 0,   mode = \"fan_in\",   nonlinearity = \"leaky_relu\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"tensor n-dimensional torch.Tensor negative slope rectifier used layer (used 'leaky_relu') mode either 'fan_in' (default) 'fan_out'. Choosing 'fan_in' preserves magnitude variance weights forward pass. Choosing 'fan_out' preserves magnitudes backwards pass. nonlinearity non-linear function. recommended use 'relu' 'leaky_relu' (default).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_kaiming_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaiming uniform initialization — nn_init_kaiming_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_kaiming_uniform_(w, mode = \"fan_in\", nonlinearity = \"leaky_relu\") } #> torch_tensor #> -0.9239 -1.0055  0.5509 -0.7905 -0.9259 #> -0.0518  1.0741 -0.9186  0.7302 -0.5758 #>  0.4865  0.1697  0.6175  0.7713 -0.2031 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Normal initialization — nn_init_normal_","title":"Normal initialization — nn_init_normal_","text":"Fills input Tensor values drawn normal distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normal initialization — nn_init_normal_","text":"","code":"nn_init_normal_(tensor, mean = 0, std = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normal initialization — nn_init_normal_","text":"tensor n-dimensional Tensor mean mean normal distribution std standard deviation normal distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normal initialization — nn_init_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_normal_(w) } #> torch_tensor #> -0.3168 -1.1078  0.8658 -0.6269  0.5245 #> -0.2218  2.9325  0.1645 -0.6746  1.0336 #> -0.3619 -0.2167 -0.5824  1.8980 -1.6161 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones initialization — nn_init_ones_","title":"Ones initialization — nn_init_ones_","text":"Fills input Tensor scalar value 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones initialization — nn_init_ones_","text":"","code":"nn_init_ones_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones initialization — nn_init_ones_","text":"tensor n-dimensional Tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_ones_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones initialization — nn_init_ones_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_ones_(w) } #> torch_tensor #>  1  1  1  1  1 #>  1  1  1  1  1 #>  1  1  1  1  1 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal initialization — nn_init_orthogonal_","title":"Orthogonal initialization — nn_init_orthogonal_","text":"Fills input Tensor (semi) orthogonal matrix, described Exact solutions nonlinear dynamics learning deep linear neural networks - Saxe, . et al. (2013). input tensor must least 2 dimensions, tensors 2 dimensions trailing dimensions flattened.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal initialization — nn_init_orthogonal_","text":"","code":"nn_init_orthogonal_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal initialization — nn_init_orthogonal_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_orthogonal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal initialization — nn_init_orthogonal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_orthogonal_(w) } #> torch_tensor #>  0.7160  0.3257  0.3196  0.5157 -0.1145 #>  0.0487 -0.4434  0.6289 -0.3018 -0.5607 #> -0.0492 -0.6043  0.3151  0.3915  0.6163 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse initialization — nn_init_sparse_","title":"Sparse initialization — nn_init_sparse_","text":"Fills 2D input Tensor sparse matrix, non-zero elements drawn normal distribution described Deep learning via Hessian-free optimization - Martens, J. (2010).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse initialization — nn_init_sparse_","text":"","code":"nn_init_sparse_(tensor, sparsity, std = 0.01)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse initialization — nn_init_sparse_","text":"tensor n-dimensional Tensor sparsity fraction elements column set zero std standard deviation normal distribution used generate non-zero values","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_sparse_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sparse initialization — nn_init_sparse_","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ w <- torch_empty(3, 5) nn_init_sparse_(w, sparsity = 0.1) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Truncated normal initialization — nn_init_trunc_normal_","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"Fills input Tensor values drawn truncated normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"","code":"nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"tensor n-dimensional Tensor mean mean normal distribution std standard deviation normal distribution minimum cutoff value b maximum cutoff value","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_trunc_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Truncated normal initialization — nn_init_trunc_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_trunc_normal_(w) } #> torch_tensor #>  0.3975  0.9598 -0.1321  0.2450  0.9107 #>  1.0950  1.1163 -1.3789  1.0975 -1.2351 #> -1.2719  0.0179 -0.1226  1.2499  1.3921 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Uniform initialization — nn_init_uniform_","title":"Uniform initialization — nn_init_uniform_","text":"Fills input Tensor values drawn uniform distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uniform initialization — nn_init_uniform_","text":"","code":"nn_init_uniform_(tensor, a = 0, b = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uniform initialization — nn_init_uniform_","text":"tensor n-dimensional Tensor lower bound uniform distribution b upper bound uniform distribution","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uniform initialization — nn_init_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_uniform_(w) } #> torch_tensor #>  0.0434  0.1934  0.2546  0.7549  0.8938 #>  0.9165  0.1504  0.3006  0.9083  0.1952 #>  0.9987  0.8478  0.9906  0.1976  0.7061 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":null,"dir":"Reference","previous_headings":"","what":"Xavier normal initialization — nn_init_xavier_normal_","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"Fills input Tensor values according method described Understanding difficulty training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using normal distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"","code":"nn_init_xavier_normal_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_normal_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xavier normal initialization — nn_init_xavier_normal_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_xavier_normal_(w) } #> torch_tensor #>  1.2405  0.0297  0.1791 -1.1906  0.5827 #>  0.2368 -0.3485  0.4034  0.3764 -0.2217 #>  0.0696  0.0207 -0.4911 -0.1007  0.1210 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":null,"dir":"Reference","previous_headings":"","what":"Xavier uniform initialization — nn_init_xavier_uniform_","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"Fills input Tensor values according method described Understanding difficulty training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using uniform distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"","code":"nn_init_xavier_uniform_(tensor, gain = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"tensor n-dimensional Tensor gain optional scaling factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_xavier_uniform_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Xavier uniform initialization — nn_init_xavier_uniform_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_xavier_uniform_(w) } #> torch_tensor #> -0.0352  0.7292  0.5037  0.6919  0.0880 #> -0.6865 -0.4254  0.0470  0.6638 -0.7051 #>  0.6776 -0.6500 -0.7674 -0.1183  0.8304 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros initialization — nn_init_zeros_","title":"Zeros initialization — nn_init_zeros_","text":"Fills input Tensor scalar value 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros initialization — nn_init_zeros_","text":"","code":"nn_init_zeros_(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros initialization — nn_init_zeros_","text":"tensor n-dimensional tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_init_zeros_.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros initialization — nn_init_zeros_","text":"","code":"if (torch_is_installed()) { w <- torch_empty(3, 5) nn_init_zeros_(w) } #> torch_tensor #>  0  0  0  0  0 #>  0  0  0  0  0 #>  0  0  0  0  0 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Kullback-Leibler divergence loss — nn_kl_div_loss","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"Kullback-Leibler divergence loss measure Kullback-Leibler divergence useful distance measure continuous distributions often useful performing direct regression space (discretely sampled) continuous output distributions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"","code":"nn_kl_div_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'batchmean' | 'sum' | 'mean'. 'none': reduction applied. 'batchmean': sum output divided batchsize. 'sum': output summed. 'mean': output divided number elements output. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"nn_nll_loss(), input given expected contain log-probabilities restricted 2D Tensor. targets interpreted probabilities default, considered log-probabilities log_target set TRUE. criterion expects target Tensor size input Tensor. unreduced (.e. reduction set 'none') loss can described : $$   l(x,y) = L = \\{ l_1,\\dots,l_N \\}, \\quad l_n = y_n \\cdot \\left( \\log y_n - x_n \\right) $$ index \\(N\\) spans dimensions input \\(L\\) shape input. reduction 'none' (default 'mean'), : $$   \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';} \\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ default reduction mode 'mean', losses averaged minibatch observations well dimensions. 'batchmean' mode gives correct KL divergence losses averaged batch dimension . 'mean' mode's behavior changed 'batchmean' next major release.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"reduction = 'mean' return true kl divergence value, please use reduction = 'batchmean' aligns KL math definition. next major release, 'mean' changed 'batchmean'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_kl_div_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Kullback-Leibler divergence loss — nn_kl_div_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar default. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"L1 loss — nn_l1_loss","title":"L1 loss — nn_l1_loss","text":"Creates criterion measures mean absolute error (MAE) element input \\(x\\) target \\(y\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L1 loss — nn_l1_loss","text":"","code":"nn_l1_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L1 loss — nn_l1_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"L1 loss — nn_l1_loss","text":"unreduced (.e. reduction set 'none') loss can described : $$ \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = \\left| x_n - y_n \\right|, $$ \\(N\\) batch size. reduction 'none' (default 'mean'), : $$ \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), & \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  & \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(x\\) \\(y\\) tensors arbitrary shapes total \\(n\\) elements . sum operation still operates elements, divides \\(n\\). division \\(n\\) can avoided one sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"L1 loss — nn_l1_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_l1_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"L1 loss — nn_l1_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_l1_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randn(3, 5) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer normalization — nn_layer_norm","title":"Layer normalization — nn_layer_norm","text":"Applies Layer Normalization mini-batch inputs described paper Layer Normalization","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer normalization — nn_layer_norm","text":"","code":"nn_layer_norm(normalized_shape, eps = 1e-05, elementwise_affine = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer normalization — nn_layer_norm","text":"normalized_shape (int list): input shape expected input size \\([* \\times \\mbox{normalized\\_shape}[0] \\times \\mbox{normalized\\_shape}[1] \\times \\ldots \\times \\mbox{normalized\\_shape}[-1]]\\) single integer used, treated singleton list, module normalize last dimension expected specific size. eps value added denominator numerical stability. Default: 1e-5 elementwise_affine boolean value set TRUE, module learnable per-element affine parameters initialized ones (weights) zeros (biases). Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Layer normalization — nn_layer_norm","text":"$$   y = \\frac{x - \\mathrm{E}[x]}{ \\sqrt{\\mathrm{Var}[x] + \\epsilon}} * \\gamma + \\beta $$ mean standard-deviation calculated separately last certain number dimensions shape specified normalized_shape. \\(\\gamma\\) \\(\\beta\\) learnable affine transform parameters normalized_shape elementwise_affine TRUE. standard-deviation calculated via biased estimator, equivalent torch_var(input, unbiased=FALSE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layer normalization — nn_layer_norm","text":"Unlike Batch Normalization Instance Normalization, applies scalar scale bias entire channel/plane affine option, Layer Normalization applies per-element scale bias elementwise_affine. layer uses statistics computed input data training evaluation modes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Layer normalization — nn_layer_norm","text":"Input: \\((N, *)\\) Output: \\((N, *)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_layer_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer normalization — nn_layer_norm","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(20, 5, 10, 10) # With Learnable Parameters m <- nn_layer_norm(input$size()[-1]) # Without Learnable Parameters m <- nn_layer_norm(input$size()[-1], elementwise_affine = FALSE) # Normalize over last two dimensions m <- nn_layer_norm(c(10, 10)) # Normalize over last dimension of size 10 m <- nn_layer_norm(10) # Activating the module output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"LeakyReLU module — nn_leaky_relu","title":"LeakyReLU module — nn_leaky_relu","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LeakyReLU module — nn_leaky_relu","text":"","code":"nn_leaky_relu(negative_slope = 0.01, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LeakyReLU module — nn_leaky_relu","text":"negative_slope Controls angle negative slope. Default: 1e-2 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LeakyReLU module — nn_leaky_relu","text":"$$   \\mbox{LeakyReLU}(x) = \\max(0, x) + \\mbox{negative\\_slope} * \\min(0, x) $$ $$   \\mbox{LeakyRELU}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x \\geq 0 \\\\ \\mbox{negative\\_slope} \\times x, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LeakyReLU module — nn_leaky_relu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_leaky_relu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LeakyReLU module — nn_leaky_relu","text":"","code":"if (torch_is_installed()) { m <- nn_leaky_relu(0.1) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear module — nn_linear","title":"Linear module — nn_linear","text":"Applies linear transformation incoming data: y = xA^T + b","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear module — nn_linear","text":"","code":"nn_linear(in_features, out_features, bias = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear module — nn_linear","text":"in_features size input sample out_features size output sample bias set FALSE, layer learn additive bias. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Linear module — nn_linear","text":"Input: (N, *, H_in) * means number additional dimensions H_in = in_features. Output: (N, *, H_out) last dimension shape input :math:H_out = out_features.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Linear module — nn_linear","text":"weight: learnable weights module shape (out_features, in_features). values initialized \\(U(-\\sqrt{k}, \\sqrt{k})\\)s, \\(k = \\frac{1}{\\mbox{\\_features}}\\) bias: learnable bias module shape \\((\\mbox{\\_features})\\). bias TRUE, values initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{\\_features}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_linear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear module — nn_linear","text":"","code":"if (torch_is_installed()) { m <- nn_linear(20, 30) input <- torch_randn(128, 20) output <- m(input) print(output$size()) } #> [1] 128  30"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"LogSigmoid module — nn_log_sigmoid","title":"LogSigmoid module — nn_log_sigmoid","text":"Applies element-wise function: $$   \\mbox{LogSigmoid}(x) = \\log\\left(\\frac{ 1 }{ 1 + \\exp(-x)}\\right)  $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LogSigmoid module — nn_log_sigmoid","text":"","code":"nn_log_sigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LogSigmoid module — nn_log_sigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LogSigmoid module — nn_log_sigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_log_sigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"LogSoftmax module — nn_log_softmax","title":"LogSoftmax module — nn_log_softmax","text":"Applies \\(\\log(\\mbox{Softmax}(x))\\) function n-dimensional input Tensor. LogSoftmax formulation can simplified :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LogSoftmax module — nn_log_softmax","text":"","code":"nn_log_softmax(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LogSoftmax module — nn_log_softmax","text":"dim (int): dimension along LogSoftmax computed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LogSoftmax module — nn_log_softmax","text":"Tensor dimension shape input values range [-inf, 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LogSoftmax module — nn_log_softmax","text":"$$   \\mbox{LogSoftmax}(x_{}) = \\log\\left(\\frac{\\exp(x_i) }{ \\sum_j \\exp(x_j)} \\right) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"LogSoftmax module — nn_log_softmax","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_log_softmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LogSoftmax module — nn_log_softmax","text":"","code":"if (torch_is_installed()) { m <- nn_log_softmax(1) input <- torch_randn(2, 3) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"window, function computed :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"","code":"nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"$$   f(X) = \\sqrt[p]{\\sum_{x \\X} x^{p}} $$ p = \\(\\infty\\), one gets Max Pooling p = 1, one gets Sum Pooling (proportional Average Pooling)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"sum power p zero, gradient function defined. implementation set gradient zero case.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor\\frac{L_{} - \\mbox{kernel\\_size}}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 1D power-average pooling over an input signal composed of several input planes. — nn_lp_pool1d","text":"","code":"if (torch_is_installed()) { # power-2 pool of window of length 3, with stride 2. m <- nn_lp_pool1d(2, 3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"window, function computed :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"","code":"nn_lp_pool2d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size size window stride stride window. Default value kernel_size ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"$$   f(X) = \\sqrt[p]{\\sum_{x \\X} x^{p}} $$ p = \\(\\infty\\), one gets Max Pooling p = 1, one gets Sum Pooling (proportional average pooling) parameters kernel_size, stride can either : single int – case value used height width dimension tuple two ints – case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"sum power p zero, gradient function defined. implementation set gradient zero case.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{} - \\mbox{kernel\\_size}[0]}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} - \\mbox{kernel\\_size}[1]}{\\mbox{stride}[1]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lp_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 2D power-average pooling over an input signal composed of several input planes. — nn_lp_pool2d","text":"","code":"if (torch_is_installed()) {  # power-2 pool of square window of size=3, stride=2 m <- nn_lp_pool2d(2, 3, stride = 2) # pool of non-square window of power 1.2 m <- nn_lp_pool2d(1.2, c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"element input sequence, layer computes following function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"","code":"nn_lstm(   input_size,   hidden_size,   num_layers = 1,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two LSTMs together form stacked LSTM, second LSTM taking outputs first LSTM computing final results. Default: 1 bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs LSTM layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional LSTM. Default: FALSE ... currently unused.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"$$ \\begin{array}{ll} \\\\ i_t = \\sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\\\ f_t = \\sigma(W_{} x_t + b_{} + W_{hf} h_{(t-1)} + b_{hf}) \\\\ g_t = \\tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{(t-1)} + b_{hg}) \\\\ o_t = \\sigma(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\\\ c_t = f_t c_{(t-1)} + i_t g_t \\\\ h_t = o_t \\tanh(c_t) \\\\ \\end{array} $$ \\(h_t\\) hidden state time t, \\(c_t\\) cell state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0, \\(i_t\\), \\(f_t\\), \\(g_t\\), \\(o_t\\) input, forget, cell, output gates, respectively. \\(\\sigma\\) sigmoid function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"Inputs: input, (h_0, c_0) input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() nn_utils_rnn_pack_sequence() details. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. c_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial cell state element batch. (h_0, c_0) provided, h_0 c_0 default zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"Outputs: output, (h_n, c_n) output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features (h_t) last layer LSTM, t. torch_nn.utils.rnn.PackedSequence given input, output also packed sequence. unpacked case, directions can separated using output$view(c(seq_len, batch, num_directions, hidden_size)), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len. Like output, layers can separated using h_n$view(c(num_layers, num_directions, batch, hidden_size)) similarly c_n. c_n (num_layers * num_directions, batch, hidden_size): tensor containing cell state t = seq_len","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"weight_ih_l[k] : learnable input-hidden weights \\(\\mbox{k}^{th}\\) layer (W_ii|W_if|W_ig|W_io), shape (4*hidden_size x input_size) weight_hh_l[k] : learnable hidden-hidden weights \\(\\mbox{k}^{th}\\) layer (W_hi|W_hf|W_hg|W_ho), shape (4*hidden_size x hidden_size) bias_ih_l[k] : learnable input-hidden bias \\(\\mbox{k}^{th}\\) layer (b_ii|b_if|b_ig|b_io), shape (4*hidden_size) bias_hh_l[k] : learnable hidden-hidden bias \\(\\mbox{k}^{th}\\) layer (b_hi|b_hf|b_hg|b_ho), shape (4*hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_lstm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. — nn_lstm","text":"","code":"if (torch_is_installed()) { rnn <- nn_lstm(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) c0 <- torch_randn(2, 3, 20) output <- rnn(input, list(h0, c0)) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Margin ranking loss — nn_margin_ranking_loss","title":"Margin ranking loss — nn_margin_ranking_loss","text":"Creates criterion measures loss given inputs \\(x1\\), \\(x2\\), two 1D mini-batch Tensors, label 1D mini-batch tensor \\(y\\) (containing 1 -1). \\(y = 1\\) assumed first input ranked higher (larger value) second input, vice-versa \\(y = -1\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margin ranking loss — nn_margin_ranking_loss","text":"","code":"nn_margin_ranking_loss(margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margin ranking loss — nn_margin_ranking_loss","text":"margin (float, optional): default value \\(0\\). reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Margin ranking loss — nn_margin_ranking_loss","text":"loss function pair samples mini-batch : $$   \\mbox{loss}(x1, x2, y) = \\max(0, -y * (x1 - x2) + \\mbox{margin}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Margin ranking loss — nn_margin_ranking_loss","text":"Input1: \\((N)\\) N batch size. Input2: \\((N)\\), shape Input1. Target: \\((N)\\), shape inputs. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_margin_ranking_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Margin ranking loss — nn_margin_ranking_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_margin_ranking_loss() input1 <- torch_randn(3, requires_grad = TRUE) input2 <- torch_randn(3, requires_grad = TRUE) target <- torch_randn(3)$sign() output <- loss(input1, input2, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxPool1D module — nn_max_pool1d","title":"MaxPool1D module — nn_max_pool1d","text":"Applies 1D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxPool1D module — nn_max_pool1d","text":"","code":"nn_max_pool1d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxPool1D module — nn_max_pool1d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful  nn_max_unpool1d() later. ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MaxPool1D module — nn_max_pool1d","text":"simplest case, output value layer input size \\((N, C, L)\\) output \\((N, C, L_{})\\) can precisely described : $$   (N_i, C_j, k) = \\max_{m=0, \\ldots, \\mbox{kernel\\_size} - 1} input(N_i, C_j, stride \\times k + m) $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link nice visualization dilation .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MaxPool1D module — nn_max_pool1d","text":"Input: \\((N, C, L_{})\\) Output: \\((N, C, L_{})\\), $$   L_{} = \\left\\lfloor \\frac{L_{} + 2 \\times \\mbox{padding} - \\mbox{dilation}     \\times (\\mbox{kernel\\_size} - 1) - 1}{\\mbox{stride}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MaxPool1D module — nn_max_pool1d","text":"","code":"if (torch_is_installed()) { # pool of size=3, stride=2 m <- nn_max_pool1d(3, stride = 2) input <- torch_randn(20, 16, 50) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"MaxPool2D module — nn_max_pool2d","title":"MaxPool2D module — nn_max_pool2d","text":"Applies 2D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MaxPool2D module — nn_max_pool2d","text":"","code":"nn_max_pool2d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MaxPool2D module — nn_max_pool2d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful nn_max_unpool2d() later. ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MaxPool2D module — nn_max_pool2d","text":"simplest case, output value layer input size \\((N, C, H, W)\\), output \\((N, C, H_{}, W_{})\\) kernel_size \\((kH, kW)\\) can precisely described : $$ \\begin{array}{ll} (N_i, C_j, h, w) ={} & \\max_{m=0, \\ldots, kH-1} \\max_{n=0, \\ldots, kW-1} \\\\ & \\mbox{input}(N_i, C_j, \\mbox{stride[0]} \\times h + m,                \\mbox{stride[1]} \\times w + n) \\end{array} $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link nice visualization dilation . parameters kernel_size, stride, padding, dilation can either : single int – case value used height width dimension tuple two ints – case, first int used height dimension, second int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MaxPool2D module — nn_max_pool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 * \\mbox{padding[0]} - \\mbox{dilation[0]}     \\times (\\mbox{kernel\\_size[0]} - 1) - 1}{\\mbox{stride[0]}} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 * \\mbox{padding[1]} - \\mbox{dilation[1]}     \\times (\\mbox{kernel\\_size[1]} - 1) - 1}{\\mbox{stride[1]}} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MaxPool2D module — nn_max_pool2d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_max_pool2d(3, stride = 2) # pool of non-square window m <- nn_max_pool2d(c(3, 2), stride = c(2, 1)) input <- torch_randn(20, 16, 50, 32) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"simplest case, output value layer input size \\((N, C, D, H, W)\\), output \\((N, C, D_{}, H_{}, W_{})\\) kernel_size \\((kD, kH, kW)\\) can precisely described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"","code":"nn_max_pool3d(   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   return_indices = FALSE,   ceil_mode = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"kernel_size size window take max stride stride window. Default value kernel_size padding implicit zero padding added three sides dilation parameter controls stride elements window return_indices TRUE, return max indices along outputs. Useful torch_nn.MaxUnpool3d later ceil_mode TRUE, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"$$ \\begin{array}{ll} \\mbox{}(N_i, C_j, d, h, w) = & \\max_{k=0, \\ldots, kD-1} \\max_{m=0, \\ldots, kH-1} \\max_{n=0, \\ldots, kW-1} \\\\  & \\mbox{input}(N_i, C_j, \\mbox{stride[0]} \\times d + k, \\mbox{stride[1]} \\times h + m, \\mbox{stride[2]} \\times w + n) \\end{array} $$ padding non-zero, input implicitly zero-padded sides padding number points. dilation controls spacing kernel points. harder describe, link_ nice visualization dilation . parameters kernel_size, stride, padding, dilation can either : single int – case value used depth, height width dimension tuple three ints – case, first int used depth dimension, second int height dimension third int width dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = \\left\\lfloor\\frac{D_{} + 2 \\times \\mbox{padding}[0] - \\mbox{dilation}[0] \\times     (\\mbox{kernel\\_size}[0] - 1) - 1}{\\mbox{stride}[0]} + 1\\right\\rfloor $$ $$   H_{} = \\left\\lfloor\\frac{H_{} + 2 \\times \\mbox{padding}[1] - \\mbox{dilation}[1] \\times     (\\mbox{kernel\\_size}[1] - 1) - 1}{\\mbox{stride}[1]} + 1\\right\\rfloor $$ $$   W_{} = \\left\\lfloor\\frac{W_{} + 2 \\times \\mbox{padding}[2] - \\mbox{dilation}[2] \\times     (\\mbox{kernel\\_size}[2] - 1) - 1}{\\mbox{stride}[2]} + 1\\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_pool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Applies a 3D max pooling over an input signal composed of several input planes. — nn_max_pool3d","text":"","code":"if (torch_is_installed()) { # pool of square window of size=3, stride=2 m <- nn_max_pool3d(3, stride = 2) # pool of non-square window m <- nn_max_pool3d(c(3, 2, 2), stride = c(2, 1, 2)) input <- torch_randn(20, 16, 50, 44, 31) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"MaxPool1d fully invertible, since non-maximal values lost. MaxUnpool1d takes input output MaxPool1d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"","code":"nn_max_unpool1d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"MaxPool1d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs Example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"input: input Tensor invert indices: indices given nn_max_pool1d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"Input: \\((N, C, H_{})\\) Output: \\((N, C, H_{})\\), $$   H_{} = (H_{} - 1) \\times \\mbox{stride}[0] - 2 \\times \\mbox{padding}[0] + \\mbox{kernel\\_size}[0] $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool1d. — nn_max_unpool1d","text":"","code":"if (torch_is_installed()) { pool <- nn_max_pool1d(2, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool1d(2, stride = 2)  input <- torch_tensor(array(1:8 / 1, dim = c(1, 1, 8))) out <- pool(input) unpool(out[[1]], out[[2]])  # Example showcasing the use of output_size input <- torch_tensor(array(1:8 / 1, dim = c(1, 1, 8))) out <- pool(input) unpool(out[[1]], out[[2]], output_size = input$size()) unpool(out[[1]], out[[2]]) } #> torch_tensor #> (1,1,.,.) =  #>   0 #>   2 #>   0 #>   4 #>   0 #>   6 #>   0 #>   8 #> [ CPUFloatType{1,1,8,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"MaxPool2d fully invertible, since non-maximal values lost. MaxUnpool2d takes input output MaxPool2d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"","code":"nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"MaxPool2d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs Example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"input: input Tensor invert indices: indices given nn_max_pool2d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"Input: \\((N, C, H_{}, W_{})\\) Output: \\((N, C, H_{}, W_{})\\), $$   H_{} = (H_{} - 1) \\times \\mbox{stride[0]} - 2 \\times \\mbox{padding[0]} + \\mbox{kernel\\_size[0]} $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride[1]} - 2 \\times \\mbox{padding[1]} + \\mbox{kernel\\_size[1]} $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool2d. — nn_max_unpool2d","text":"","code":"if (torch_is_installed()) {  pool <- nn_max_pool2d(2, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool2d(2, stride = 2) input <- torch_randn(1, 1, 4, 4) out <- pool(input) unpool(out[[1]], out[[2]])  # specify a different output size than input size unpool(out[[1]], out[[2]], output_size = c(1, 1, 5, 5)) } #> torch_tensor #> (1,1,.,.) =  #>   2.7033  0.0000  0.0000  0.0000  0.0000 #>   0.0000  0.0000  2.4918  0.0000  0.0000 #>   0.2663  0.0000  1.2793  0.0000  0.0000 #>   0.0000  0.0000  0.0000  0.0000  0.0000 #>   0.0000  0.0000  0.0000  0.0000  0.0000 #> [ CPUFloatType{1,1,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"MaxPool3d fully invertible, since non-maximal values lost. MaxUnpool3d takes input output MaxPool3d including indices maximal values computes partial inverse non-maximal values set zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"","code":"nn_max_unpool3d(kernel_size, stride = NULL, padding = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"kernel_size (int tuple): Size max pooling window. stride (int tuple): Stride max pooling window. set kernel_size default. padding (int tuple): Padding added input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"MaxPool3d can map several input sizes output sizes. Hence, inversion process can get ambiguous. accommodate , can provide needed output size additional argument output_size forward call. See Inputs section .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"input: input Tensor invert indices: indices given nn_max_pool3d() output_size (optional): targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"Input: \\((N, C, D_{}, H_{}, W_{})\\) Output: \\((N, C, D_{}, H_{}, W_{})\\), $$   D_{} = (D_{} - 1) \\times \\mbox{stride[0]} - 2 \\times \\mbox{padding[0]} + \\mbox{kernel\\_size[0]} $$ $$   H_{} = (H_{} - 1) \\times \\mbox{stride[1]} - 2 \\times \\mbox{padding[1]} + \\mbox{kernel\\_size[1]} $$ $$   W_{} = (W_{} - 1) \\times \\mbox{stride[2]} - 2 \\times \\mbox{padding[2]} + \\mbox{kernel\\_size[2]} $$ given output_size call operator","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_max_unpool3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes a partial inverse of MaxPool3d. — nn_max_unpool3d","text":"","code":"if (torch_is_installed()) {  # pool of square window of size=3, stride=2 pool <- nn_max_pool3d(3, stride = 2, return_indices = TRUE) unpool <- nn_max_unpool3d(3, stride = 2) out <- pool(torch_randn(20, 16, 51, 33, 15)) unpooled_output <- unpool(out[[1]], out[[2]]) unpooled_output$size() } #> [1] 20 16 51 33 15"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":null,"dir":"Reference","previous_headings":"","what":"Base class for all neural network modules. — nn_module","title":"Base class for all neural network modules. — nn_module","text":"models also subclass class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Base class for all neural network modules. — nn_module","text":"","code":"nn_module(   classname = NULL,   inherit = nn_Module,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Base class for all neural network modules. — nn_module","text":"classname optional name module inherit optional module inherit ... methods implementation private passed R6::R6Class(). active passed R6::R6Class(). parent_env passed R6::R6Class().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Base class for all neural network modules. — nn_module","text":"Modules can also contain Modules, allowing nest tree structure. can assign submodules regular attributes. expected implement initialize forward create new nn_module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"initialize","dir":"Reference","previous_headings":"","what":"Initialize","title":"Base class for all neural network modules. — nn_module","text":"initialize function called whenever new instance nn_module created. use initialize functions define submodules parameters module. example:   initialize function can number parameters. objects assigned self$ available methods implement. Tensors wrapped nn_parameter() nn_buffer() submodules automatically tracked assigned self$. initialize function optional module defining weights, submodules buffers.","code":"initialize = function(input_size, output_size) {    self$conv1 <- nn_conv2d(input_size, output_size, 5)    self$conv2 <- nn_conv2d(output_size, output_size, 5) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"forward","dir":"Reference","previous_headings":"","what":"Forward","title":"Base class for all neural network modules. — nn_module","text":"forward method called whenever instance nn_module called. usually used implement computation module weights ad submodules defined initialize function. example:   forward function can use self$training attribute make different computations depending wether model training , example implementing dropout module.","code":"forward = function(input) {    input <- self$conv1(input)    input <- nnf_relu(input)    input <- self$conv2(input)    input <- nnf_relu(input)    input  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"cloning","dir":"Reference","previous_headings":"","what":"Cloning","title":"Base class for all neural network modules. — nn_module","text":"finalize cloning module, can define private finalize_deep_clone() method. method called cloned object deep-cloning module, modules, parameters buffers already cloned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Base class for all neural network modules. — nn_module","text":"","code":"if (torch_is_installed()) { model <- nn_module(   initialize = function() {     self$conv1 <- nn_conv2d(1, 20, 5)     self$conv2 <- nn_conv2d(20, 20, 5)   },   forward = function(input) {     input <- self$conv1(input)     input <- nnf_relu(input)     input <- self$conv2(input)     input <- nnf_relu(input)     input   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":null,"dir":"Reference","previous_headings":"","what":"Container that allows named values — nn_module_dict","title":"Container that allows named values — nn_module_dict","text":"Container allows named values","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Container that allows named values — nn_module_dict","text":"","code":"nn_module_dict(dict)"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Container that allows named values — nn_module_dict","text":"dict named list submodules saved module.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_module_dict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Container that allows named values — nn_module_dict","text":"","code":"if (torch_is_installed()) { nn_module <- nn_module(   initialize = function() {     self$dict <- nn_module_dict(list(       l1 = nn_linear(10, 20),       l2 = nn_linear(20, 10)     ))   },   forward = function(x) {     x <- self$dict$l1(x)     self$dict$l2(x)   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Holds submodules in a list. — nn_module_list","title":"Holds submodules in a list. — nn_module_list","text":"nn_module_list can indexed like regular R list, modules contains properly registered, visible nn_module methods.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Holds submodules in a list. — nn_module_list","text":"","code":"nn_module_list(modules = list())"},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Holds submodules in a list. — nn_module_list","text":"modules list modules add","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_module_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Holds submodules in a list. — nn_module_list","text":"","code":"if (torch_is_installed()) {  my_module <- nn_module(   initialize = function() {     self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))   },   forward = function(x) {     for (i in 1:length(self$linears)) {       x <- self$linears[[i]](x)     }     x   } ) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"MSE loss — nn_mse_loss","title":"MSE loss — nn_mse_loss","text":"Creates criterion measures mean squared error (squared L2 norm) element input \\(x\\) target \\(y\\). unreduced (.e. reduction set 'none') loss can described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSE loss — nn_mse_loss","text":"","code":"nn_mse_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSE loss — nn_mse_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSE loss — nn_mse_loss","text":"$$   \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = \\left( x_n - y_n \\right)^2, $$ \\(N\\) batch size. reduction 'none' (default 'mean'), : $$   \\ell(x, y) =   \\begin{array}{ll} \\mbox{mean}(L), &  \\mbox{reduction} = \\mbox{'mean';}\\\\ \\mbox{sum}(L),  &  \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ \\(x\\) \\(y\\) tensors arbitrary shapes total \\(n\\) elements . mean operation still operates elements, divides \\(n\\). division \\(n\\) can avoided one sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MSE loss — nn_mse_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_mse_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSE loss — nn_mse_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_mse_loss() input <- torch_randn(3, 5, requires_grad = TRUE) target <- torch_randn(3, 5) output <- loss(input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi margin loss — nn_multi_margin_loss","title":"Multi margin loss — nn_multi_margin_loss","text":"Creates criterion optimizes multi-class classification hinge loss (margin-based loss) input \\(x\\) (2D mini-batch Tensor) output \\(y\\) (1D tensor target class indices, \\(0 \\leq y \\leq \\mbox{x.size}(1)-1\\)):","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi margin loss — nn_multi_margin_loss","text":"","code":"nn_multi_margin_loss(p = 1, margin = 1, weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi margin loss — nn_multi_margin_loss","text":"p (int, optional): default value \\(1\\). \\(1\\) \\(2\\) supported values. margin (float, optional): default value \\(1\\). weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multi_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi margin loss — nn_multi_margin_loss","text":"mini-batch sample, loss terms 1D input \\(x\\) scalar output \\(y\\) : $$   \\mbox{loss}(x, y) = \\frac{\\sum_i \\max(0, \\mbox{margin} - x[y] + x[]))^p}{\\mbox{x.size}(0)} $$ \\(x \\\\left\\{0, \\; \\cdots , \\; \\mbox{x.size}(0) - 1\\right\\}\\) \\(\\neq y\\). Optionally, can give non-equal weighting classes passing 1D weight tensor constructor. loss function becomes: $$   \\mbox{loss}(x, y) = \\frac{\\sum_i \\max(0, w[y] * (\\mbox{margin} - x[y] + x[]))^p)}{\\mbox{x.size}(0)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":null,"dir":"Reference","previous_headings":"","what":"MultiHead attention — nn_multihead_attention","title":"MultiHead attention — nn_multihead_attention","text":"Allows model jointly attend information different representation subspaces. See reference: Attention Need","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MultiHead attention — nn_multihead_attention","text":"","code":"nn_multihead_attention(   embed_dim,   num_heads,   dropout = 0,   bias = TRUE,   add_bias_kv = FALSE,   add_zero_attn = FALSE,   kdim = NULL,   vdim = NULL,   batch_first = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MultiHead attention — nn_multihead_attention","text":"embed_dim total dimension model. num_heads parallel attention heads. Note embed_dim split across num_heads (.e. head dimension embed_dim %/% num_heads). dropout Dropout layer attn_output_weights. Default: 0.0. bias add bias module parameter. Default: True. add_bias_kv add bias key value sequences dim=0. add_zero_attn add new batch zeros key value sequences dim=1. kdim total number features key. Default: NULL vdim total number features value. Default: NULL. Note: kdim vdim NULL, set embed_dim query, key, value number features. batch_first TRUE input output tensors \\((N,   S, E)\\) instead \\((S, N, E)\\), N batch size, S sequence length, E embedding dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MultiHead attention — nn_multihead_attention","text":"$$ \\mbox{MultiHead}(Q, K, V) = \\mbox{Concat}(head_1,\\dots,head_h)W^O \\mbox{} head_i = \\mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"MultiHead attention — nn_multihead_attention","text":"Inputs: query: \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. (see batch_first argument) key: \\((S, N, E)\\), S source sequence length, N batch size, E embedding dimension. (see batch_first argument) value: \\((S, N, E)\\) S source sequence length, N batch size, E embedding dimension. (see batch_first argument) key_padding_mask: \\((N, S)\\) N batch size, S source sequence length. ByteTensor provided, non-zero positions ignored position zero positions unchanged. BoolTensor provided, positions value True ignored position value False unchanged. attn_mask: 2D mask \\((L, S)\\) L target sequence length, S source sequence length. 3D mask \\((N*num_heads, L, S)\\) N batch size, L target sequence length, S source sequence length. attn_mask ensure position allowed attend unmasked positions. ByteTensor provided, non-zero positions allowed attend zero positions unchanged. BoolTensor provided, positions True allowed attend False values unchanged. FloatTensor provided, added attention weight. Outputs: attn_output: \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. (see  batch_first argument) attn_output_weights: avg_weights TRUE (default), output attention weights averaged attention heads, giving tensor shape \\((N, L, S)\\) N batch size, L target sequence length, S source sequence length. avg_weights FALSE, attention weight tensor output -, shape \\((N, H, L, S)\\), H number attention heads.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multihead_attention.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MultiHead attention — nn_multihead_attention","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ multihead_attn <- nn_multihead_attention(embed_dim, num_heads) out <- multihead_attn(query, key, value) attn_output <- out[[1]] attn_output_weights <- out[[2]] } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel margin loss — nn_multilabel_margin_loss","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"Creates criterion optimizes multi-class multi-classification hinge loss (margin-based loss) input \\(x\\) (2D mini-batch Tensor) output \\(y\\) (2D Tensor target class indices). sample mini-batch:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"","code":"nn_multilabel_margin_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"$$   \\mbox{loss}(x, y) = \\sum_{ij}\\frac{\\max(0, 1 - (x[y[j]] - x[]))}{\\mbox{x.size}(0)} $$ \\(x \\\\left\\{0, \\; \\cdots , \\; \\mbox{x.size}(0) - 1\\right\\}\\), \\ \\(y \\\\left\\{0, \\; \\cdots , \\; \\mbox{y.size}(0) - 1\\right\\}\\), \\ \\(0 \\leq y[j] \\leq \\mbox{x.size}(0)-1\\), \\ \\(\\neq y[j]\\) \\(\\) \\(j\\). \\(y\\) \\(x\\) must size. criterion considers contiguous block non-negative targets starts front. allows different samples variable amounts target classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"Input: \\((C)\\) \\((N, C)\\) N batch size C number classes. Target: \\((C)\\) \\((N, C)\\), label targets padded -1 ensuring shape input. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_margin_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multilabel margin loss — nn_multilabel_margin_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_multilabel_margin_loss() x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1, 4)) # for target y, only consider labels 4 and 1, not after label -1 y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1, 4)) loss(x, y) } #> torch_tensor #> 0.85 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"Creates criterion optimizes multi-label one-versus-loss based max-entropy, input \\(x\\) target \\(y\\) size \\((N, C)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"","code":"nn_multilabel_soft_margin_loss(weight = NULL, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"sample minibatch: $$   loss(x, y) = - \\frac{1}{C} * \\sum_i y[] * \\log((1 + \\exp(-x[]))^{-1}) + (1-y[]) * \\log\\left(\\frac{\\exp(-x[])}{(1 + \\exp(-x[]))}\\right) $$ \\(\\\\left\\{0, \\; \\cdots , \\; \\mbox{x.nElement}() - 1\\right\\}\\), \\(y[] \\\\left\\{0, \\; 1\\right\\}\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_multilabel_soft_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Multi label soft margin loss — nn_multilabel_soft_margin_loss","text":"Input: \\((N, C)\\) N batch size C number classes. Target: \\((N, C)\\), label targets padded -1 ensuring shape input. Output: scalar. reduction 'none', \\((N)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Nll loss — nn_nll_loss","title":"Nll loss — nn_nll_loss","text":"negative log likelihood loss. useful train classification problem C classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nll loss — nn_nll_loss","text":"","code":"nn_nll_loss(weight = NULL, ignore_index = -100, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nll loss — nn_nll_loss","text":"weight (Tensor, optional): manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. ignore_index (int, optional): Specifies target value ignored contribute input gradient. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': weighted mean output taken, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nll loss — nn_nll_loss","text":"provided, optional argument weight 1D Tensor assigning weight classes. particularly useful unbalanced training set. input given forward call expected contain log-probabilities class. input Tensor size either \\((minibatch, C)\\) \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional case (described later). Obtaining log-probabilities neural network easily achieved adding  LogSoftmax  layer last layer network. may use CrossEntropyLoss instead, prefer add extra layer. target loss expects class index range \\([0, C-1]\\) C = number classes; ignore_index specified, loss also accepts class index (index may necessarily class range). unreduced (.e. reduction set 'none') loss can described : $$ \\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_n = - w_{y_n} x_{n,y_n}, \\quad w_{c} = \\mbox{weight}[c] \\cdot \\mbox{1}\\{c \\= \\mbox{ignore\\_index}\\}, $$ \\(x\\) input, \\(y\\) target, \\(w\\) weight, \\(N\\) batch size. reduction 'none' (default 'mean'), $$ \\ell(x, y) = \\begin{array}{ll} \\sum_{n=1}^N \\frac{1}{\\sum_{n=1}^N w_{y_n}} l_n, &   \\mbox{reduction} = \\mbox{'mean';}\\\\ \\sum_{n=1}^N l_n,  &   \\mbox{reduction} = \\mbox{'sum'.} \\end{array} $$ Can also used higher dimension inputs, 2D images, providing input size \\((minibatch, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\), \\(K\\) number dimensions, target appropriate shape (see ). case images, computes NLL loss per-pixel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Nll loss — nn_nll_loss","text":"Input: \\((N, C)\\) C = number classes, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Target: \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. Output: scalar. reduction 'none', size target: \\((N)\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_nll_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nll loss — nn_nll_loss","text":"","code":"if (torch_is_installed()) { m <- nn_log_softmax(dim = 2) loss <- nn_nll_loss() # input is of size N x C = 3 x 5 input <- torch_randn(3, 5, requires_grad = TRUE) # each element in target has to have 0 <= value < C target <- torch_tensor(c(2, 1, 5), dtype = torch_long()) output <- loss(m(input), target) output$backward()  # 2D loss example (used, for example, with image inputs) N <- 5 C <- 4 loss <- nn_nll_loss() # input is of size N x C x height x width data <- torch_randn(N, 16, 10, 10) conv <- nn_conv2d(16, C, c(3, 3)) m <- nn_log_softmax(dim = 1) # each element in target has to have 0 <= value < C target <- torch_empty(N, 8, 8, dtype = torch_long())$random_(1, C) output <- loss(m(conv(data)), target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise distance — nn_pairwise_distance","title":"Pairwise distance — nn_pairwise_distance","text":"Computes batchwise pairwise distance vectors \\(v_1\\), \\(v_2\\) using p-norm:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise distance — nn_pairwise_distance","text":"","code":"nn_pairwise_distance(p = 2, eps = 1e-06, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise distance — nn_pairwise_distance","text":"p (real): norm degree. Default: 2 eps (float, optional): Small value avoid division zero. Default: 1e-6 keepdim (bool, optional): Determines whether keep vector dimension. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pairwise distance — nn_pairwise_distance","text":"$$  \\Vert x \\Vert _p = \\left( \\sum_{=1}^n  \\vert x_i \\vert ^ p \\right) ^ {1/p}. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Pairwise distance — nn_pairwise_distance","text":"Input1: \\((N, D)\\) D = vector dimension Input2: \\((N, D)\\), shape Input1 Output: \\((N)\\). keepdim TRUE, \\((N, 1)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_pairwise_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise distance — nn_pairwise_distance","text":"","code":"if (torch_is_installed()) { pdist <- nn_pairwise_distance(p = 2) input1 <- torch_randn(100, 128) input2 <- torch_randn(100, 128) output <- pdist(input1, input2) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an nn_parameter — nn_parameter","title":"Creates an nn_parameter — nn_parameter","text":"Indicates nn_module x parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an nn_parameter — nn_parameter","text":"","code":"nn_parameter(x, requires_grad = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_parameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an nn_parameter — nn_parameter","text":"x tensor want indicate parameter requires_grad whether parameter  requires_grad = TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson NLL loss — nn_poisson_nll_loss","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"Negative log likelihood loss Poisson distribution target. loss can described :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"","code":"nn_poisson_nll_loss(   log_input = TRUE,   full = FALSE,   eps = 1e-08,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"log_input (bool, optional): TRUE loss computed \\(\\exp(\\mbox{input}) - \\mbox{target}*\\mbox{input}\\), FALSE loss \\(\\mbox{input} - \\mbox{target}*\\log(\\mbox{input}+\\mbox{eps})\\). full (bool, optional): whether compute full loss, . e. add Stirling approximation term \\(\\mbox{target}*\\log(\\mbox{target}) - \\mbox{target} + 0.5 * \\log(2\\pi\\mbox{target})\\). eps (float, optional): Small value avoid evaluation \\(\\log(0)\\) log_input = FALSE. Default: 1e-8 reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"$$ \\mbox{target} \\sim \\mathrm{Poisson}(\\mbox{input}) \\mbox{loss}(\\mbox{input}, \\mbox{target}) = \\mbox{input} - \\mbox{target} * \\log(\\mbox{input}) + \\log(\\mbox{target!}) $$ last term can omitted approximated Stirling formula. approximation used target values 1. targets less equal 1 zeros added loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar default. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_poisson_nll_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Poisson NLL loss — nn_poisson_nll_loss","text":"","code":"if (torch_is_installed()) { loss <- nn_poisson_nll_loss() log_input <- torch_randn(5, 2, requires_grad = TRUE) target <- torch_randn(5, 2) output <- loss(log_input, target) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":null,"dir":"Reference","previous_headings":"","what":"PReLU module — nn_prelu","title":"PReLU module — nn_prelu","text":"Applies element-wise function: $$   \\mbox{PReLU}(x) = \\max(0,x) + * \\min(0,x) $$ $$   \\mbox{PReLU}(x) =   \\left\\{ \\begin{array}{ll} x, & \\mbox{ } x \\geq 0 \\\\ ax, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PReLU module — nn_prelu","text":"","code":"nn_prelu(num_parameters = 1, init = 0.25)"},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PReLU module — nn_prelu","text":"num_parameters (int): number \\(\\) learn. Although takes int input, two values legitimate: 1, number channels input. Default: 1 init (float): initial value \\(\\). Default: 0.25","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PReLU module — nn_prelu","text":"\\(\\) learnable parameter. called without arguments, nn.prelu() uses single parameter \\(\\) across input channels. called nn_prelu(nChannels), separate \\(\\) used input channel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"PReLU module — nn_prelu","text":"weight decay used learning \\(\\) good performance. Channel dim 2nd dim input. input dims < 2, channel dim number channels = 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"PReLU module — nn_prelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"PReLU module — nn_prelu","text":"weight (Tensor): learnable weights shape (num_parameters).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PReLU module — nn_prelu","text":"","code":"if (torch_is_installed()) { m <- nn_prelu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":null,"dir":"Reference","previous_headings":"","what":"Prune top layer(s) of a network — nn_prune_head","title":"Prune top layer(s) of a network — nn_prune_head","text":"Prune head_size last layers nn_module order replace head, order use pruned module sequential embedding module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prune top layer(s) of a network — nn_prune_head","text":"","code":"nn_prune_head(x, head_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prune top layer(s) of a network — nn_prune_head","text":"x nn_network prune head_size number nn_layers prune","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prune top layer(s) of a network — nn_prune_head","text":"nn_sequential network top nn_layer removed","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_prune_head.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prune top layer(s) of a network — nn_prune_head","text":"","code":"if (torch_is_installed()) { if (torch_is_installed()) { x <- nn_sequential(   nn_relu(),   nn_tanh(),   nn_relu6(),   nn_relu(),   nn_linear(2,10),   nn_batch_norm1d(10),   nn_tanh(),   nn_linear(10,3) ) prune <- nn_prune_head(x, 3) prune } } #> An `nn_module` containing 30 parameters. #>  #> ── Modules ───────────────────────────────────────────────────────────────────── #> • 0: <nn_relu> #0 parameters #> • 1: <nn_tanh> #0 parameters #> • 2: <nn_relu6> #0 parameters #> • 3: <nn_relu> #0 parameters #> • 4: <nn_linear> #30 parameters"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"ReLU module — nn_relu","title":"ReLU module — nn_relu","text":"Applies rectified linear unit function element-wise $$\\mbox{ReLU}(x) = (x)^+ = \\max(0, x)$$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReLU module — nn_relu","text":"","code":"nn_relu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ReLU module — nn_relu","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ReLU module — nn_relu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ReLU module — nn_relu","text":"","code":"if (torch_is_installed()) { m <- nn_relu() input <- torch_randn(2) m(input) } #> torch_tensor #> 0.01 * #>  0.0000 #>  2.7849 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"ReLu6 module — nn_relu6","title":"ReLu6 module — nn_relu6","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReLu6 module — nn_relu6","text":"","code":"nn_relu6(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ReLu6 module — nn_relu6","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ReLu6 module — nn_relu6","text":"$$   \\mbox{ReLU6}(x) = \\min(\\max(0,x), 6) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"ReLu6 module — nn_relu6","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_relu6.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ReLu6 module — nn_relu6","text":"","code":"if (torch_is_installed()) { m <- nn_relu6() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":null,"dir":"Reference","previous_headings":"","what":"RNN module — nn_rnn","title":"RNN module — nn_rnn","text":"Applies multi-layer Elman RNN \\(\\tanh\\) \\(\\mbox{ReLU}\\) non-linearity input sequence.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RNN module — nn_rnn","text":"","code":"nn_rnn(   input_size,   hidden_size,   num_layers = 1,   nonlinearity = NULL,   bias = TRUE,   batch_first = FALSE,   dropout = 0,   bidirectional = FALSE,   ... )"},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RNN module — nn_rnn","text":"input_size number expected features input x hidden_size number features hidden state h num_layers Number recurrent layers. E.g., setting num_layers=2 mean stacking two RNNs together form stacked RNN, second RNN taking outputs first RNN computing final results. Default: 1 nonlinearity non-linearity use. Can either 'tanh' 'relu'. Default: 'tanh' bias FALSE, layer use bias weights b_ih b_hh. Default: TRUE batch_first TRUE, input output tensors provided (batch, seq, feature). Default: FALSE dropout non-zero, introduces Dropout layer outputs RNN layer except last layer, dropout probability equal dropout. Default: 0 bidirectional TRUE, becomes bidirectional RNN. Default: FALSE ... arguments can passed super class.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RNN module — nn_rnn","text":"element input sequence, layer computes following function: $$ h_t = \\tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh}) $$ \\(h_t\\) hidden state time t, \\(x_t\\) input time t, \\(h_{(t-1)}\\) hidden state previous layer time t-1 initial hidden state time 0. nonlinearity 'relu', \\(\\mbox{ReLU}\\) used instead \\(\\tanh\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"inputs","dir":"Reference","previous_headings":"","what":"Inputs","title":"RNN module — nn_rnn","text":"input shape (seq_len, batch, input_size): tensor containing features input sequence. input can also packed variable length sequence. h_0 shape (num_layers * num_directions, batch, hidden_size): tensor containing initial hidden state element batch. Defaults zero provided. RNN bidirectional, num_directions 2, else 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"outputs","dir":"Reference","previous_headings":"","what":"Outputs","title":"RNN module — nn_rnn","text":"output shape (seq_len, batch, num_directions * hidden_size): tensor containing output features (h_t) last layer RNN, t.  :class:nn_packed_sequence given input, output also packed sequence. unpacked case, directions can separated using output$view(seq_len, batch, num_directions, hidden_size), forward backward direction 0 1 respectively. Similarly, directions can separated packed case. h_n shape (num_layers * num_directions, batch, hidden_size): tensor containing hidden state t = seq_len. Like output, layers can separated using h_n$view(num_layers, num_directions, batch, hidden_size).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"RNN module — nn_rnn","text":"Input1: \\((L, N, H_{})\\) tensor containing input features \\(H_{}=\\mbox{input\\_size}\\) L represents sequence length. Input2: \\((S, N, H_{})\\) tensor containing initial hidden state element batch. \\(H_{}=\\mbox{hidden\\_size}\\) Defaults zero provided. \\(S=\\mbox{num\\_layers} * \\mbox{num\\_directions}\\) RNN bidirectional, num_directions 2, else 1. Output1: \\((L, N, H_{})\\) \\(H_{}=\\mbox{num\\_directions} * \\mbox{hidden\\_size}\\) Output2: \\((S, N, H_{})\\) tensor containing next hidden state element batch","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"attributes","dir":"Reference","previous_headings":"","what":"Attributes","title":"RNN module — nn_rnn","text":"weight_ih_l[k]: learnable input-hidden weights k-th layer, shape (hidden_size, input_size) k = 0. Otherwise, shape (hidden_size, num_directions * hidden_size) weight_hh_l[k]: learnable hidden-hidden weights k-th layer, shape (hidden_size, hidden_size) bias_ih_l[k]: learnable input-hidden bias k-th layer, shape (hidden_size) bias_hh_l[k]: learnable hidden-hidden bias k-th layer, shape (hidden_size)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RNN module — nn_rnn","text":"weights biases initialized \\(\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})\\) \\(k = \\frac{1}{\\mbox{hidden\\_size}}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rnn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RNN module — nn_rnn","text":"","code":"if (torch_is_installed()) { rnn <- nn_rnn(10, 20, 2) input <- torch_randn(5, 3, 10) h0 <- torch_randn(2, 3, 20) rnn(input, h0) } #> [[1]] #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 9  0.5944 -0.6401 -0.4884  0.2165 -0.4695 -0.5841  0.4003  0.0293  0.1361 #>   0.3141 -0.4003  0.6153  0.1675 -0.1991  0.1980 -0.0638  0.2009  0.3664 #>  -0.3311 -0.2844  0.2501 -0.5501 -0.0930 -0.1435 -0.4153  0.1443  0.3987 #>  #> Columns 10 to 18 -0.8953 -0.3160  0.7690  0.4353 -0.9125  0.1048 -0.3679 -0.2261 -0.7134 #>   0.8789  0.2743  0.3330 -0.9160 -0.2329 -0.4732  0.7987  0.2498 -0.3513 #>  -0.0595 -0.0414  0.2853  0.5087 -0.1204 -0.6069 -0.4815  0.3294  0.2926 #>  #> Columns 19 to 20  0.6519 -0.8058 #>  -0.1518  0.2662 #>   0.5490  0.2870 #>  #> (2,.,.) =  #>  Columns 1 to 9  0.6038  0.5381 -0.2702 -0.2611  0.1526  0.5815  0.5129 -0.1028  0.0631 #>   0.6053  0.2879  0.2676 -0.1108 -0.5120 -0.1488  0.2754 -0.0545  0.2809 #>   0.4840 -0.0389  0.4519  0.0532  0.3017  0.4180  0.0665 -0.3966  0.2519 #>  #> Columns 10 to 18 -0.0173 -0.4471  0.4763  0.6337 -0.3888 -0.1050 -0.5589  0.2354 -0.5651 #>   0.0250 -0.3052 -0.2256 -0.0450 -0.1452  0.3206 -0.0882 -0.1725 -0.1897 #>  -0.2431 -0.5869  0.4712  0.0990 -0.1732  0.0419  0.4712  0.5383 -0.7632 #>  #> Columns 19 to 20 -0.0893 -0.5099 #>   0.4132  0.1224 #>   0.1380 -0.1980 #>  #> (3,.,.) =  #>  Columns 1 to 9  0.1520  0.5244  0.0603 -0.0252  0.1342  0.4345  0.4929 -0.0837  0.2292 #>   0.2401  0.0731 -0.0629 -0.2905 -0.0231  0.3164  0.2400  0.1131  0.3396 #>   0.4581  0.1719  0.1856 -0.4919 -0.2999  0.4888  0.1800 -0.3973 -0.1134 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{5,3,20} ][ grad_fn = <StackBackward0> ] #>  #> [[2]] #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 9  0.7433  0.0132  0.7771 -0.5761 -0.0948 -0.3255  0.7292 -0.4665 -0.1679 #>  -0.2671 -0.0385  0.1691 -0.5765 -0.0204  0.3695  0.0650 -0.3395  0.2541 #>  -0.1996 -0.3506  0.4058 -0.1777  0.5691  0.4692  0.8625  0.7024  0.0454 #>  #> Columns 10 to 18 -0.5392  0.6396  0.7567  0.7827 -0.4873  0.1251 -0.3834 -0.0474  0.0489 #>  -0.1094 -0.2379 -0.7107 -0.1123 -0.3464 -0.3640 -0.1792 -0.3552  0.5378 #>  -0.5916 -0.0385  0.8658  0.1396  0.5103  0.5543  0.5276  0.5941 -0.6288 #>  #> Columns 19 to 20 -0.4282 -0.1346 #>  -0.3171 -0.1174 #>   0.6226 -0.1535 #>  #> (2,.,.) =  #>  Columns 1 to 9  0.4440 -0.2710  0.1350 -0.3705 -0.0772 -0.4557  0.0953  0.2506  0.6398 #>   0.0045  0.3164  0.4202 -0.4949 -0.2211  0.4044 -0.0044 -0.2685  0.6013 #>   0.7687 -0.2897  0.5142 -0.2122 -0.1092  0.0634  0.4387  0.3466  0.2665 #>  #> Columns 10 to 18  0.2733  0.1802 -0.6125 -0.2482  0.4468  0.4122 -0.1445 -0.1921 -0.0106 #>   0.1850 -0.0958 -0.4403 -0.3507  0.6460 -0.3670 -0.1135  0.2356  0.3084 #>  -0.0798 -0.4872 -0.2650  0.1576  0.3333  0.2148 -0.6969  0.5199 -0.4006 #>  #> Columns 19 to 20 -0.2628  0.2681 #>  -0.4175 -0.3963 #>  -0.3259 -0.2066 #> [ CPUFloatType{2,3,20} ][ grad_fn = <StackBackward0> ] #>"},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":null,"dir":"Reference","previous_headings":"","what":"RReLU module — nn_rrelu","title":"RReLU module — nn_rrelu","text":"Applies randomized leaky rectified liner unit function, element-wise, described paper:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RReLU module — nn_rrelu","text":"","code":"nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RReLU module — nn_rrelu","text":"lower lower bound uniform distribution. Default: \\(\\frac{1}{8}\\) upper upper bound uniform distribution. Default: \\(\\frac{1}{3}\\) inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RReLU module — nn_rrelu","text":"Empirical Evaluation Rectified Activations Convolutional Network. function defined : $$ \\mbox{RReLU}(x) = \\left\\{ \\begin{array}{ll} x & \\mbox{} x \\geq 0 \\\\ ax & \\mbox{ otherwise } \\end{array} \\right. $$ \\(\\) randomly sampled uniform distribution \\(\\mathcal{U}(\\mbox{lower}, \\mbox{upper})\\). See: https://arxiv.org/pdf/1505.00853.pdf","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"RReLU module — nn_rrelu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_rrelu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RReLU module — nn_rrelu","text":"","code":"if (torch_is_installed()) { m <- nn_rrelu(0.1, 0.3) input <- torch_randn(2) m(input) } #> torch_tensor #> -0.1454 #> -0.0347 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"SELU module — nn_selu","title":"SELU module — nn_selu","text":"Applied element-wise, :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SELU module — nn_selu","text":"","code":"nn_selu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SELU module — nn_selu","text":"inplace (bool, optional): can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SELU module — nn_selu","text":"$$   \\mbox{SELU}(x) = \\mbox{scale} * (\\max(0,x) + \\min(0, \\alpha * (\\exp(x) - 1))) $$ \\(\\alpha = 1.6732632423543772848170429916717\\) \\(\\mbox{scale} = 1.0507009873554804934193349852946\\). details can found paper Self-Normalizing Neural Networks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"SELU module — nn_selu","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_selu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SELU module — nn_selu","text":"","code":"if (torch_is_installed()) { m <- nn_selu() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"A sequential container — nn_sequential","title":"A sequential container — nn_sequential","text":"sequential container. Modules added order passed constructor. See examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A sequential container — nn_sequential","text":"","code":"nn_sequential(...)"},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A sequential container — nn_sequential","text":"... sequence modules added","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A sequential container — nn_sequential","text":"","code":"if (torch_is_installed()) {  model <- nn_sequential(   nn_conv2d(1, 20, 5),   nn_relu(),   nn_conv2d(20, 64, 5),   nn_relu() ) input <- torch_randn(32, 1, 28, 28) output <- model(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid module — nn_sigmoid","title":"Sigmoid module — nn_sigmoid","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid module — nn_sigmoid","text":"","code":"nn_sigmoid()"},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sigmoid module — nn_sigmoid","text":"$$   \\mbox{Sigmoid}(x) = \\sigma(x) = \\frac{1}{1 + \\exp(-x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Sigmoid module — nn_sigmoid","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid module — nn_sigmoid","text":"","code":"if (torch_is_installed()) { m <- nn_sigmoid() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"Applies Sigmoid Linear Unit (SiLU) function, element-wise. SiLU function also known swish function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"","code":"nn_silu(inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_silu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. The SiLU function is also known as the swish function. — nn_silu","text":"See Gaussian Error Linear Units (GELUs) SiLU (Sigmoid Linear Unit) originally coined, see Sigmoid-Weighted Linear Units Neural Network Function Approximation Reinforcement Learning Swish: Self-Gated Activation Function SiLU experimented later.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth L1 loss — nn_smooth_l1_loss","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"Creates criterion uses squared term absolute element-wise error falls 1 L1 term otherwise. less sensitive outliers MSELoss cases prevents exploding gradients (e.g. see Fast R-CNN paper Ross Girshick). Also known Huber loss:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"","code":"nn_smooth_l1_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"$$   \\mbox{loss}(x, y) = \\frac{1}{n} \\sum_{} z_{} $$ \\(z_{}\\) given : $$   z_{} =   \\begin{array}{ll} 0.5 (x_i - y_i)^2, & \\mbox{} |x_i - y_i| < 1 \\\\ |x_i - y_i| - 0.5, & \\mbox{otherwise } \\end{array} $$ \\(x\\) \\(y\\) arbitrary shapes total \\(n\\) elements sum operation still operates elements, divides \\(n\\). division \\(n\\) can avoided sets reduction = 'sum'.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_smooth_l1_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Smooth L1 loss — nn_smooth_l1_loss","text":"Input: \\((N, *)\\) \\(*\\) means, number additional dimensions Target: \\((N, *)\\), shape input Output: scalar. reduction 'none', \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Soft margin loss — nn_soft_margin_loss","title":"Soft margin loss — nn_soft_margin_loss","text":"Creates criterion optimizes two-class classification logistic loss input tensor \\(x\\) target tensor \\(y\\) (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soft margin loss — nn_soft_margin_loss","text":"","code":"nn_soft_margin_loss(reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soft margin loss — nn_soft_margin_loss","text":"reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Soft margin loss — nn_soft_margin_loss","text":"$$   \\mbox{loss}(x, y) = \\sum_i \\frac{\\log(1 + \\exp(-y[]*x[]))}{\\mbox{x.nelement}()} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_soft_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Soft margin loss — nn_soft_margin_loss","text":"Input: \\((*)\\) \\(*\\) means, number additional dimensions Target: \\((*)\\), shape input Output: scalar. reduction 'none', shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax module — nn_softmax","title":"Softmax module — nn_softmax","text":"Applies Softmax function n-dimensional input Tensor rescaling elements n-dimensional output Tensor lie range [0,1] sum 1. Softmax defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax module — nn_softmax","text":"","code":"nn_softmax(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax module — nn_softmax","text":"dim (int): dimension along Softmax computed (every slice along dim sum 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax module — nn_softmax","text":": Tensor dimension shape input values range [0, 1]","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmax module — nn_softmax","text":"$$   \\mbox{Softmax}(x_{}) = \\frac{\\exp(x_i)}{\\sum_j \\exp(x_j)} $$ input Tensor sparse tensor unspecifed values treated -Inf.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Softmax module — nn_softmax","text":"module work directly NLLLoss, expects Log computed Softmax . Use LogSoftmax instead (faster better numerical properties).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmax module — nn_softmax","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax module — nn_softmax","text":"","code":"if (torch_is_installed()) { m <- nn_softmax(1) input <- torch_randn(2, 3) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax2d module — nn_softmax2d","title":"Softmax2d module — nn_softmax2d","text":"Applies SoftMax features spatial location. given image Channels x Height x Width, apply Softmax location \\((Channels, h_i, w_j)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax2d module — nn_softmax2d","text":"","code":"nn_softmax2d()"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmax2d module — nn_softmax2d","text":"Tensor dimension shape input values range [0, 1]","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmax2d module — nn_softmax2d","text":"Input: \\((N, C, H, W)\\) Output: \\((N, C, H, W)\\) (shape input)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmax2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmax2d module — nn_softmax2d","text":"","code":"if (torch_is_installed()) { m <- nn_softmax2d() input <- torch_randn(2, 3, 12, 13) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmin — nn_softmin","title":"Softmin — nn_softmin","text":"Applies Softmin function n-dimensional input Tensor rescaling elements n-dimensional output Tensor lie range [0, 1] sum 1. Softmin defined :","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmin — nn_softmin","text":"","code":"nn_softmin(dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmin — nn_softmin","text":"dim (int): dimension along Softmin computed (every slice along dim sum 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Softmin — nn_softmin","text":"Tensor dimension shape input, values range [0, 1].","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmin — nn_softmin","text":"$$   \\mbox{Softmin}(x_{}) = \\frac{\\exp(-x_i)}{\\sum_j \\exp(-x_j)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softmin — nn_softmin","text":"Input: \\((*)\\) * means, number additional dimensions Output: \\((*)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softmin — nn_softmin","text":"","code":"if (torch_is_installed()) { m <- nn_softmin(dim = 1) input <- torch_randn(2, 2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus module — nn_softplus","title":"Softplus module — nn_softplus","text":"Applies element-wise function: $$   \\mbox{Softplus}(x) = \\frac{1}{\\beta} * \\log(1 + \\exp(\\beta * x)) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus module — nn_softplus","text":"","code":"nn_softplus(beta = 1, threshold = 20)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus module — nn_softplus","text":"beta \\(\\beta\\) value Softplus formulation. Default: 1 threshold values revert linear function. Default: 20","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softplus module — nn_softplus","text":"SoftPlus smooth approximation ReLU function can used constrain output machine always positive. numerical stability implementation reverts linear function \\(input \\times \\beta > threshold\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softplus module — nn_softplus","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softplus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softplus module — nn_softplus","text":"","code":"if (torch_is_installed()) { m <- nn_softplus() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Softshrink module — nn_softshrink","title":"Softshrink module — nn_softshrink","text":"Applies soft shrinkage function elementwise:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softshrink module — nn_softshrink","text":"","code":"nn_softshrink(lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softshrink module — nn_softshrink","text":"lambd \\(\\lambda\\) (must less zero) value Softshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softshrink module — nn_softshrink","text":"$$   \\mbox{SoftShrinkage}(x) =   \\left\\{ \\begin{array}{ll} x - \\lambda, & \\mbox{ } x > \\lambda \\\\ x + \\lambda, & \\mbox{ } x < -\\lambda \\\\ 0, & \\mbox{ otherwise } \\end{array} \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softshrink module — nn_softshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softshrink module — nn_softshrink","text":"","code":"if (torch_is_installed()) { m <- nn_softshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign module — nn_softsign","title":"Softsign module — nn_softsign","text":"Applies element-wise function: $$   \\mbox{SoftSign}(x) = \\frac{x}{ 1 + |x|} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign module — nn_softsign","text":"","code":"nn_softsign()"},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Softsign module — nn_softsign","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_softsign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Softsign module — nn_softsign","text":"","code":"if (torch_is_installed()) { m <- nn_softsign() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanh module — nn_tanh","title":"Tanh module — nn_tanh","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanh module — nn_tanh","text":"","code":"nn_tanh()"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tanh module — nn_tanh","text":"$$   \\mbox{Tanh}(x) = \\tanh(x) = \\frac{\\exp(x) - \\exp(-x)} {\\exp(x) + \\exp(-x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Tanh module — nn_tanh","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanh module — nn_tanh","text":"","code":"if (torch_is_installed()) { m <- nn_tanh() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanhshrink module — nn_tanhshrink","title":"Tanhshrink module — nn_tanhshrink","text":"Applies element-wise function:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanhshrink module — nn_tanhshrink","text":"","code":"nn_tanhshrink()"},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tanhshrink module — nn_tanhshrink","text":"$$   \\mbox{Tanhshrink}(x) = x - \\tanh(x) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Tanhshrink module — nn_tanhshrink","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_tanhshrink.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanhshrink module — nn_tanhshrink","text":"","code":"if (torch_is_installed()) { m <- nn_tanhshrink() input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold module — nn_threshold","title":"Threshold module — nn_threshold","text":"Thresholds element input Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold module — nn_threshold","text":"","code":"nn_threshold(threshold, value, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold module — nn_threshold","text":"threshold value threshold value value replace inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Threshold module — nn_threshold","text":"Threshold defined : $$   y =   \\left\\{ \\begin{array}{ll}   x, &\\mbox{ } x > \\mbox{threshold} \\\\   \\mbox{value}, &\\mbox{ otherwise }   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Threshold module — nn_threshold","text":"Input: \\((N, *)\\) * means, number additional dimensions Output: \\((N, *)\\), shape input","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Threshold module — nn_threshold","text":"","code":"if (torch_is_installed()) { m <- nn_threshold(0.1, 20) input <- torch_randn(2) output <- m(input) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin loss — nn_triplet_margin_loss","title":"Triplet margin loss — nn_triplet_margin_loss","text":"Creates criterion measures triplet loss given input tensors \\(x1\\), \\(x2\\), \\(x3\\) margin value greater \\(0\\). used measuring relative similarity samples. triplet composed , p n (.e., anchor, positive examples negative examples respectively). shapes input tensors \\((N, D)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin loss — nn_triplet_margin_loss","text":"","code":"nn_triplet_margin_loss(   margin = 1,   p = 2,   eps = 1e-06,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin loss — nn_triplet_margin_loss","text":"margin (float, optional): Default: \\(1\\). p (int, optional): norm degree pairwise distance. Default: \\(2\\). eps constant avoid NaN's swap (bool, optional): distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional): Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triplet margin loss — nn_triplet_margin_loss","text":"distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. loss function sample mini-batch : $$   L(, p, n) = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\} $$ $$   d(x_i, y_i) = | {\\bf x}_i - {\\bf y}_i |_p $$ See also nn_triplet_margin_with_distance_loss(), computes triplet margin loss input tensors using custom distance function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Triplet margin loss — nn_triplet_margin_loss","text":"Input: \\((N, D)\\) \\(D\\) vector dimension. Output: Tensor shape \\((N)\\) reduction 'none', scalar otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triplet margin loss — nn_triplet_margin_loss","text":"","code":"if (torch_is_installed()) { triplet_loss <- nn_triplet_margin_loss(margin = 1, p = 2) anchor <- torch_randn(100, 128, requires_grad = TRUE) positive <- torch_randn(100, 128, requires_grad = TRUE) negative <- torch_randn(100, 128, requires_grad = TRUE) output <- triplet_loss(anchor, positive, negative) output$backward() }"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"Creates criterion measures triplet loss given input tensors \\(\\), \\(p\\), \\(n\\) (representing anchor, positive, negative examples, respectively), nonnegative, real-valued function (\"distance function\") used compute relationship anchor positive example (\"positive distance\") anchor negative example (\"negative distance\").","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"","code":"nn_triplet_margin_with_distance_loss(   distance_function = NULL,   margin = 1,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"distance_function (callable, optional): nonnegative, real-valued function quantifies closeness two tensors. specified, nn_pairwise_distance() used.  Default: None margin (float, optional): non-negative margin representing minimum difference positive negative distances required loss 0. Larger margins penalize cases negative examples distant enough anchors, relative positives. Default: \\(1\\). swap (bool, optional): Whether use distance swap described paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. TRUE, positive example closer negative example anchor , swaps positive example anchor loss computation. Default: FALSE. reduction (string, optional): Specifies (optional) reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"unreduced loss (.e., reduction set 'none') can described : $$   \\ell(, p, n) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad l_i = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\} $$ \\(N\\) batch size; \\(d\\) nonnegative, real-valued function quantifying closeness two tensors, referred distance_function; \\(margin\\) non-negative margin representing minimum difference positive negative distances required loss 0.  input tensors \\(N\\) elements can shape distance function can handle. reduction 'none' (default 'mean'), : $$ \\ell(x, y) = \\begin{array}{ll} \\mbox{mean}(L), &  \\mbox{reduction} = \\mbox{`mean';}\\\\             \\mbox{sum}(L),  &  \\mbox{reduction} = \\mbox{`sum'.} \\end{array} $$ See also nn_triplet_margin_loss(), computes triplet loss input tensors using \\(l_p\\) distance distance function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"shape","dir":"Reference","previous_headings":"","what":"Shape","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"Input: \\((N, *)\\) \\(*\\) represents number additional dimensions supported distance function. Output: Tensor shape \\((N)\\) reduction 'none', scalar otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_triplet_margin_with_distance_loss.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triplet margin with distance loss — nn_triplet_margin_with_distance_loss","text":"","code":"if (torch_is_installed()) { # Initialize embeddings embedding <- nn_embedding(1000, 128) anchor_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) positive_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) negative_ids <- torch_randint(1, 1000, 1, dtype = torch_long()) anchor <- embedding(anchor_ids) positive <- embedding(positive_ids) negative <- embedding(negative_ids)  # Built-in Distance Function triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = nn_pairwise_distance() ) output <- triplet_loss(anchor, positive, negative)  # Custom Distance Function l_infinity <- function(x1, x2) {   torch_max(torch_abs(x1 - x2), dim = 1)[[1]] }  triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = l_infinity, margin = 1.5 ) output <- triplet_loss(anchor, positive, negative)  # Custom Distance Function (Lambda) triplet_loss <- nn_triplet_margin_with_distance_loss(   distance_function = function(x, y) {     1 - nnf_cosine_similarity(x, y)   } )  output <- triplet_loss(anchor, positive, negative) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"Unflattens tensor dim expanding desired shape. use [nn_sequential.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"","code":"nn_unflatten(dim, unflattened_size)"},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"dim Dimension unflattened unflattened_size New shape unflattened dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_unflatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unflattens a tensor dim expanding it to a desired shape. For use with [nn_sequential. — nn_unflatten","text":"","code":"if (torch_is_installed()) { input <- torch_randn(2, 50)  m <- nn_sequential(   nn_linear(50, 50),   nn_unflatten(2, c(2, 5, 5)) ) output <- m(input) output$size() } #> [1] 2 2 5 5"},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample module — nn_upsample","title":"Upsample module — nn_upsample","text":"Upsamples given multi-channel 1D (temporal), 2D (spatial) 3D (volumetric) data. input data assumed form minibatch x channels x optional depth x optional height] x width. Hence, spatial inputs, expect 4D Tensor volumetric inputs, expect 5D Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample module — nn_upsample","text":"","code":"nn_upsample(   size = NULL,   scale_factor = NULL,   mode = \"nearest\",   align_corners = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample module — nn_upsample","text":"size (int Tuple[int] Tuple[int, int] Tuple[int, int, int], optional): output spatial sizes scale_factor (float Tuple[float] Tuple[float, float] Tuple[float, float, float], optional): multiplier spatial size. match input size tuple. mode (str, optional): upsampling algorithm: one 'nearest', 'linear', 'bilinear', 'bicubic' 'trilinear'. Default: 'nearest' align_corners (bool, optional): TRUE, corner pixels input output tensors aligned, thus preserving values pixels. effect mode 'linear', 'bilinear', 'trilinear'. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Upsample module — nn_upsample","text":"algorithms available upsampling nearest neighbor linear, bilinear, bicubic trilinear 3D, 4D 5D input Tensor, respectively. One can either give scale_factor target output size calculate output size. (give , ambiguous)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_upsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample module — nn_upsample","text":"","code":"if (torch_is_installed()) { input <- torch_arange(start = 1, end = 4, dtype = torch_float())$view(c(1, 1, 2, 2)) nn_upsample(scale_factor = c(2), mode = \"nearest\")(input) nn_upsample(scale_factor = c(2, 2), mode = \"nearest\")(input) } #> torch_tensor #> (1,1,.,.) =  #>   1  1  2  2 #>   1  1  2  2 #>   3  3  4  4 #>   3  3  4  4 #> [ CPUFloatType{1,1,4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":null,"dir":"Reference","previous_headings":"","what":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"norm computed gradients together, concatenated single vector. Gradients modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"","code":"nn_utils_clip_grad_norm_(parameters, max_norm, norm_type = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"parameters (IterableTensor Tensor): iterable Tensors single Tensor gradients normalized max_norm (float int): max norm gradients norm_type (float int): type used p-norm. Can Inf infinity norm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_norm_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clips gradient norm of an iterable of parameters. — nn_utils_clip_grad_norm_","text":"Total norm parameters (viewed single vector).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":null,"dir":"Reference","previous_headings":"","what":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"Gradients modified -place.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"","code":"nn_utils_clip_grad_value_(parameters, clip_value)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"parameters (Iterable(Tensor) Tensor): iterable Tensors single Tensor gradients normalized clip_value (float int): maximum allowed value gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_clip_grad_value_.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Clips gradient of an iterable of parameters at specified value. — nn_utils_clip_grad_value_","text":"gradients clipped range \\(\\left[\\mbox{-clip\\_value}, \\mbox{clip\\_value}\\right]\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"input can size T x B x * T length longest sequence (equal lengths[1]), B batch size, * number dimensions (including 0). batch_first TRUE, B x T x * input expected.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"","code":"nn_utils_rnn_pack_padded_sequence(   input,   lengths,   batch_first = FALSE,   enforce_sorted = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"input (Tensor): padded batch variable length sequences. lengths (Tensor): list sequences lengths batch element. batch_first (bool, optional): TRUE, input expected B x T x * format. enforce_sorted (bool, optional): TRUE, input expected contain sequences sorted length decreasing order. FALSE, input get sorted unconditionally. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"PackedSequence object","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"unsorted sequences, use enforce_sorted = FALSE. enforce_sorted TRUE, sequences sorted length decreasing order, .e. input[,1] longest sequence, input[,B] shortest one. enforce_sorted = TRUE necessary ONNX export.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_padded_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence","text":"function accepts input least two dimensions. can apply pack labels, use output RNN compute loss directly. Tensor can retrieved PackedSequence object accessing .data attribute.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"sequences list Tensors size L x *, L length sequence * number trailing dimensions, including zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"","code":"nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"sequences (list[Tensor]): list sequences decreasing length. enforce_sorted (bool, optional): TRUE, checks input contains sequences sorted length decreasing order. FALSE, condition checked. Default: TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"PackedSequence object","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"unsorted sequences, use enforce_sorted = FALSE. enforce_sorted TRUE, sequences sorted order decreasing length. enforce_sorted = TRUE necessary ONNX export.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pack_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(c(1, 2, 3), dtype = torch_long()) y <- torch_tensor(c(4, 5), dtype = torch_long()) z <- torch_tensor(c(6), dtype = torch_long())  p <- nn_utils_rnn_pack_sequence(list(x, y, z)) }"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"inverse operation nn_utils_rnn_pack_padded_sequence().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"","code":"nn_utils_rnn_pad_packed_sequence(   sequence,   batch_first = FALSE,   padding_value = 0,   total_length = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"sequence (PackedSequence): batch pad batch_first (bool, optional): True, output “B x T x *` format. padding_value (float, optional): values padded elements. total_length (int, optional): NULL, output padded length total_length. method throw ValueError total_length less max sequence length sequence.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"Tuple Tensor containing padded sequence, Tensor containing list lengths sequence batch. Batch elements re-ordered ordered originally batch passed nn_utils_rnn_pack_padded_sequence() nn_utils_rnn_pack_sequence().","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"returned Tensor's data size T x B x *, T length longest sequence B batch size. batch_first TRUE, data transposed B x T x * format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"total_length useful implement pack sequence -> recurrent network -> unpack sequence pattern nn_module wrapped ~torch.nn.DataParallel.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_packed_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence","text":"","code":"if (torch_is_installed()) { seq <- torch_tensor(rbind(c(1, 2, 0), c(3, 0, 0), c(4, 5, 6))) lens <- c(2, 1, 3) packed <- nn_utils_rnn_pack_padded_sequence(seq, lens,   batch_first = TRUE,   enforce_sorted = FALSE ) packed nn_utils_rnn_pad_packed_sequence(packed, batch_first = TRUE) } #> [[1]] #> torch_tensor #>  1  2  0 #>  3  0  0 #>  4  5  6 #> [ CPUFloatType{3,3} ] #>  #> [[2]] #> torch_tensor #>  2 #>  1 #>  3 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"pad_sequence stacks list Tensors along new dimension, pads equal length. example, input list sequences size L x * batch_first False, T x B x * otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"","code":"nn_utils_rnn_pad_sequence(sequences, batch_first = FALSE, padding_value = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"sequences (list[Tensor]): list variable length sequences. batch_first (bool, optional): output B x T x * TRUE, T x B x * otherwise padding_value (float, optional): value padded elements. Default: 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"Tensor size T x B x * batch_first FALSE. Tensor size B x T x * otherwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"B batch size. equal number elements sequences. T length longest sequence. L length sequence. * number trailing dimensions, including none.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"function returns Tensor size T x B x * B x T x * T length longest sequence. function assumes trailing dimensions type Tensors sequences .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_rnn_pad_sequence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pad a list of variable length Tensors with padding_value — nn_utils_rnn_pad_sequence","text":"","code":"if (torch_is_installed()) { a <- torch_ones(25, 300) b <- torch_ones(22, 300) c <- torch_ones(15, 300) nn_utils_rnn_pad_sequence(list(a, b, c))$size() } #> [1]  25   3 300"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"nn_utils_weight_norm — nn_utils_weight_norm","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"Applies weight normalization parameter given module.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"original module weight_v weight_g paramters.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"Weight normalization reparameterization decouples magnitude weight tensor direction. replaces parameter specified name  (e.g. 'weight') two parameters: one specifying magnitude (e.g. 'weight_g') one specifying direction (e.g. 'weight_v').","code":"\\eqn{\\mathbf{w} = g \\dfrac{\\mathbf{v}}{\\|\\mathbf{v}\\|}}"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"pytorch Weight normalization implemented via hook recomputes weight tensor magnitude direction every forward() call. Since torch R still support hooks, weight recomputation need done explicitly inside forward() definition trough call recompute() method. See examples. default, dim = 0, norm computed independently per output channel/plane. compute norm entire weight tensor, use dim = NULL. @references https://arxiv.org/abs/1602.07868","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"nn_utils_weight_norm$new() nn_utils_weight_norm$compute_weight() nn_utils_weight_norm$apply() nn_utils_weight_norm$call() nn_utils_weight_norm$recompute() nn_utils_weight_norm$remove() nn_utils_weight_norm$clone()","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$new(name, dim)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"name (str, optional): name weight parameter dim (int, optional): dimension compute norm","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$compute_weight(module, name = NULL, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module name (str, optional): name weight parameter dim (int, optional): dimension compute norm","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$apply(module, name = NULL, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module name (str, optional): name weight parameter dim (int, optional): dimension compute norm","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$call(module)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$recompute(module)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$remove(module, name = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"module (Module): containing module name (str, optional): name weight parameter","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"objects class cloneable method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"nn_utils_weight_norm$clone(deep = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"deep Whether make deep clone.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nn_utils_weight_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nn_utils_weight_norm — nn_utils_weight_norm","text":"","code":"if (torch_is_installed()) { x = nn_linear(in_features = 20, out_features = 40) weight_norm = nn_utils_weight_norm$new(name = 'weight', dim = 2) weight_norm$apply(x) x$weight_g$size() x$weight_v$size() x$weight  # the recompute() method recomputes the weight using g and v. It must be called # explicitly inside `forward()`. weight_norm$recompute(x)  }"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"Applies 1D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"","code":"nnf_adaptive_avg_pool1d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool1d — nnf_adaptive_avg_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) output_size target output size (single integer)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"Applies 2D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"","code":"nnf_adaptive_avg_pool2d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool2d — nnf_adaptive_avg_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) output_size target output size (single integer double-integer tuple)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"Applies 3D adaptive average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"","code":"nnf_adaptive_avg_pool3d(input, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool3d — nnf_adaptive_avg_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) output_size target output size (single integer triple-integer tuple)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"Applies 1D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"","code":"nnf_adaptive_max_pool1d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool1d — nnf_adaptive_max_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) output_size target output size (single integer) return_indices whether return pooling indices. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"Applies 2D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"","code":"nnf_adaptive_max_pool2d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool2d — nnf_adaptive_max_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) output_size target output size (single integer double-integer tuple) return_indices whether return pooling indices. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"Applies 3D adaptive max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"","code":"nnf_adaptive_max_pool3d(input, output_size, return_indices = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_adaptive_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_max_pool3d — nnf_adaptive_max_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) output_size target output size (single integer triple-integer tuple) return_indices whether return pooling indices. Default:FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Affine_grid — nnf_affine_grid","title":"Affine_grid — nnf_affine_grid","text":"Generates 2D 3D flow field (sampling grid), given batch affine matrices theta.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Affine_grid — nnf_affine_grid","text":"","code":"nnf_affine_grid(theta, size, align_corners = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Affine_grid — nnf_affine_grid","text":"theta (Tensor) input batch affine matrices shape (\\(N \\times 2 \\times 3\\)) 2D  (\\(N \\times 3 \\times 4\\)) 3D size (torch.Size) target output image size. (\\(N \\times C \\times H \\times W\\) 2D \\(N \\times C \\times D \\times H \\times W\\) 3D) Example: torch.Size((32, 3, 24, 24)) align_corners (bool, optional) True, consider -1 1 refer centers corner pixels rather image corners. Refer nnf_grid_sample() complete description. grid generated nnf_affine_grid() passed nnf_grid_sample()  setting option. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_affine_grid.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Affine_grid — nnf_affine_grid","text":"function often used conjunction nnf_grid_sample() build Spatial Transformer Networks_ .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Alpha_dropout — nnf_alpha_dropout","title":"Alpha_dropout — nnf_alpha_dropout","text":"Applies alpha dropout input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Alpha_dropout — nnf_alpha_dropout","text":"","code":"nnf_alpha_dropout(input, p = 0.5, training = FALSE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_alpha_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Alpha_dropout — nnf_alpha_dropout","text":"input input tensor p probability element zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool1d — nnf_avg_pool1d","title":"Avg_pool1d — nnf_avg_pool1d","text":"Applies 1D average pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool1d — nnf_avg_pool1d","text":"","code":"nnf_avg_pool1d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool1d — nnf_avg_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) kernel_size size window. Can single number tuple (kW,). stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 ceil_mode True, use ceil instead floor compute output shape. Default: FALSE count_include_pad True, include zero-padding averaging calculation. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool2d — nnf_avg_pool2d","title":"Avg_pool2d — nnf_avg_pool2d","text":"Applies 2D average-pooling operation \\(kH * kW\\) regions step size \\(sH * sW\\) steps. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool2d — nnf_avg_pool2d","text":"","code":"nnf_avg_pool2d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool2d — nnf_avg_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) kernel_size size pooling region. Can single number tuple (kH, kW) stride stride pooling operation. Can single number tuple (sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padH, padW). Default: 0 ceil_mode True, use ceil instead floor formula compute output shape. Default: FALSE count_include_pad True, include zero-padding averaging calculation. Default: TRUE divisor_override specified, used divisor, otherwise size pooling region used. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool3d — nnf_avg_pool3d","title":"Avg_pool3d — nnf_avg_pool3d","text":"Applies 3D average-pooling operation \\(kT * kH * kW\\) regions step size \\(sT * sH * sW\\) steps. number output features equal \\(\\lfloor \\frac{ \\mbox{input planes} }{sT} \\rfloor\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool3d — nnf_avg_pool3d","text":"","code":"nnf_avg_pool3d(   input,   kernel_size,   stride = NULL,   padding = 0,   ceil_mode = FALSE,   count_include_pad = TRUE,   divisor_override = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_avg_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool3d — nnf_avg_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) kernel_size size pooling region. Can single number tuple (kT, kH, kW) stride stride pooling operation. Can single number tuple (sT, sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padT, padH, padW), Default: 0 ceil_mode True, use ceil instead floor formula compute output shape count_include_pad True, include zero-padding averaging calculation divisor_override NA specified, used divisor, otherwise size pooling region used. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch_norm — nnf_batch_norm","title":"Batch_norm — nnf_batch_norm","text":"Applies Batch Normalization channel across batch data.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch_norm — nnf_batch_norm","text":"","code":"nnf_batch_norm(   input,   running_mean,   running_var,   weight = NULL,   bias = NULL,   training = FALSE,   momentum = 0.1,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_batch_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch_norm — nnf_batch_norm","text":"input input tensor running_mean running_mean tensor running_var running_var tensor weight weight tensor bias bias tensor training bool wether training. Default: FALSE momentum value used running_mean running_var computation. Can set None cumulative moving average (.e. simple average). Default: 0.1 eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":null,"dir":"Reference","previous_headings":"","what":"Bilinear — nnf_bilinear","title":"Bilinear — nnf_bilinear","text":"Applies bilinear transformation incoming data: \\(y = x_1 x_2 + b\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bilinear — nnf_bilinear","text":"","code":"nnf_bilinear(input1, input2, weight, bias = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bilinear — nnf_bilinear","text":"input1 \\((N, *, H_{in1})\\) \\(H_{in1}=\\mbox{in1\\_features}\\) \\(*\\) means number additional dimensions. last dimension inputs . input2 \\((N, *, H_{in2})\\) \\(H_{in2}=\\mbox{in2\\_features}\\) weight \\((\\mbox{\\_features}, \\mbox{in1\\_features}, \\mbox{in2\\_features})\\) bias \\((\\mbox{\\_features})\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_bilinear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bilinear — nnf_bilinear","text":"output \\((N, *, H_{})\\) \\(H_{}=\\mbox{\\_features}\\) last dimension shape input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary_cross_entropy — nnf_binary_cross_entropy","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"Function measures Binary Cross Entropy target output.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"","code":"nnf_binary_cross_entropy(   input,   target,   weight = NULL,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary_cross_entropy — nnf_binary_cross_entropy","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input weight (tensor) weight value. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"Function measures Binary Cross Entropy target output logits.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"","code":"nnf_binary_cross_entropy_with_logits(   input,   target,   weight = NULL,   reduction = c(\"mean\", \"sum\", \"none\"),   pos_weight = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_binary_cross_entropy_with_logits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits","text":"input Tensor arbitrary shape target Tensor shape input weight (Tensor, optional) manual rescaling weight provided repeated match input tensor shape. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean' pos_weight (Tensor, optional) weight positive examples. Must vector length equal number classes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu — nnf_celu","title":"Celu — nnf_celu","text":"Applies element-wise, \\(CELU(x) = max(0,x) + min(0, \\alpha * (exp(x \\alpha) - 1))\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu — nnf_celu","text":"","code":"nnf_celu(input, alpha = 1, inplace = FALSE)  nnf_celu_(input, alpha = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu — nnf_celu","text":"input (N,*) tensor, * means, number additional dimensions alpha alpha value CELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparsemax — nnf_contrib_sparsemax","title":"Sparsemax — nnf_contrib_sparsemax","text":"Applies SparseMax activation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparsemax — nnf_contrib_sparsemax","text":"","code":"nnf_contrib_sparsemax(input, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparsemax — nnf_contrib_sparsemax","text":"input input tensor dim dimension apply sparsemax function. (-1)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_contrib_sparsemax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sparsemax — nnf_contrib_sparsemax","text":"SparseMax activation described 'Softmax Sparsemax: Sparse Model Attention Multi-Label Classification' implementation based aced125/sparsemax","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1d — nnf_conv1d","title":"Conv1d — nnf_conv1d","text":"Applies 1D convolution input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1d — nnf_conv1d","text":"","code":"nnf_conv1d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1d — nnf_conv1d","text":"input input tensor shape (minibatch, in_channels , iW) weight filters shape (out_channels, in_channels/groups , kW) bias optional bias shape (out_channels). Default: NULL stride stride convolving kernel. Can single number one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can single number one-element tuple (padW,). Default: 0 dilation spacing kernel elements. Can single number one-element tuple (dW,). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2d — nnf_conv2d","title":"Conv2d — nnf_conv2d","text":"Applies 2D convolution input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2d — nnf_conv2d","text":"","code":"nnf_conv2d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2d — nnf_conv2d","text":"input input tensor shape (minibatch, in_channels, iH , iW) weight filters shape (out_channels , in_channels/groups, kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padH, padW). Default: 0 dilation spacing kernel elements. Can single number tuple (dH, dW). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3d — nnf_conv3d","title":"Conv3d — nnf_conv3d","text":"Applies 3D convolution input image composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3d — nnf_conv3d","text":"","code":"nnf_conv3d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   dilation = 1,   groups = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3d — nnf_conv3d","text":"input input tensor shape (minibatch, in_channels , , iH , iW) weight filters shape (out_channels , in_channels/groups, kT , kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padT, padH, padW). Default: 0 dilation spacing kernel elements. Can single number tuple (dT, dH, dW). Default: 1 groups split input groups, in_channels divisible number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_tbc — nnf_conv_tbc","title":"Conv_tbc — nnf_conv_tbc","text":"Applies 1-dimensional sequence convolution input sequence. Input output dimensions (Time, Batch, Channels) - hence TBC.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_tbc — nnf_conv_tbc","text":"","code":"nnf_conv_tbc(input, weight, bias, pad = 0)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_tbc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_tbc — nnf_conv_tbc","text":"input input tensor shape \\((\\mbox{sequence length} \\times batch \\times \\mbox{\\_channels})\\) weight filter shape (\\(\\mbox{kernel width} \\times \\mbox{\\_channels} \\times \\mbox{\\_channels}\\)) bias bias shape (\\(\\mbox{\\_channels}\\)) pad number timesteps pad. Default: 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose1d — nnf_conv_transpose1d","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"Applies 1D transposed convolution operator input signal composed several input planes, sometimes also called \"deconvolution\".","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"","code":"nnf_conv_transpose1d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose1d — nnf_conv_transpose1d","text":"input input tensor shape (minibatch, in_channels , iW) weight filters shape (out_channels, in_channels/groups , kW) bias optional bias shape (out_channels). Default: NULL stride stride convolving kernel. Can single number one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can single number one-element tuple (padW,). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number one-element tuple (dW,). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose2d — nnf_conv_transpose2d","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\".","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"","code":"nnf_conv_transpose2d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose2d — nnf_conv_transpose2d","text":"input input tensor shape (minibatch, in_channels, iH , iW) weight filters shape (out_channels , in_channels/groups, kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padH, padW). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number tuple (dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose3d — nnf_conv_transpose3d","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\"","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"","code":"nnf_conv_transpose3d(   input,   weight,   bias = NULL,   stride = 1,   padding = 0,   output_padding = 0,   groups = 1,   dilation = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose3d — nnf_conv_transpose3d","text":"input input tensor shape (minibatch, in_channels , , iH , iW) weight filters shape (out_channels , in_channels/groups, kT , kH , kW) bias optional bias tensor shape (out_channels). Default: NULL stride stride convolving kernel. Can single number tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can single number tuple (padT, padH, padW). Default: 0 output_padding padding applied output groups split input groups, in_channels divisible number groups. Default: 1 dilation spacing kernel elements. Can single number tuple (dT, dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_embedding_loss — nnf_cosine_embedding_loss","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"Creates criterion measures loss given input tensors x_1, x_2 Tensor label y values 1 -1. used measuring whether two inputs similar dissimilar, using cosine distance, typically used learning nonlinear embeddings semi-supervised learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"","code":"nnf_cosine_embedding_loss(   input1,   input2,   target,   margin = 0,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_embedding_loss — nnf_cosine_embedding_loss","text":"input1 input x_1 tensor input2 input x_2 tensor target target tensor margin number -1 1 , 0 0.5 suggested. margin missing, default value 0. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_similarity — nnf_cosine_similarity","title":"Cosine_similarity — nnf_cosine_similarity","text":"Returns cosine similarity x1 x2, computed along dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_similarity — nnf_cosine_similarity","text":"","code":"nnf_cosine_similarity(x1, x2, dim = 2, eps = 1e-08)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_similarity — nnf_cosine_similarity","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). dim (int, optional) Dimension vectors. Default: 2 eps (float, optional) Small value avoid division zero. Default: 1e-8","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cosine_similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cosine_similarity — nnf_cosine_similarity","text":"$$     \\mbox{similarity} = \\frac{x_1 \\cdot x_2}{\\max(\\Vert x_1 \\Vert _2 \\cdot \\Vert x_2 \\Vert _2, \\epsilon)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross_entropy — nnf_cross_entropy","title":"Cross_entropy — nnf_cross_entropy","text":"criterion combines log_softmax nll_loss single function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross_entropy — nnf_cross_entropy","text":"","code":"nnf_cross_entropy(   input,   target,   weight = NULL,   ignore_index = -100,   reduction = c(\"mean\", \"sum\", \"none\") )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_cross_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross_entropy — nnf_cross_entropy","text":"input (Tensor) \\((N, C)\\) C = number classes \\((N, C, H, W)\\) case 2D Loss, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. target (Tensor) \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional loss. weight (Tensor, optional) manual rescaling weight given class. given, Tensor size C ignore_index (int, optional) Specifies target value ignored contribute input gradient. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Ctc_loss — nnf_ctc_loss","title":"Ctc_loss — nnf_ctc_loss","text":"Connectionist Temporal Classification loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ctc_loss — nnf_ctc_loss","text":"","code":"nnf_ctc_loss(   log_probs,   targets,   input_lengths,   target_lengths,   blank = 0,   reduction = c(\"mean\", \"sum\", \"none\"),   zero_infinity = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_ctc_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ctc_loss — nnf_ctc_loss","text":"log_probs \\((T, N, C)\\) C = number characters alphabet including blank, T = input length, N = batch size. logarithmized probabilities outputs (e.g. obtained nnf_log_softmax). targets \\((N, S)\\) (sum(target_lengths)). Targets blank. second form, targets assumed concatenated. input_lengths \\((N)\\). Lengths inputs (must \\(\\leq T\\)) target_lengths \\((N)\\). Lengths targets blank (int, optional) Blank label. Default \\(0\\). reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean' zero_infinity (bool, optional) Whether zero infinite losses associated gradients. Default: FALSE Infinite losses mainly occur inputs short aligned targets.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout — nnf_dropout","title":"Dropout — nnf_dropout","text":"training, randomly zeroes elements input tensor probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout — nnf_dropout","text":"","code":"nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout — nnf_dropout","text":"input input tensor p probability element zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout2d — nnf_dropout2d","title":"Dropout2d — nnf_dropout2d","text":"Randomly zero entire channels (channel 2D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 2D tensor \\(input[, j]\\)) input tensor). channel zeroed independently every forward call probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout2d — nnf_dropout2d","text":"","code":"nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout2d — nnf_dropout2d","text":"input input tensor p probability channel zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE. inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Dropout3d — nnf_dropout3d","title":"Dropout3d — nnf_dropout3d","text":"Randomly zero entire channels (channel 3D feature map, e.g., \\(j\\)-th channel \\(\\)-th sample batched input 3D tensor \\(input[, j]\\)) input tensor). channel zeroed independently every forward call probability p using samples Bernoulli distribution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dropout3d — nnf_dropout3d","text":"","code":"nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_dropout3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dropout3d — nnf_dropout3d","text":"input input tensor p probability channel zeroed. Default: 0.5 training apply dropout TRUE. Default: TRUE. inplace set TRUE, operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":null,"dir":"Reference","previous_headings":"","what":"Elu — nnf_elu","title":"Elu — nnf_elu","text":"Applies element-wise, $$ELU(x) = max(0,x) + min(0, \\alpha * (exp(x) - 1))$$.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Elu — nnf_elu","text":"","code":"nnf_elu(input, alpha = 1, inplace = FALSE)  nnf_elu_(input, alpha = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Elu — nnf_elu","text":"input (N,*) tensor, * means, number additional dimensions alpha alpha value ELU formulation. Default: 1.0 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_elu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Elu — nnf_elu","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, 2) y <- nnf_elu(x, alpha = 1) nnf_elu_(x, alpha = 1) torch_equal(x, y) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding — nnf_embedding","title":"Embedding — nnf_embedding","text":"simple lookup table looks embeddings fixed dictionary size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding — nnf_embedding","text":"","code":"nnf_embedding(   input,   weight,   padding_idx = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   sparse = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding — nnf_embedding","text":"input (LongTensor) Tensor containing indices embedding matrix weight (Tensor) embedding matrix number rows equal maximum possible index + 1, number columns equal embedding size padding_idx (int, optional) given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index. max_norm (float, optional) given, embedding vector norm larger max_norm renormalized norm max_norm. Note: modify weight -place. norm_type (float, optional) p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional) given, scale gradients inverse frequency words mini-batch. Default FALSE. sparse (bool, optional) TRUE, gradient w.r.t. weight sparse tensor. See Notes nn_embedding details regarding sparse gradients.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Embedding — nnf_embedding","text":"module often used retrieve word embeddings using indices. input module list indices, embedding matrix, output corresponding word embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":null,"dir":"Reference","previous_headings":"","what":"Embedding_bag — nnf_embedding_bag","title":"Embedding_bag — nnf_embedding_bag","text":"Computes sums, means maxes bags embeddings, without instantiating intermediate embeddings.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Embedding_bag — nnf_embedding_bag","text":"","code":"nnf_embedding_bag(   input,   weight,   offsets = NULL,   max_norm = NULL,   norm_type = 2,   scale_grad_by_freq = FALSE,   mode = \"mean\",   sparse = FALSE,   per_sample_weights = NULL,   include_last_offset = FALSE,   padding_idx = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_embedding_bag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Embedding_bag — nnf_embedding_bag","text":"input (LongTensor) Tensor containing bags indices embedding matrix weight (Tensor) embedding matrix number rows equal maximum possible index + 1, number columns equal embedding size offsets (LongTensor, optional) used input 1D. offsets determines starting index position bag (sequence) input. max_norm (float, optional) given, embedding vector norm larger max_norm renormalized norm max_norm. Note: modify weight -place. norm_type (float, optional) p p-norm compute max_norm option. Default 2. scale_grad_by_freq (boolean, optional) given, scale gradients inverse frequency words mini-batch. Default FALSE.                                            Note: option supported mode=\"max\". mode (string, optional) \"sum\", \"mean\" \"max\". Specifies way reduce bag. Default: 'mean' sparse (bool, optional) TRUE, gradient w.r.t. weight sparse tensor. See Notes nn_embedding details regarding sparse gradients. Note: option supported mode=\"max\". per_sample_weights (Tensor, optional) tensor float / double weights, NULL indicate weights taken 1. specified, per_sample_weights must exactly shape input treated offsets, NULL. include_last_offset (bool, optional) TRUE, size offsets equal number bags + 1. padding_idx (int, optional) given, pads output embedding vector padding_idx (initialized zeros) whenever encounters index.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold — nnf_fold","title":"Fold — nnf_fold","text":"Combines array sliding local blocks large containing tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold — nnf_fold","text":"","code":"nnf_fold(   input,   output_size,   kernel_size,   dilation = 1,   padding = 0,   stride = 1 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold — nnf_fold","text":"input input tensor output_size shape spatial dimensions output (.e., output$sizes()[-c(1,2)]) kernel_size size sliding blocks dilation parameter controls stride elements within neighborhood. Default: 1 padding implicit zero padding added sides input. Default: 0 stride stride sliding blocks input spatial dimensions. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fold.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Fold — nnf_fold","text":"Currently, 4-D output tensors (batched image-like tensors) supported.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional_max_pool2d — nnf_fractional_max_pool2d","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"Applies 2D fractional max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"","code":"nnf_fractional_max_pool2d(   input,   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE,   random_samples = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"input input tensor kernel_size size window take max . Can single number \\(k\\) (square kernel \\(k * k\\)) tuple (kH, kW) output_size target output size image form \\(oH * oW\\). Can tuple (oH, oW) single number \\(oH\\) square image \\(oH * oH\\) output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices True, return indices along outputs. random_samples optional random samples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional_max_pool2d — nnf_fractional_max_pool2d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling_ Ben Graham max-pooling operation applied \\(kH * kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Fractional_max_pool3d — nnf_fractional_max_pool3d","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"Applies 3D fractional max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"","code":"nnf_fractional_max_pool3d(   input,   kernel_size,   output_size = NULL,   output_ratio = NULL,   return_indices = FALSE,   random_samples = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"input input tensor kernel_size size window take max . Can single number \\(k\\) (square kernel \\(k * k * k\\)) tuple (kT, kH, kW) output_size target output size form \\(oT * oH * oW\\). Can tuple (oT, oH, oW) single number \\(oH\\) cubic output \\(oH * oH * oH\\) output_ratio one wants output size ratio input size, option can given. number tuple range (0, 1) return_indices True, return indices along outputs. random_samples undocumented argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_fractional_max_pool3d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fractional_max_pool3d — nnf_fractional_max_pool3d","text":"Fractional MaxPooling described detail paper Fractional MaxPooling_ Ben Graham max-pooling operation applied \\(kT * kH * kW\\) regions stochastic step size determined target output size. number output features equal number input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Gelu — nnf_gelu","title":"Gelu — nnf_gelu","text":"Gelu","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gelu — nnf_gelu","text":"","code":"nnf_gelu(input, approximate = \"none\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gelu — nnf_gelu","text":"input (N,*) tensor, * means, number additional dimensions approximate default none, applies element-wise x*pnorm(x), 'tanh', GELU estimated. See GELU info.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gelu.html","id":"gelu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"gelu(input) -> Tensor","title":"Gelu — nnf_gelu","text":"Applies element-wise function \\(GELU(x) = x * \\Phi(x)\\) \\(\\Phi(x)\\) Cumulative Distribution Function Gaussian Distribution. See Gaussian Error Linear Units (GELUs).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":null,"dir":"Reference","previous_headings":"","what":"Glu — nnf_glu","title":"Glu — nnf_glu","text":"gated linear unit. Computes:","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glu — nnf_glu","text":"","code":"nnf_glu(input, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glu — nnf_glu","text":"input (Tensor) input tensor dim (int) dimension split input. Default: -1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_glu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glu — nnf_glu","text":"$$GLU(, b) = \\otimes \\sigma(b)$$ input split half along dim form b, \\(\\sigma\\) sigmoid function \\(\\otimes\\) element-wise product matrices. See Language Modeling Gated Convolutional Networks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid_sample — nnf_grid_sample","title":"Grid_sample — nnf_grid_sample","text":"Given input flow-field grid, computes output using input values pixel locations grid.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid_sample — nnf_grid_sample","text":"","code":"nnf_grid_sample(   input,   grid,   mode = c(\"bilinear\", \"nearest\"),   padding_mode = c(\"zeros\", \"border\", \"reflection\"),   align_corners = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid_sample — nnf_grid_sample","text":"input (Tensor) input shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\) (4-D case)                    \\((N, C, D_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}})\\) (5-D case) grid (Tensor) flow-field shape \\((N, H_{\\mbox{}}, W_{\\mbox{}}, 2)\\) (4-D case)                   \\((N, D_{\\mbox{}}, H_{\\mbox{}}, W_{\\mbox{}}, 3)\\) (5-D case) mode (str) interpolation mode calculate output values 'bilinear' | 'nearest'. Default: 'bilinear' padding_mode (str) padding mode outside grid values 'zeros' | 'border' | 'reflection'. Default: 'zeros' align_corners (bool, optional) Geometrically, consider pixels input  squares rather points. set True, extrema (-1 1) considered referring center points input's corner pixels. set False, instead considered referring corner points input's corner pixels, making sampling resolution agnostic. option parallels align_corners option  nnf_interpolate(), whichever option used also used resize input image grid sampling. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Grid_sample — nnf_grid_sample","text":"Currently, spatial (4-D) volumetric (5-D) input supported. spatial (4-D) case, input shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\) grid shape \\((N, H_{\\mbox{}}, W_{\\mbox{}}, 2)\\), output shape \\((N, C, H_{\\mbox{}}, W_{\\mbox{}})\\). output location output[n, :, h, w], size-2 vector grid[n, h, w] specifies input pixel locations x y, used interpolate output value output[n, :, h, w]. case 5D inputs, grid[n, d, h, w] specifies x, y, z pixel locations interpolating output[n, :, d, h, w]. mode argument specifies nearest bilinear interpolation method sample input pixels. grid specifies sampling pixel locations normalized input spatial dimensions. Therefore, values range [-1, 1]. example, values x = -1, y = -1 left-top pixel input, values  x = 1, y = 1 right-bottom pixel input. grid values outside range [-1, 1], corresponding outputs handled defined padding_mode. Options padding_mode=\"zeros\": use 0 --bound grid locations, padding_mode=\"border\": use border values --bound grid locations, padding_mode=\"reflection\": use values locations reflected border --bound grid locations. location far away border, keep reflected becoming bound, e.g., (normalized) pixel location x = -3.5 reflects border -1 becomes x' = 1.5, reflects border 1 becomes x'' = -0.5.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_grid_sample.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Grid_sample — nnf_grid_sample","text":"function often used conjunction nnf_affine_grid() build Spatial Transformer Networks_ .","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Group_norm — nnf_group_norm","title":"Group_norm — nnf_group_norm","text":"Applies Group Normalization last certain number dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group_norm — nnf_group_norm","text":"","code":"nnf_group_norm(input, num_groups, weight = NULL, bias = NULL, eps = 1e-05)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_group_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group_norm — nnf_group_norm","text":"input input tensor num_groups number groups separate channels weight weight tensor bias bias tensor eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Gumbel_softmax — nnf_gumbel_softmax","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"Samples Gumbel-Softmax distribution optionally discretizes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"","code":"nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_gumbel_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gumbel_softmax — nnf_gumbel_softmax","text":"logits [..., num_features] unnormalized log probabilities tau non-negative scalar temperature hard True, returned samples discretized one-hot vectors,        differentiated soft sample autograd dim (int) dimension along softmax computed. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardshrink — nnf_hardshrink","title":"Hardshrink — nnf_hardshrink","text":"Applies hard shrinkage function element-wise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardshrink — nnf_hardshrink","text":"","code":"nnf_hardshrink(input, lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardshrink — nnf_hardshrink","text":"input (N,*) tensor, * means, number additional dimensions lambd lambda value Hardshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardsigmoid — nnf_hardsigmoid","title":"Hardsigmoid — nnf_hardsigmoid","text":"Applies element-wise function \\(\\mbox{Hardsigmoid}(x) = \\frac{ReLU6(x + 3)}{6}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardsigmoid — nnf_hardsigmoid","text":"","code":"nnf_hardsigmoid(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardsigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardsigmoid — nnf_hardsigmoid","text":"input (N,*) tensor, * means, number additional dimensions inplace NA set True, operation -place. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardswish — nnf_hardswish","title":"Hardswish — nnf_hardswish","text":"Applies hardswish function, element-wise, described paper: Searching MobileNetV3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardswish — nnf_hardswish","text":"","code":"nnf_hardswish(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardswish — nnf_hardswish","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardswish.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hardswish — nnf_hardswish","text":"$$ \\mbox{Hardswish}(x) = \\left\\{   \\begin{array}{ll}   0 & \\mbox{} x \\le -3, \\\\   x & \\mbox{} x \\ge +3, \\\\   x \\cdot (x + 3)/6 & \\mbox{otherwise}   \\end{array}   \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Hardtanh — nnf_hardtanh","title":"Hardtanh — nnf_hardtanh","text":"Applies HardTanh function element-wise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hardtanh — nnf_hardtanh","text":"","code":"nnf_hardtanh(input, min_val = -1, max_val = 1, inplace = FALSE)  nnf_hardtanh_(input, min_val = -1, max_val = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hardtanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hardtanh — nnf_hardtanh","text":"input (N,*) tensor, * means, number additional dimensions min_val minimum value linear region range. Default: -1 max_val maximum value linear region range. Default: 1 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Hinge_embedding_loss — nnf_hinge_embedding_loss","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"Measures loss given input tensor xx labels tensor yy (containing 1 -1). usually used measuring whether two inputs similar dissimilar, e.g. using L1 pairwise distance xx , typically used learning nonlinear embeddings semi-supervised learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"","code":"nnf_hinge_embedding_loss(input, target, margin = 1, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_hinge_embedding_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hinge_embedding_loss — nnf_hinge_embedding_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input margin default value 1. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Instance_norm — nnf_instance_norm","title":"Instance_norm — nnf_instance_norm","text":"Applies Instance Normalization channel data sample batch.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Instance_norm — nnf_instance_norm","text":"","code":"nnf_instance_norm(   input,   running_mean = NULL,   running_var = NULL,   weight = NULL,   bias = NULL,   use_input_stats = TRUE,   momentum = 0.1,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_instance_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Instance_norm — nnf_instance_norm","text":"input input tensor running_mean running_mean tensor running_var running var tensor weight weight tensor bias bias tensor use_input_stats whether use input stats momentum double momentum eps eps double numerical stability","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpolate — nnf_interpolate","title":"Interpolate — nnf_interpolate","text":"/samples input either given size given scale_factor","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpolate — nnf_interpolate","text":"","code":"nnf_interpolate(   input,   size = NULL,   scale_factor = NULL,   mode = \"nearest\",   align_corners = FALSE,   recompute_scale_factor = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpolate — nnf_interpolate","text":"input (Tensor) input tensor size (int Tuple[int] Tuple[int, int] Tuple[int, int, int]) output spatial size. scale_factor (float Tuple[float]) multiplier spatial size. match input size tuple. mode (str) algorithm used upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' Default: 'nearest' align_corners (bool, optional) Geometrically, consider pixels input output squares rather points. set TRUE, input output tensors aligned center points corner pixels, preserving values corner pixels. set False, input output tensors aligned corner points corner pixels, interpolation uses edge value padding --boundary values, making operation independent input size scale_factor kept . effect mode  'linear', 'bilinear', 'bicubic' 'trilinear'.  Default: False recompute_scale_factor (bool, optional) recompute scale_factor use interpolation calculation.  scale_factor passed parameter, used compute output_size.  recompute_scale_factor “`True“ specified, new scale_factor computed based output input sizes use interpolation computation (.e. computation identical computed `output_size` passed-explicitly).  Otherwise, passed-`scale_factor` used interpolation computation.  Note `scale_factor` floating-point, recomputed scale_factor may differ one passed due rounding precision issues.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_interpolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interpolate — nnf_interpolate","text":"algorithm used interpolation determined mode. Currently temporal, spatial volumetric sampling supported, .e. expected inputs 3-D, 4-D 5-D shape. input dimensions interpreted form: mini-batch x channels x [optional depth] x [optional height] x width. modes available resizing : nearest, linear (3D-), bilinear, bicubic (4D-), trilinear (5D-), area","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Kl_div — nnf_kl_div","title":"Kl_div — nnf_kl_div","text":"Kullback-Leibler divergence Loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kl_div — nnf_kl_div","text":"","code":"nnf_kl_div(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_kl_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kl_div — nnf_kl_div","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"L1_loss — nnf_l1_loss","title":"L1_loss — nnf_l1_loss","text":"Function takes mean element-wise absolute value difference.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L1_loss — nnf_l1_loss","text":"","code":"nnf_l1_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L1_loss — nnf_l1_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer_norm — nnf_layer_norm","title":"Layer_norm — nnf_layer_norm","text":"Applies Layer Normalization last certain number dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer_norm — nnf_layer_norm","text":"","code":"nnf_layer_norm(   input,   normalized_shape,   weight = NULL,   bias = NULL,   eps = 1e-05 )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_layer_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer_norm — nnf_layer_norm","text":"input input tensor normalized_shape input shape expected input size. single integer used, treated singleton list, module normalize last dimension expected specific size. weight weight tensor bias bias tensor eps value added denominator numerical stability. Default: 1e-5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Leaky_relu — nnf_leaky_relu","title":"Leaky_relu — nnf_leaky_relu","text":"Applies element-wise, \\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leaky_relu — nnf_leaky_relu","text":"","code":"nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_leaky_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leaky_relu — nnf_leaky_relu","text":"input (N,*) tensor, * means, number additional dimensions negative_slope Controls angle negative slope. Default: 1e-2 inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear — nnf_linear","title":"Linear — nnf_linear","text":"Applies linear transformation incoming data: \\(y = xA^T + b\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear — nnf_linear","text":"","code":"nnf_linear(input, weight, bias = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear — nnf_linear","text":"input \\((N, *, \\_features)\\) * means number additional dimensions weight \\((\\_features, \\_features)\\) weights tensor. bias optional tensor \\((\\_features)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Local_response_norm — nnf_local_response_norm","title":"Local_response_norm — nnf_local_response_norm","text":"Applies local response normalization input signal composed several input planes, channels occupy second dimension. Applies normalization across channels.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Local_response_norm — nnf_local_response_norm","text":"","code":"nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_local_response_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Local_response_norm — nnf_local_response_norm","text":"input input tensor size amount neighbouring channels used normalization alpha multiplicative factor. Default: 0.0001 beta exponent. Default: 0.75 k additive factor. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Log_softmax — nnf_log_softmax","title":"Log_softmax — nnf_log_softmax","text":"Applies softmax followed logarithm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log_softmax — nnf_log_softmax","text":"","code":"nnf_log_softmax(input, dim = NULL, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log_softmax — nnf_log_softmax","text":"input (Tensor) input dim (int) dimension along log_softmax computed. dtype (torch.dtype, optional) desired data type returned tensor. specified, input tensor casted dtype operation performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_log_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Log_softmax — nnf_log_softmax","text":"mathematically equivalent log(softmax(x)), two operations separately slower, numerically unstable. function uses alternative formulation compute output gradient correctly.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Logsigmoid — nnf_logsigmoid","title":"Logsigmoid — nnf_logsigmoid","text":"Applies element-wise \\(LogSigmoid(x_i) = log(\\frac{1}{1 + exp(-x_i)})\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logsigmoid — nnf_logsigmoid","text":"","code":"nnf_logsigmoid(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_logsigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logsigmoid — nnf_logsigmoid","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Lp_pool1d — nnf_lp_pool1d","title":"Lp_pool1d — nnf_lp_pool1d","text":"Applies 1D power-average pooling input signal composed several input planes. sum inputs power p zero, gradient set zero well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lp_pool1d — nnf_lp_pool1d","text":"","code":"nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lp_pool1d — nnf_lp_pool1d","text":"input input tensor norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode True, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Lp_pool2d — nnf_lp_pool2d","title":"Lp_pool2d — nnf_lp_pool2d","text":"Applies 2D power-average pooling input signal composed several input planes. sum inputs power p zero, gradient set zero well.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lp_pool2d — nnf_lp_pool2d","text":"","code":"nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_lp_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lp_pool2d — nnf_lp_pool2d","text":"input input tensor norm_type inf one gets max pooling 0 get sum pooling ( proportional avg pooling) kernel_size single int, size window stride single int, stride window. Default value kernel_size ceil_mode True, use ceil instead floor compute output shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Margin_ranking_loss — nnf_margin_ranking_loss","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"Creates criterion measures loss given inputs x1 , x2 , two 1D mini-batch Tensors, label 1D mini-batch tensor y (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"","code":"nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_margin_ranking_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Margin_ranking_loss — nnf_margin_ranking_loss","text":"input1 first tensor input2 second input tensor target target tensor margin default value 00 . reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool1d — nnf_max_pool1d","title":"Max_pool1d — nnf_max_pool1d","text":"Applies 1D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool1d — nnf_max_pool1d","text":"","code":"nnf_max_pool1d(   input,   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool1d — nnf_max_pool1d","text":"input input tensor shape (minibatch , in_channels , iW) kernel_size size window. Can single number tuple (kW,). stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor compute output shape. Default: FALSE return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool2d — nnf_max_pool2d","title":"Max_pool2d — nnf_max_pool2d","text":"Applies 2D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool2d — nnf_max_pool2d","text":"","code":"nnf_max_pool2d(   input,   kernel_size,   stride = kernel_size,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool2d — nnf_max_pool2d","text":"input input tensor (minibatch, in_channels , iH , iW) kernel_size size pooling region. Can single number tuple (kH, kW) stride stride pooling operation. Can single number tuple (sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padH, padW). Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor formula compute output shape. Default: FALSE return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_pool3d — nnf_max_pool3d","title":"Max_pool3d — nnf_max_pool3d","text":"Applies 3D max pooling input signal composed several input planes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_pool3d — nnf_max_pool3d","text":"","code":"nnf_max_pool3d(   input,   kernel_size,   stride = NULL,   padding = 0,   dilation = 1,   ceil_mode = FALSE,   return_indices = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_pool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_pool3d — nnf_max_pool3d","text":"input input tensor (minibatch, in_channels , * iH , iW) kernel_size size pooling region. Can single number tuple (kT, kH, kW) stride stride pooling operation. Can single number tuple (sT, sH, sW). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padT, padH, padW), Default: 0 dilation controls spacing kernel points; also known à trous algorithm. ceil_mode True, use ceil instead floor formula compute output shape return_indices whether return indices max occurs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool1d — nnf_max_unpool1d","title":"Max_unpool1d — nnf_max_unpool1d","text":"Computes partial inverse MaxPool1d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool1d — nnf_max_unpool1d","text":"","code":"nnf_max_unpool1d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool1d — nnf_max_unpool1d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool2d — nnf_max_unpool2d","title":"Max_unpool2d — nnf_max_unpool2d","text":"Computes partial inverse MaxPool2d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool2d — nnf_max_unpool2d","text":"","code":"nnf_max_unpool2d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool2d — nnf_max_unpool2d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Max_unpool3d — nnf_max_unpool3d","title":"Max_unpool3d — nnf_max_unpool3d","text":"Computes partial inverse MaxPool3d.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Max_unpool3d — nnf_max_unpool3d","text":"","code":"nnf_max_unpool3d(   input,   indices,   kernel_size,   stride = NULL,   padding = 0,   output_size = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_max_unpool3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max_unpool3d — nnf_max_unpool3d","text":"input input Tensor invert indices indices given max pool kernel_size Size max pooling window. stride Stride max pooling window. set kernel_size default. padding Padding added input output_size targeted output size","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Mse_loss — nnf_mse_loss","title":"Mse_loss — nnf_mse_loss","text":"Measures element-wise mean squared error.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mse_loss — nnf_mse_loss","text":"","code":"nnf_mse_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_mse_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mse_loss — nnf_mse_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi head attention forward — nnf_multi_head_attention_forward","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"Allows model jointly attend information different representation subspaces. See reference: Attention Need","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"","code":"nnf_multi_head_attention_forward(   query,   key,   value,   embed_dim_to_check,   num_heads,   in_proj_weight,   in_proj_bias,   bias_k,   bias_v,   add_zero_attn,   dropout_p,   out_proj_weight,   out_proj_bias,   training = TRUE,   key_padding_mask = NULL,   need_weights = TRUE,   attn_mask = NULL,   avg_weights = TRUE,   use_separate_proj_weight = FALSE,   q_proj_weight = NULL,   k_proj_weight = NULL,   v_proj_weight = NULL,   static_k = NULL,   static_v = NULL,   batch_first = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_head_attention_forward.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi head attention forward — nnf_multi_head_attention_forward","text":"query \\((L, N, E)\\) L target sequence length, N batch size, E embedding dimension. batch_first TRUE, first two dimensions transposed. key \\((S, N, E)\\), S source sequence length, N batch size, E embedding dimension. batch_first TRUE, first two dimensions transposed. value \\((S, N, E)\\) S source sequence length, N batch size, E embedding dimension. batch_first TRUE, first two dimensions transposed. embed_dim_to_check total dimension model. num_heads parallel attention heads. in_proj_weight input projection weight. in_proj_bias input projection bias. bias_k bias key value sequences added dim=0. bias_v currently undocumented. add_zero_attn add new batch zeros key value sequences dim=1. dropout_p probability element zeroed. out_proj_weight output projection weight. out_proj_bias output projection bias. training apply dropout TRUE. key_padding_mask \\((N, S)\\) N batch size, S source sequence length. ByteTensor provided, non-zero positions ignored position zero positions unchanged. BoolTensor provided, positions value True ignored position value False unchanged. need_weights output attn_output_weights. attn_mask 2D mask \\((L, S)\\) L target sequence length, S source sequence length. 3D mask \\((N*num_heads, L, S)\\) N batch size, L target sequence length, S source sequence length. attn_mask ensure position allowed attend unmasked positions. ByteTensor provided, non-zero positions allowed attend zero positions unchanged. BoolTensor provided, positions True allowed attend False values unchanged. FloatTensor provided, added attention weight. avg_weights Logical; whether average attn_output_weights attention heads outputting . change returned value attn_output; affects returned attention weight matrix. use_separate_proj_weight function accept proj. weights query, key, value different forms. false, in_proj_weight used, combination q_proj_weight, k_proj_weight, v_proj_weight. q_proj_weight input projection weight bias. k_proj_weight currently undocumented. v_proj_weight currently undocumented. static_k static key value used attention operators. static_v currently undocumented. batch_first Logical; whether expect query, key, value batch first parameter, return output batch first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi_margin_loss — nnf_multi_margin_loss","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"Creates criterion optimizes multi-class classification hinge loss (margin-based loss) input x (2D mini-batch Tensor) output y (1D tensor target class indices, 0 <= y <= x$size(2) - 1 ).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"","code":"nnf_multi_margin_loss(   input,   target,   p = 1,   margin = 1,   weight = NULL,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multi_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi_margin_loss — nnf_multi_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input p default value 1. 1 2 supported values. margin default value 1. weight manual rescaling weight given class. given, Tensor size C. Otherwise, treated ones. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel_margin_loss — nnf_multilabel_margin_loss","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"Creates criterion optimizes multi-class multi-classification hinge loss (margin-based loss) input x (2D mini-batch Tensor) output y (2D Tensor target class indices).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"","code":"nnf_multilabel_margin_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel_margin_loss — nnf_multilabel_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"Creates criterion optimizes multi-label one-versus-loss based max-entropy, input x target y size (N, C).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"","code":"nnf_multilabel_soft_margin_loss(   input,   target,   weight = NULL,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input weight weight tensor apply loss. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_multilabel_soft_margin_loss.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss","text":"takes one hot encoded target vector input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Nll_loss — nnf_nll_loss","title":"Nll_loss — nnf_nll_loss","text":"negative log likelihood loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nll_loss — nnf_nll_loss","text":"","code":"nnf_nll_loss(   input,   target,   weight = NULL,   ignore_index = -100,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nll_loss — nnf_nll_loss","text":"input \\((N, C)\\) C = number classes \\((N, C, H, W)\\) case 2D Loss, \\((N, C, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) case K-dimensional loss. target \\((N)\\) value \\(0 \\leq \\mbox{targets}[] \\leq C-1\\), \\((N, d_1, d_2, ..., d_K)\\) \\(K \\geq 1\\) K-dimensional loss. weight (Tensor, optional) manual rescaling weight given class. given, Tensor size C ignore_index (int, optional) Specifies target value ignored contribute input gradient. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize — nnf_normalize","title":"Normalize — nnf_normalize","text":"Performs \\(L_p\\) normalization inputs specified dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize — nnf_normalize","text":"","code":"nnf_normalize(input, p = 2, dim = 2, eps = 1e-12, out = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize — nnf_normalize","text":"input input tensor shape p (float) exponent value norm formulation. Default: 2 dim (int) dimension reduce. Default: 1 eps (float) small value avoid division zero. Default: 1e-12 (Tensor, optional) output tensor. used,                            operation differentiable.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_normalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Normalize — nnf_normalize","text":"tensor input sizes \\((n_0, ..., n_{dim}, ..., n_k)\\), \\(n_{dim}\\) -element vector \\(v\\) along dimension dim transformed $$         v = \\frac{v}{\\max(\\Vert v \\Vert_p, \\epsilon)}. $$ default arguments uses Euclidean norm vectors along dimension \\(1\\) normalization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":null,"dir":"Reference","previous_headings":"","what":"One_hot — nnf_one_hot","title":"One_hot — nnf_one_hot","text":"Takes LongTensor index values shape (*) returns tensor shape (*, num_classes) zeros everywhere except index last dimension matches corresponding value input tensor, case 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One_hot — nnf_one_hot","text":"","code":"nnf_one_hot(tensor, num_classes = -1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One_hot — nnf_one_hot","text":"tensor (LongTensor) class values shape. num_classes (int) Total number classes. set -1, number classes inferred one greater largest class value input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_one_hot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"One_hot — nnf_one_hot","text":"One-hot Wikipedia: https://en.wikipedia.org/wiki/One-hot","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"Pad — nnf_pad","title":"Pad — nnf_pad","text":"Pads tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pad — nnf_pad","text":"","code":"nnf_pad(input, pad, mode = \"constant\", value = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pad — nnf_pad","text":"input (Tensor) N-dimensional tensor pad (tuple) m-elements tuple, \\(\\frac{m}{2} \\leq\\) input dimensions \\(m\\) even. mode 'constant', 'reflect', 'replicate' 'circular'. Default: 'constant' value fill value 'constant' padding. Default: 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"padding-size","dir":"Reference","previous_headings":"","what":"Padding size","title":"Pad — nnf_pad","text":"padding size pad dimensions input described starting last dimension moving forward. \\(\\left\\lfloor\\frac{\\mbox{len(pad)}}{2}\\right\\rfloor\\) dimensions input padded. example, pad last dimension input tensor, pad form \\((\\mbox{padding\\_left}, \\mbox{padding\\_right})\\); pad last 2 dimensions input tensor, use \\((\\mbox{padding\\_left}, \\mbox{padding\\_right},\\) \\(\\mbox{padding\\_top}, \\mbox{padding\\_bottom})\\); pad last 3 dimensions, use \\((\\mbox{padding\\_left}, \\mbox{padding\\_right},\\) \\(\\mbox{padding\\_top}, \\mbox{padding\\_bottom}\\) \\(\\mbox{padding\\_front}, \\mbox{padding\\_back})\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pad.html","id":"padding-mode","dir":"Reference","previous_headings":"","what":"Padding mode","title":"Pad — nnf_pad","text":"See nn_constant_pad_2d, nn_reflection_pad_2d, nn_replication_pad_2d concrete examples padding modes works. Constant padding implemented arbitrary dimensions. tensor, last 2 dimensions 4D input tensor, last dimension 3D input tensor. Reflect padding implemented padding last 2 dimensions 4D input tensor, last dimension 3D input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise_distance — nnf_pairwise_distance","title":"Pairwise_distance — nnf_pairwise_distance","text":"Computes batchwise pairwise distance vectors using p-norm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise_distance — nnf_pairwise_distance","text":"","code":"nnf_pairwise_distance(x1, x2, p = 2, eps = 1e-06, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pairwise_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise_distance — nnf_pairwise_distance","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). p norm degree. Default: 2 eps (float, optional) Small value avoid division zero. Default: 1e-8 keepdim Determines whether keep vector dimension. Default: False","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Pdist — nnf_pdist","title":"Pdist — nnf_pdist","text":"Computes p-norm distance every pair row vectors input. identical upper triangular portion, excluding diagonal, torch_norm(input[:, None] - input, dim=2, p=p). function faster rows contiguous.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pdist — nnf_pdist","text":"","code":"nnf_pdist(input, p = 2)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pdist — nnf_pdist","text":"input input tensor shape \\(N \\times M\\). p p value p-norm distance calculate vector pair \\(\\[0, \\infty]\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pdist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pdist — nnf_pdist","text":"input shape \\(N \\times M\\) output shape \\(\\frac{1}{2} N (N - 1)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Pixel_shuffle — nnf_pixel_shuffle","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"Rearranges elements tensor shape \\((*, C \\times r^2, H, W)\\) tensor shape \\((*, C, H \\times r, W \\times r)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"","code":"nnf_pixel_shuffle(input, upscale_factor)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_pixel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pixel_shuffle — nnf_pixel_shuffle","text":"input (Tensor) input tensor upscale_factor (int) factor increase spatial resolution ","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson_nll_loss — nnf_poisson_nll_loss","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"Poisson negative log likelihood loss.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"","code":"nnf_poisson_nll_loss(   input,   target,   log_input = TRUE,   full = FALSE,   eps = 1e-08,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_poisson_nll_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson_nll_loss — nnf_poisson_nll_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input log_input TRUE loss computed \\(\\exp(\\mbox{input}) - \\mbox{target} * \\mbox{input}\\), FALSE loss \\(\\mbox{input} - \\mbox{target} * \\log(\\mbox{input}+\\mbox{eps})\\). Default: TRUE. full whether compute full loss, . e. add Stirling approximation term. Default: FALSE. eps (float, optional) Small value avoid evaluation \\(\\log(0)\\) log_input=FALSE. Default: 1e-8 reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Prelu — nnf_prelu","title":"Prelu — nnf_prelu","text":"Applies element-wise function \\(PReLU(x) = max(0,x) + weight * min(0,x)\\) weight learnable parameter.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prelu — nnf_prelu","text":"","code":"nnf_prelu(input, weight)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_prelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prelu — nnf_prelu","text":"input (N,*) tensor, * means, number additional dimensions weight (Tensor) learnable weights","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu — nnf_relu","title":"Relu — nnf_relu","text":"Applies rectified linear unit function element-wise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu — nnf_relu","text":"","code":"nnf_relu(input, inplace = FALSE)  nnf_relu_(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu — nnf_relu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu6 — nnf_relu6","title":"Relu6 — nnf_relu6","text":"Applies element-wise function \\(ReLU6(x) = min(max(0,x), 6)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu6 — nnf_relu6","text":"","code":"nnf_relu6(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_relu6.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu6 — nnf_relu6","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":null,"dir":"Reference","previous_headings":"","what":"Rrelu — nnf_rrelu","title":"Rrelu — nnf_rrelu","text":"Randomized leaky ReLU.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rrelu — nnf_rrelu","text":"","code":"nnf_rrelu(input, lower = 1/8, upper = 1/3, training = FALSE, inplace = FALSE)  nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_rrelu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rrelu — nnf_rrelu","text":"input (N,*) tensor, * means, number additional dimensions lower lower bound uniform distribution. Default: 1/8 upper upper bound uniform distribution. Default: 1/3 training bool wether training pass. DEfault: FALSE inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu — nnf_selu","title":"Selu — nnf_selu","text":"Applies element-wise, $$SELU(x) = scale * (max(0,x) + min(0, \\alpha * (exp(x) - 1)))$$, \\(\\alpha=1.6732632423543772848170429916717\\) \\(scale=1.0507009873554804934193349852946\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu — nnf_selu","text":"","code":"nnf_selu(input, inplace = FALSE)  nnf_selu_(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu — nnf_selu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_selu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selu — nnf_selu","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, 2) y <- nnf_selu(x) nnf_selu_(x) torch_equal(x, y) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid — nnf_sigmoid","title":"Sigmoid — nnf_sigmoid","text":"Applies element-wise \\(Sigmoid(x_i) = \\frac{1}{1 + exp(-x_i)}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid — nnf_sigmoid","text":"","code":"nnf_sigmoid(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid — nnf_sigmoid","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_silu.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","text":"Applies Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() information.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_silu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","text":"","code":"nnf_silu(input, inplace = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_silu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the Sigmoid Linear Unit (SiLU) function, element-wise. See nn_silu() for more information. — nnf_silu","text":"input (N,*) tensor, * means, number additional dimensions inplace can optionally operation -place. Default: FALSE","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth_l1_loss — nnf_smooth_l1_loss","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"Function uses squared term absolute element-wise error falls 1 L1 term otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"","code":"nnf_smooth_l1_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_smooth_l1_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth_l1_loss — nnf_smooth_l1_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Soft_margin_loss — nnf_soft_margin_loss","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"Creates criterion optimizes two-class classification logistic loss input tensor x target tensor y (containing 1 -1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"","code":"nnf_soft_margin_loss(input, target, reduction = \"mean\")"},{"path":"https://torch.mlverse.org/docs/reference/nnf_soft_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soft_margin_loss — nnf_soft_margin_loss","text":"input tensor (N,*) ** means, number additional dimensions target tensor (N,*) , shape input reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmax — nnf_softmax","title":"Softmax — nnf_softmax","text":"Applies softmax function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmax — nnf_softmax","text":"","code":"nnf_softmax(input, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmax — nnf_softmax","text":"input (Tensor) input dim (int) dimension along softmax computed. dtype (torch.dtype, optional) desired data type returned tensor.      specified, input tensor casted dtype operation      performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmax.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmax — nnf_softmax","text":"Softmax defined : $$Softmax(x_{}) = exp(x_i)/\\sum_j exp(x_j)$$ applied slices along dim, re-scale elements lie range [0, 1] sum 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Softmin — nnf_softmin","title":"Softmin — nnf_softmin","text":"Applies softmin function.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softmin — nnf_softmin","text":"","code":"nnf_softmin(input, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softmin — nnf_softmin","text":"input (Tensor) input dim (int) dimension along softmin computed (every slice        along dim sum 1). dtype (torch.dtype, optional) desired data type returned tensor.      specified, input tensor casted dtype operation      performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softmin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softmin — nnf_softmin","text":"Note $$Softmin(x) = Softmax(-x)$$. See nnf_softmax definition mathematical formula.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":null,"dir":"Reference","previous_headings":"","what":"Softplus — nnf_softplus","title":"Softplus — nnf_softplus","text":"Applies element-wise, function \\(Softplus(x) = 1/\\beta * log(1 + exp(\\beta * x))\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softplus — nnf_softplus","text":"","code":"nnf_softplus(input, beta = 1, threshold = 20)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softplus — nnf_softplus","text":"input (N,*) tensor, * means, number additional dimensions beta beta value Softplus formulation. Default: 1 threshold values revert linear function. Default: 20","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softplus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Softplus — nnf_softplus","text":"numerical stability implementation reverts linear function \\(input * \\beta > threshold\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Softshrink — nnf_softshrink","title":"Softshrink — nnf_softshrink","text":"Applies soft shrinkage function elementwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softshrink — nnf_softshrink","text":"","code":"nnf_softshrink(input, lambd = 0.5)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softshrink — nnf_softshrink","text":"input (N,*) tensor, * means, number additional dimensions lambd lambda (must less zero) value Softshrink formulation. Default: 0.5","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":null,"dir":"Reference","previous_headings":"","what":"Softsign — nnf_softsign","title":"Softsign — nnf_softsign","text":"Applies element-wise, function \\(SoftSign(x) = x/(1 + |x|\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Softsign — nnf_softsign","text":"","code":"nnf_softsign(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_softsign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Softsign — nnf_softsign","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanhshrink — nnf_tanhshrink","title":"Tanhshrink — nnf_tanhshrink","text":"Applies element-wise, \\(Tanhshrink(x) = x - Tanh(x)\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanhshrink — nnf_tanhshrink","text":"","code":"nnf_tanhshrink(input)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_tanhshrink.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tanhshrink — nnf_tanhshrink","text":"input (N,*) tensor, * means, number additional dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold — nnf_threshold","title":"Threshold — nnf_threshold","text":"Thresholds element input Tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold — nnf_threshold","text":"","code":"nnf_threshold(input, threshold, value, inplace = FALSE)  nnf_threshold_(input, threshold, value)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold — nnf_threshold","text":"input (N,*) tensor, * means, number additional dimensions threshold value threshold value value replace inplace can optionally operation -place. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet_margin_loss — nnf_triplet_margin_loss","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"Creates criterion measures triplet loss given input tensors x1 , x2 , x3 margin value greater 0 . used measuring relative similarity samples. triplet composed , p n (.e., anchor, positive examples negative examples respectively). shapes input tensors (N, D).","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"","code":"nnf_triplet_margin_loss(   anchor,   positive,   negative,   margin = 1,   p = 2,   eps = 1e-06,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet_margin_loss — nnf_triplet_margin_loss","text":"anchor anchor input tensor positive positive input tensor negative negative input tensor margin Default: 1. p norm degree pairwise distance. Default: 2. eps (float, optional) Small value avoid division zero. swap distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":null,"dir":"Reference","previous_headings":"","what":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"See nn_triplet_margin_with_distance_loss()","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"","code":"nnf_triplet_margin_with_distance_loss(   anchor,   positive,   negative,   distance_function = NULL,   margin = 1,   swap = FALSE,   reduction = \"mean\" )"},{"path":"https://torch.mlverse.org/docs/reference/nnf_triplet_margin_with_distance_loss.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triplet margin with distance loss — nnf_triplet_margin_with_distance_loss","text":"anchor anchor input tensor positive positive input tensor negative negative input tensor distance_function (callable, optional): nonnegative, real-valued function quantifies closeness two tensors. specified, nn_pairwise_distance() used.  Default: None margin Default: 1. swap distance swap described detail paper Learning shallow convolutional feature descriptors triplet losses V. Balntas, E. Riba et al. Default: FALSE. reduction (string, optional) – Specifies reduction apply output: 'none' | 'mean' | 'sum'. 'none': reduction applied, 'mean': sum output divided number elements output, 'sum': output summed. Default: 'mean'","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":null,"dir":"Reference","previous_headings":"","what":"Unfold — nnf_unfold","title":"Unfold — nnf_unfold","text":"Extracts sliding local blocks batched input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unfold — nnf_unfold","text":"","code":"nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)"},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unfold — nnf_unfold","text":"input input tensor kernel_size size sliding blocks dilation parameter controls stride elements within neighborhood. Default: 1 padding implicit zero padding added sides input. Default: 0 stride stride sliding blocks input spatial dimensions. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/nnf_unfold.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unfold — nnf_unfold","text":"one element unfolded tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensor, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":null,"dir":"Reference","previous_headings":"","what":"Adadelta optimizer — optim_adadelta","title":"Adadelta optimizer — optim_adadelta","text":"proposed ADADELTA: Adaptive Learning Rate Method","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adadelta optimizer — optim_adadelta","text":"","code":"optim_adadelta(params, lr = 1, rho = 0.9, eps = 1e-06, weight_decay = 0)"},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adadelta optimizer — optim_adadelta","text":"params (iterable): list parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-3) rho (float, optional): coefficient used computing running average squared gradients (default: 0.9) eps (float, optional): term added denominator improve numerical stability (default: 1e-6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Adadelta optimizer — optim_adadelta","text":"According original paper, decaying average squared gradients computed follows: $$ E[g^2]_{t} = \\rho E[g^2]_{t- 1} + (1 - \\rho){g_{t}}^2 $$ RMS previous squared gradients time t: $$ RMS[g_{t}] = \\sqrt{E[g^2]_{t} + \\epsilon } $$ Adadelta update rule: $$  \\begin{array}{ll}  \\Delta \\theta_{t} = - \\frac{RMS [\\Delta \\theta]_{t - 1} }{RMS[g]_{t}}  \\theta_{t+1} = \\theta_{t} + \\Delta \\theta_{t} \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Adadelta optimizer — optim_adadelta","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adadelta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adadelta optimizer — optim_adadelta","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_adadelta(model$parameters, lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"Adagrad optimizer — optim_adagrad","title":"Adagrad optimizer — optim_adagrad","text":"Proposed Adaptive Subgradient Methods Online Learning Stochastic Optimization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adagrad optimizer — optim_adagrad","text":"","code":"optim_adagrad(   params,   lr = 0.01,   lr_decay = 0,   weight_decay = 0,   initial_accumulator_value = 0,   eps = 1e-10 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adagrad optimizer — optim_adagrad","text":"params (iterable): list parameters optimize list parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) initial_accumulator_value initial value accumulator. (default: 0) Adagrad especially good optimizer sparse data. individually modifies learning rate every single parameter, dividing original learning rate value sum squares gradients. causes rarely occurring features get greater learning rates. main downside method fact learning rate may getting small fast, point model learn anymore. eps (float, optional): term added denominator improve numerical stability (default: 1e-10)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Adagrad optimizer — optim_adagrad","text":"Update rule: $$ \\theta_{t+1} = \\theta_{t} - \\frac{\\eta }{\\sqrt{G_{t} + \\epsilon}} \\odot g_{t} $$ equation remarks quoted overview gradient descent optimization algorithms Sebastian Ruder.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adagrad.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Adagrad optimizer — optim_adagrad","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements Adam algorithm. — optim_adam","title":"Implements Adam algorithm. — optim_adam","text":"proposed Adam: Method Stochastic Optimization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements Adam algorithm. — optim_adam","text":"","code":"optim_adam(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements Adam algorithm. — optim_adam","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Implements Adam algorithm. — optim_adam","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements Adam algorithm. — optim_adam","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_adam(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/optim_adamw.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements AdamW algorithm — optim_adamw","title":"Implements AdamW algorithm — optim_adamw","text":"details regarding algorithm refer Decoupled Weight Decay Regularization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_adamw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements AdamW algorithm — optim_adamw","text":"","code":"optim_adamw(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0.01,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_adamw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements AdamW algorithm — optim_adamw","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":null,"dir":"Reference","previous_headings":"","what":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"Proposed Acceleration stochastic approximation averaging","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"","code":"optim_asgd(   params,   lr = 0.01,   lambda = 1e-04,   alpha = 0.75,   t0 = 1e+06,   weight_decay = 0 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"params (iterable): iterable parameters optimize lists defining parameter groups lr (float): learning rate lambda (float, optional): decay term (default: 1e-4) alpha (float, optional): power eta update (default: 0.75) t0 (float, optional): point start averaging (default: 1e6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_asgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Averaged Stochastic Gradient Descent optimizer — optim_asgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_asgd(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adagrad.html","id":null,"dir":"Reference","previous_headings":"","what":"LibTorch implementation of Adagrad — optim_ignite_adagrad","title":"LibTorch implementation of Adagrad — optim_ignite_adagrad","text":"Proposed Adaptive Subgradient Methods Online Learning Stochastic Optimization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adagrad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LibTorch implementation of Adagrad — optim_ignite_adagrad","text":"","code":"optim_ignite_adagrad(   params,   lr = 0.01,   lr_decay = 0,   weight_decay = 0,   initial_accumulator_value = 0,   eps = 1e-10 )"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adagrad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LibTorch implementation of Adagrad — optim_ignite_adagrad","text":"params (iterable): list parameters optimize list parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) initial_accumulator_value initial value accumulator. (default: 0) Adagrad especially good optimizer sparse data. individually modifies learning rate every single parameter, dividing original learning rate value sum squares gradients. causes rarely occurring features get greater learning rates. main downside method fact learning rate may getting small fast, point model learn anymore. eps (float, optional): term added denominator improve numerical stability (default: 1e-10)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adagrad.html","id":"fields-and-methods","dir":"Reference","previous_headings":"","what":"Fields and Methods","title":"LibTorch implementation of Adagrad — optim_ignite_adagrad","text":"See OptimizerIgnite.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adagrad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LibTorch implementation of Adagrad — optim_ignite_adagrad","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_ignite_adagrad(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adam.html","id":null,"dir":"Reference","previous_headings":"","what":"LibTorch implementation of Adam — optim_ignite_adam","title":"LibTorch implementation of Adam — optim_ignite_adam","text":"proposed Adam: Method Stochastic Optimization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LibTorch implementation of Adam — optim_ignite_adam","text":"","code":"optim_ignite_adam(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LibTorch implementation of Adam — optim_ignite_adam","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adam.html","id":"fields-and-methods","dir":"Reference","previous_headings":"","what":"Fields and Methods","title":"LibTorch implementation of Adam — optim_ignite_adam","text":"See OptimizerIgnite.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LibTorch implementation of Adam — optim_ignite_adam","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_ignite_adam(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adamw.html","id":null,"dir":"Reference","previous_headings":"","what":"LibTorch implementation of AdamW — optim_ignite_adamw","title":"LibTorch implementation of AdamW — optim_ignite_adamw","text":"details regarding algorithm refer Decoupled Weight Decay Regularization","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adamw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LibTorch implementation of AdamW — optim_ignite_adamw","text":"","code":"optim_ignite_adamw(   params,   lr = 0.001,   betas = c(0.9, 0.999),   eps = 1e-08,   weight_decay = 0.01,   amsgrad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adamw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LibTorch implementation of AdamW — optim_ignite_adamw","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used computing running averages gradient square (default: (0.9, 0.999)) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether use AMSGrad variant algorithm paper Convergence Adam Beyond (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adamw.html","id":"fields-and-methods","dir":"Reference","previous_headings":"","what":"Fields and Methods","title":"LibTorch implementation of AdamW — optim_ignite_adamw","text":"See OptimizerIgnite.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_adamw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LibTorch implementation of AdamW — optim_ignite_adamw","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_ignite_adamw(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"LibTorch implementation of RMSprop — optim_ignite_rmsprop","title":"LibTorch implementation of RMSprop — optim_ignite_rmsprop","text":"Proposed G. Hinton course.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LibTorch implementation of RMSprop — optim_ignite_rmsprop","text":"","code":"optim_ignite_rmsprop(   params,   lr = 0.01,   alpha = 0.99,   eps = 1e-08,   weight_decay = 0,   momentum = 0,   centered = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LibTorch implementation of RMSprop — optim_ignite_rmsprop","text":"params (iterable): iterable parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-2) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay optional weight decay penalty. (default: 0) momentum (float, optional): momentum factor (default: 0) centered (bool, optional) : TRUE, compute centered RMSProp, gradient normalized estimation variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_rmsprop.html","id":"fields-and-methods","dir":"Reference","previous_headings":"","what":"Fields and Methods","title":"LibTorch implementation of RMSprop — optim_ignite_rmsprop","text":"See OptimizerIgnite.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_rmsprop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LibTorch implementation of RMSprop — optim_ignite_rmsprop","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_ignite_rmsprop(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"LibTorch implementation of SGD — optim_ignite_sgd","title":"LibTorch implementation of SGD — optim_ignite_sgd","text":"Implements stochastic gradient descent (optionally momentum). Nesterov momentum based formula importance initialization momentum deep learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LibTorch implementation of SGD — optim_ignite_sgd","text":"","code":"optim_ignite_sgd(   params,   lr = optim_required(),   momentum = 0,   dampening = 0,   weight_decay = 0,   nesterov = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LibTorch implementation of SGD — optim_ignite_sgd","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate momentum (float, optional): momentum factor (default: 0) dampening (float, optional): dampening momentum (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) nesterov (bool, optional): enables Nesterov momentum (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_sgd.html","id":"fields-and-methods","dir":"Reference","previous_headings":"","what":"Fields and Methods","title":"LibTorch implementation of SGD — optim_ignite_sgd","text":"See OptimizerIgnite.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_ignite_sgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LibTorch implementation of SGD — optim_ignite_sgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_ignite_sgd(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":null,"dir":"Reference","previous_headings":"","what":"LBFGS optimizer — optim_lbfgs","title":"LBFGS optimizer — optim_lbfgs","text":"Implements L-BFGS algorithm, heavily inspired minFunc","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LBFGS optimizer — optim_lbfgs","text":"","code":"optim_lbfgs(   params,   lr = 1,   max_iter = 20,   max_eval = NULL,   tolerance_grad = 1e-07,   tolerance_change = 1e-09,   history_size = 100,   line_search_fn = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LBFGS optimizer — optim_lbfgs","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate (default: 1) max_iter (int): maximal number iterations per optimization step (default: 20) max_eval (int): maximal number function evaluations per optimization step (default: max_iter * 1.25). tolerance_grad (float): termination tolerance first order optimality (default: 1e-5). tolerance_change (float): termination tolerance function value/parameter changes (default: 1e-9). history_size (int): update history size (default: 100). line_search_fn (str): either 'strong_wolfe' None (default: None).","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LBFGS optimizer — optim_lbfgs","text":"optimizer different others optimizer$step(), needs passed closure (1) calculates loss, (2) calls backward() , (3) returns . See example .","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"LBFGS optimizer — optim_lbfgs","text":"memory intensive optimizer (requires additional param_bytes * (history_size + 1) bytes). fit memory try reducing history size, use different algorithm.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"LBFGS optimizer — optim_lbfgs","text":"optimizer support per-parameter options parameter groups (can one). Right now parameters single device. improved future. need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_lbfgs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LBFGS optimizer — optim_lbfgs","text":"","code":"if (torch_is_installed()) { a <- 1 b <- 5 rosenbrock <- function(x) {   x1 <- x[1]   x2 <- x[2]   (a - x1)^2 + b * (x2 - x1^2)^2 }  x <- torch_tensor(c(-1, 1), requires_grad = TRUE)  optimizer <- optim_lbfgs(x) calc_loss <- function() {   optimizer$zero_grad()   value <- rosenbrock(x)   value$backward()   value }  num_iterations <- 2 for (i in 1:num_iterations) {   optimizer$step(calc_loss) }  rosenbrock(x)  } #> torch_tensor #> 1e-12 * #>  4.5475 #> [ CPUFloatType{1} ][ grad_fn = <AddBackward0> ]"},{"path":"https://torch.mlverse.org/docs/reference/optim_required.html","id":null,"dir":"Reference","previous_headings":"","what":"Dummy value indicating a required value. — optim_required","title":"Dummy value indicating a required value. — optim_required","text":"Dummy value indicating required value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_required.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dummy value indicating a required value. — optim_required","text":"","code":"optim_required()"},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":null,"dir":"Reference","previous_headings":"","what":"RMSprop optimizer — optim_rmsprop","title":"RMSprop optimizer — optim_rmsprop","text":"Proposed G. Hinton course.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RMSprop optimizer — optim_rmsprop","text":"","code":"optim_rmsprop(   params,   lr = 0.01,   alpha = 0.99,   eps = 1e-08,   weight_decay = 0,   momentum = 0,   centered = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RMSprop optimizer — optim_rmsprop","text":"params (iterable): iterable parameters optimize list defining parameter groups lr (float, optional): learning rate (default: 1e-2) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added denominator improve numerical stability (default: 1e-8) weight_decay optional weight decay penalty. (default: 0) momentum (float, optional): momentum factor (default: 0) centered (bool, optional) : TRUE, compute centered RMSProp, gradient normalized estimation variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RMSprop optimizer — optim_rmsprop","text":"centered version first appears Generating Sequences Recurrent Neural Networks. implementation takes square root gradient average adding epsilon (note TensorFlow interchanges two operations). effective learning rate thus \\(\\alpha/(\\sqrt{v} + \\epsilon)\\) \\(\\alpha\\) scheduled learning rate \\(v\\) weighted moving average squared gradient. Update rule: $$ \\theta_{t+1} = \\theta_{t} - \\frac{\\eta }{\\sqrt{{E[g^2]}_{t} + \\epsilon}} * g_{t} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rmsprop.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"RMSprop optimizer — optim_rmsprop","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements the resilient backpropagation algorithm. — optim_rprop","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"Proposed first RPROP - Fast Adaptive Learning Algorithm","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"","code":"optim_rprop(params, lr = 0.01, etas = c(0.5, 1.2), step_sizes = c(1e-06, 50))"},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"params (iterable): iterable parameters optimize lists defining parameter groups lr (float, optional): learning rate (default: 1e-2) etas (Tuple(float, float), optional): pair (etaminus, etaplis), multiplicative increase decrease factors (default: (0.5, 1.2)) step_sizes (vector(float, float), optional): pair minimal maximal allowed step sizes (default: (1e-6, 50))","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_rprop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements the resilient backpropagation algorithm. — optim_rprop","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_rprop(model$parameters(), lr = 0.1) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":null,"dir":"Reference","previous_headings":"","what":"SGD optimizer — optim_sgd","title":"SGD optimizer — optim_sgd","text":"Implements stochastic gradient descent (optionally momentum). Nesterov momentum based formula importance initialization momentum deep learning.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SGD optimizer — optim_sgd","text":"","code":"optim_sgd(   params,   lr = optim_required(),   momentum = 0,   dampening = 0,   weight_decay = 0,   nesterov = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SGD optimizer — optim_sgd","text":"params (iterable): iterable parameters optimize dicts defining parameter groups lr (float): learning rate momentum (float, optional): momentum factor (default: 0) dampening (float, optional): dampening momentum (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) nesterov (bool, optional): enables Nesterov momentum (default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"SGD optimizer — optim_sgd","text":"implementation SGD Momentum-Nesterov subtly differs Sutskever et. al. implementations frameworks. Considering specific case Momentum, update can written $$   \\begin{array}{ll} v_{t+1} & = \\mu * v_{t} + g_{t+1}, \\\\ p_{t+1} & = p_{t} - \\mbox{lr} * v_{t+1}, \\end{array} $$ \\(p\\), \\(g\\), \\(v\\) \\(\\mu\\) denote parameters, gradient, velocity, momentum respectively. contrast Sutskever et. al. frameworks employ update form $$   \\begin{array}{ll} v_{t+1} & = \\mu * v_{t} + \\mbox{lr} * g_{t+1}, \\\\ p_{t+1} & = p_{t} - v_{t+1}. \\end{array} $$ Nesterov version analogously modified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"SGD optimizer — optim_sgd","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optim_sgd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SGD optimizer — optim_sgd","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ optimizer <- optim_sgd(model$parameters(), lr = 0.1, momentum = 0.9) optimizer$zero_grad() loss_fn(model(input), target)$backward() optimizer$step() } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a custom optimizer — optimizer","title":"Creates a custom optimizer — optimizer","text":"implementing custom optimizers usually need implement initialize step methods. See example section full example.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a custom optimizer — optimizer","text":"","code":"optimizer(   name = NULL,   inherit = Optimizer,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a custom optimizer — optimizer","text":"name (optional) name optimizer inherit (optional) can inherit optimizers re-use methods. ... Pass number fields methods. least define initialize step methods. See examples section. private (optional) list private methods optimizer. active (optional) list active methods optimizer. parent_env used capture right environment define class. default fine situations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Creates a custom optimizer — optimizer","text":"need move model GPU via $cuda(), please constructing optimizers . Parameters model $cuda() different objects call. general, make sure objects pointed model parameters subject optimization remain whole lifecycle optimizer creation usage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a custom optimizer — optimizer","text":"","code":"if (torch_is_installed()) {  # In this example we will create a custom optimizer # that's just a simplified version of the `optim_sgd` function.  optim_sgd2 <- optimizer(   initialize = function(params, learning_rate) {     defaults <- list(       learning_rate = learning_rate     )     super$initialize(params, defaults)   },   step = function() {     with_no_grad({       for (g in seq_along(self$param_groups)) {         group <- self$param_groups[[g]]         for (p in seq_along(group$params)) {           param <- group$params[[p]]            if (is.null(param$grad) || is_undefined_tensor(param$grad)) {             next           }            param$add_(param$grad, alpha = -group$learning_rate)         }       }     })   } )  x <- torch_randn(1, requires_grad = TRUE) opt <- optim_sgd2(x, learning_rate = 0.1) for (i in 1:100) {   opt$zero_grad()   y <- x^2   y$backward()   opt$step() } all.equal(x$item(), 0, tolerance = 1e-9) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/optimizer_ignite.html","id":null,"dir":"Reference","previous_headings":"","what":"Abstract Base Class for LibTorch Optimizers — optimizer_ignite","title":"Abstract Base Class for LibTorch Optimizers — optimizer_ignite","text":"Abstract base class wrapping LibTorch C++ optimizers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/optimizer_ignite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abstract Base Class for LibTorch Optimizers — optimizer_ignite","text":"","code":"optimizer_ignite(   name = NULL,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/optimizer_ignite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abstract Base Class for LibTorch Optimizers — optimizer_ignite","text":"name (optional) name optimizer ... Pass number fields methods. least define initialize step methods. See examples section. private (optional) list private methods optimizer. active (optional) list active methods optimizer. parent_env used capture right environment define class. default fine situations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://torch.mlverse.org/docs/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-exporting the as_iterator function. — reexports","title":"Re-exporting the as_iterator function. — reexports","text":"objects imported packages. Follow links see documentation. coro as_iterator, loop, yield","code":""},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a new Sampler — sampler","title":"Creates a new Sampler — sampler","text":"Samplers can used dataloader() creating batches torch dataset().","code":""},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a new Sampler — sampler","text":"","code":"sampler(   name = NULL,   inherit = Sampler,   ...,   private = NULL,   active = NULL,   parent_env = parent.frame() )"},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a new Sampler — sampler","text":"name (optional) name sampler inherit (optional) can inherit samplers re-use methods. ... Pass number fields methods. least define initialize step methods. See examples section. private (optional) list private methods sampler active (optional) list active methods sampler. parent_env used capture right environment define class. default fine situations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/sampler.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a new Sampler — sampler","text":"sampler must implement .iter .length() methods. initialize takes data_source. general dataset(). .iter returns function returns dataset index everytime called. .length returns maximum number samples can retrieved sampler.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a slice — slc","title":"Creates a slice — slc","text":"Creates slice object can used indexing torch tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a slice — slc","text":"","code":"slc(start, end, step = 1)"},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a slice — slc","text":"start (integer) starting index. end (integer) last selected index. step (integer) step indexes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/slc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a slice — slc","text":"","code":"if (torch_is_installed()) { x <- torch_randn(10) x[slc(start = 1, end = 5, step = 2)]  } #> torch_tensor #> -0.7800 #> -0.2878 #> -0.3140 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Dataset wrapping tensors. — tensor_dataset","title":"Dataset wrapping tensors. — tensor_dataset","text":"sample retrieved indexing tensors along first dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dataset wrapping tensors. — tensor_dataset","text":"","code":"tensor_dataset(...)"},{"path":"https://torch.mlverse.org/docs/reference/tensor_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dataset wrapping tensors. — tensor_dataset","text":"... tensors size first dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of threads — threads","title":"Number of threads — threads","text":"Get set numbers used torch computations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of threads — threads","text":"","code":"torch_set_num_threads(num_threads)  torch_set_num_interop_threads(num_threads)  torch_get_num_interop_threads()  torch_get_num_threads()"},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of threads — threads","text":"num_threads number threads set.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of threads — threads","text":"details see CPU threading article PyTorch documentation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/threads.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Number of threads — threads","text":"torch_set_threads work macOS system must 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":null,"dir":"Reference","previous_headings":"","what":"Abs — torch_abs","title":"Abs — torch_abs","text":"Abs","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abs — torch_abs","text":"","code":"torch_abs(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abs — torch_abs","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"abs-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"abs(input) -> Tensor","title":"Abs — torch_abs","text":"Computes element-wise absolute value given input tensor. $$     \\mbox{}_{} = |\\mbox{input}_{}| $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_abs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abs — torch_abs","text":"","code":"if (torch_is_installed()) {  torch_abs(torch_tensor(c(-1, -2, 3))) } #> torch_tensor #>  1 #>  2 #>  3 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":null,"dir":"Reference","previous_headings":"","what":"Absolute — torch_absolute","title":"Absolute — torch_absolute","text":"Absolute","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Absolute — torch_absolute","text":"","code":"torch_absolute(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Absolute — torch_absolute","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_absolute.html","id":"absolute-input-out-none-gt-tensor","dir":"Reference","previous_headings":"","what":"absolute(input, *, out=None) -> Tensor","title":"Absolute — torch_absolute","text":"Alias torch_abs()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":null,"dir":"Reference","previous_headings":"","what":"Acos — torch_acos","title":"Acos — torch_acos","text":"Acos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acos — torch_acos","text":"","code":"torch_acos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acos — torch_acos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"acos-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"acos(input) -> Tensor","title":"Acos — torch_acos","text":"Returns new tensor arccosine  elements input. $$     \\mbox{}_{} = \\cos^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acos — torch_acos","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_acos(a) } #> torch_tensor #>  2.2260 #>  1.5170 #>  0.9419 #>     nan #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Acosh — torch_acosh","title":"Acosh — torch_acosh","text":"Acosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acosh — torch_acosh","text":"","code":"torch_acosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acosh — torch_acosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acosh — torch_acosh","text":"domain inverse hyperbolic cosine [1, inf) values outside range mapped NaN, except + INF output mapped + INF. $$     \\mbox{}_{} = \\cosh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"acosh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"acosh(input, *, out=None) -> Tensor","title":"Acosh — torch_acosh","text":"Returns new tensor inverse hyperbolic cosine elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_acosh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acosh — torch_acosh","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4))$uniform_(1, 2) a torch_acosh(a) } #> torch_tensor #>  0.5337 #>  0.6603 #>  0.7640 #>  1.2929 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"Adaptive_avg_pool1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"","code":"torch_adaptive_avg_pool1d(self, output_size)"},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"self input tensor output_size target output size (single integer)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_adaptive_avg_pool1d.html","id":"adaptive-avg-pool-d-input-output-size-gt-tensor-","dir":"Reference","previous_headings":"","what":"adaptive_avg_pool1d(input, output_size) -> Tensor","title":"Adaptive_avg_pool1d — torch_adaptive_avg_pool1d","text":"Applies 1D adaptive average pooling input signal composed several input planes. See nn_adaptive_avg_pool1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":null,"dir":"Reference","previous_headings":"","what":"Add — torch_add","title":"Add — torch_add","text":"Add","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add — torch_add","text":"","code":"torch_add(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add — torch_add","text":"self (Tensor) input tensor. (Tensor/Number) second input tensor/number. alpha (Number) scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"add-input-other-out-null-","dir":"Reference","previous_headings":"","what":"add(input, other, out=NULL)","title":"Add — torch_add","text":"Adds scalar element input input returns new resulting tensor. $$     \\mbox{} = \\mbox{input} + \\mbox{} $$ input type FloatTensor DoubleTensor, must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"add-input-other-alpha-out-null-","dir":"Reference","previous_headings":"","what":"add(input, other, *, alpha=1, out=NULL)","title":"Add — torch_add","text":"element tensor multiplied scalar alpha added element tensor input. resulting tensor returned. shapes input must broadcastable . $$     \\mbox{} = \\mbox{input} + \\mbox{alpha} \\times \\mbox{} $$ type FloatTensor DoubleTensor, alpha must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_add.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add — torch_add","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_add(a, 20)   a = torch_randn(c(4)) a b = torch_randn(c(4, 1)) b torch_add(a, b) } #> torch_tensor #>  0.6399 -0.7199  1.3798  0.6940 #>  1.2243 -0.1355  1.9642  1.2784 #> -0.1605 -1.5202  0.5795 -0.1063 #> -1.3106 -2.6704 -0.5707 -1.2565 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Addbmm — torch_addbmm","title":"Addbmm — torch_addbmm","text":"Addbmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addbmm — torch_addbmm","text":"","code":"torch_addbmm(self, batch1, batch2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addbmm — torch_addbmm","text":"self (Tensor) matrix added batch1 (Tensor) first batch matrices multiplied batch2 (Tensor) second batch matrices multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier batch1 @ batch2 (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addbmm — torch_addbmm","text":"Performs batch matrix-matrix product matrices stored batch1 batch2, reduced add step (matrix multiplications get accumulated along first dimension). input added final result. batch1 batch2 must 3-D tensors containing number matrices. batch1 \\((b \\times n \\times m)\\) tensor, batch2 \\((b \\times m \\times p)\\) tensor, input must broadcastable  \\((n \\times p)\\) tensor \\((n \\times p)\\) tensor. $$     = \\beta\\ \\mbox{input} + \\alpha\\ (\\sum_{=0}^{b-1} \\mbox{batch1}_i \\mathbin{@} \\mbox{batch2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addbmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addbmm — torch_addbmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(3, 5)) batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_addbmm(M, batch1, batch2) } #> torch_tensor #>  -9.8463  -3.5710  -2.3026  -5.3469  -5.8927 #>  -2.8985  -1.8813  -0.5713   1.0045  12.3332 #> -11.9484  -9.7646   4.3405  -2.6854  -6.1667 #> [ CPUFloatType{3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":null,"dir":"Reference","previous_headings":"","what":"Addcdiv — torch_addcdiv","title":"Addcdiv — torch_addcdiv","text":"Addcdiv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addcdiv — torch_addcdiv","text":"","code":"torch_addcdiv(self, tensor1, tensor2, value = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addcdiv — torch_addcdiv","text":"self (Tensor) tensor added tensor1 (Tensor) numerator tensor tensor2 (Tensor) denominator tensor value (Number, optional) multiplier \\(\\mbox{tensor1} / \\mbox{tensor2}\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"addcdiv-input-tensor-tensor-value-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor","title":"Addcdiv — torch_addcdiv","text":"Performs element-wise division tensor1 tensor2, multiply result scalar value add input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Addcdiv — torch_addcdiv","text":"Integer division addcdiv deprecated, future release addcdiv perform true division tensor1 tensor2. current addcdiv behavior can replicated using torch_floor_divide() integral inputs (input + value * tensor1 // tensor2) torch_div() float inputs (input + value * tensor1 / tensor2). new addcdiv behavior can implemented torch_true_divide() (input + value * torch.true_divide(tensor1, tensor2). $$     \\mbox{}_i = \\mbox{input}_i + \\mbox{value} \\times \\frac{\\mbox{tensor1}_i}{\\mbox{tensor2}_i} $$ shapes input, tensor1, tensor2 must broadcastable . inputs type FloatTensor DoubleTensor, value must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcdiv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addcdiv — torch_addcdiv","text":"","code":"if (torch_is_installed()) {  t = torch_randn(c(1, 3)) t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcdiv(t, t1, t2, 0.1) } #> torch_tensor #> -0.3026  0.0542  0.0819 #>  0.1963  0.2301 -1.2194 #> -0.0807  0.1325 -0.4968 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Addcmul — torch_addcmul","title":"Addcmul — torch_addcmul","text":"Addcmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addcmul — torch_addcmul","text":"","code":"torch_addcmul(self, tensor1, tensor2, value = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addcmul — torch_addcmul","text":"self (Tensor) tensor added tensor1 (Tensor) tensor multiplied tensor2 (Tensor) tensor multiplied value (Number, optional) multiplier \\(tensor1 .* tensor2\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"addcmul-input-tensor-tensor-value-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -> Tensor","title":"Addcmul — torch_addcmul","text":"Performs element-wise multiplication tensor1 tensor2, multiply result scalar value add input. $$     \\mbox{}_i = \\mbox{input}_i + \\mbox{value} \\times \\mbox{tensor1}_i \\times \\mbox{tensor2}_i $$ shapes tensor, tensor1, tensor2 must broadcastable . inputs type FloatTensor DoubleTensor, value must real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addcmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addcmul — torch_addcmul","text":"","code":"if (torch_is_installed()) {  t = torch_randn(c(1, 3)) t1 = torch_randn(c(3, 1)) t2 = torch_randn(c(1, 3)) torch_addcmul(t, t1, t2, 0.1) } #> torch_tensor #>  0.4208 -0.4834  1.9171 #>  0.4852 -0.4345  1.9426 #>  0.4563 -0.4564  1.9312 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Addmm — torch_addmm","title":"Addmm — torch_addmm","text":"Addmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addmm — torch_addmm","text":"","code":"torch_addmm(self, mat1, mat2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addmm — torch_addmm","text":"self (Tensor) matrix added mat1 (Tensor) first matrix multiplied mat2 (Tensor) second matrix multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(mat1 @ mat2\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addmm — torch_addmm","text":"Performs matrix multiplication matrices mat1 mat2. matrix input added final result. mat1 \\((n \\times m)\\) tensor, mat2 \\((m \\times p)\\) tensor, input must broadcastable  \\((n \\times p)\\) tensor \\((n \\times p)\\) tensor. alpha beta scaling factors matrix-vector product mat1 mat2 added matrix input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{mat1}_i \\mathbin{@} \\mbox{mat2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addmm — torch_addmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(2, 3)) mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_addmm(M, mat1, mat2) } #> torch_tensor #>  0.2046  0.9396 -2.2487 #>  0.1475  0.1011 -1.0476 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":null,"dir":"Reference","previous_headings":"","what":"Addmv — torch_addmv","title":"Addmv — torch_addmv","text":"Addmv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addmv — torch_addmv","text":"","code":"torch_addmv(self, mat, vec, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addmv — torch_addmv","text":"self (Tensor) vector added mat (Tensor) matrix multiplied vec (Tensor) vector multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(mat @ vec\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addmv — torch_addmv","text":"Performs matrix-vector product matrix mat vector vec. vector input added final result. mat \\((n \\times m)\\) tensor, vec 1-D tensor size m, input must broadcastable  1-D tensor size n 1-D tensor size n. alpha beta scaling factors matrix-vector product mat vec added tensor input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{mat} \\mathbin{@} \\mbox{vec}) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addmv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addmv — torch_addmv","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(2)) mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_addmv(M, mat, vec) } #> torch_tensor #> -3.7403 #> -3.1906 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":null,"dir":"Reference","previous_headings":"","what":"Addr — torch_addr","title":"Addr — torch_addr","text":"Addr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Addr — torch_addr","text":"","code":"torch_addr(self, vec1, vec2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Addr — torch_addr","text":"self (Tensor) matrix added vec1 (Tensor) first vector outer product vec2 (Tensor) second vector outer product beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(\\mbox{vec1} \\otimes \\mbox{vec2}\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"addr-input-vec-vec-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Addr — torch_addr","text":"Performs outer-product vectors vec1 vec2 adds matrix input. Optional values beta alpha scaling factors outer product vec1 vec2 added matrix input respectively. $$     \\mbox{} = \\beta\\ \\mbox{input} + \\alpha\\ (\\mbox{vec1} \\otimes \\mbox{vec2}) $$ vec1 vector size n vec2 vector size m, input must broadcastable  matrix size \\((n \\times m)\\) matrix size \\((n \\times m)\\). inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_addr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Addr — torch_addr","text":"","code":"if (torch_is_installed()) {  vec1 = torch_arange(1, 3) vec2 = torch_arange(1, 2) M = torch_zeros(c(3, 2)) torch_addr(M, vec1, vec2) } #> torch_tensor #>  1  2 #>  2  4 #>  3  6 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Allclose — torch_allclose","title":"Allclose — torch_allclose","text":"Allclose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allclose — torch_allclose","text":"","code":"torch_allclose(self, other, rtol = 1e-05, atol = 1e-08, equal_nan = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allclose — torch_allclose","text":"self (Tensor) first tensor compare (Tensor) second tensor compare rtol (float, optional) relative tolerance. Default: 1e-05 atol (float, optional) absolute tolerance. Default: 1e-08 equal_nan (bool, optional) TRUE, two NaN s compared equal. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-","dir":"Reference","previous_headings":"","what":"allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool","title":"Allclose — torch_allclose","text":"function checks input satisfy condition: $$     \\vert \\mbox{input} - \\mbox{} \\vert \\leq \\mbox{atol} + \\mbox{rtol} \\times \\vert \\mbox{} \\vert $$ elementwise, elements input . behaviour function analogous numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_allclose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Allclose — torch_allclose","text":"","code":"if (torch_is_installed()) {  torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08))) torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09))) torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN))) torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":null,"dir":"Reference","previous_headings":"","what":"Amax — torch_amax","title":"Amax — torch_amax","text":"Amax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Amax — torch_amax","text":"","code":"torch_amax(self, dim = list(), keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Amax — torch_amax","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Amax — torch_amax","text":"difference max/min amax/amin : amax/amin supports reducing multiple dimensions, amax/amin return indices, amax/amin evenly distributes gradient equal values, max(dim)/min(dim) propagates gradient single index source tensor. keepdim TRUE, output tensors size inputexcept dimension(s)dimwhere size 1. Otherwise,dims squeezed (see [torch_squeeze()]), resulting output tensors fewer dimension input`.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"amax-input-dim-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"amax(input, dim, keepdim=FALSE, *, out=None) -> Tensor","title":"Amax — torch_amax","text":"Returns maximum value slice input tensor given dimension(s) dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Amax — torch_amax","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4, 4)) a torch_amax(a, 1) } #> torch_tensor #>  1.0024 #>  0.6182 #> -0.2393 #>  1.1946 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":null,"dir":"Reference","previous_headings":"","what":"Amin — torch_amin","title":"Amin — torch_amin","text":"Amin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Amin — torch_amin","text":"","code":"torch_amin(self, dim = list(), keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Amin — torch_amin","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Amin — torch_amin","text":"difference max/min amax/amin : amax/amin supports reducing multiple dimensions, amax/amin return indices, amax/amin evenly distributes gradient equal values, max(dim)/min(dim) propagates gradient single index source tensor. keepdim TRUE, output tensors size input except dimension(s) dim size 1. Otherwise, dims squeezed (see torch_squeeze()), resulting output tensors fewer dimensions input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"amin-input-dim-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"amin(input, dim, keepdim=FALSE, *, out=None) -> Tensor","title":"Amin — torch_amin","text":"Returns minimum value slice input tensor given dimension(s) dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_amin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Amin — torch_amin","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4, 4)) a torch_amin(a, 1) } #> torch_tensor #>  0.6590 #> -0.5597 #> -2.0884 #> -0.3251 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":null,"dir":"Reference","previous_headings":"","what":"Angle — torch_angle","title":"Angle — torch_angle","text":"Angle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Angle — torch_angle","text":"","code":"torch_angle(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Angle — torch_angle","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"angle-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"angle(input) -> Tensor","title":"Angle — torch_angle","text":"Computes element-wise angle (radians) given input tensor. $$     \\mbox{}_{} = angle(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_angle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Angle — torch_angle","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ torch_angle(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i)))*180/3.14159 } # }  }"},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":null,"dir":"Reference","previous_headings":"","what":"Arange — torch_arange","title":"Arange — torch_arange","text":"Arange","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arange — torch_arange","text":"","code":"torch_arange(   start,   end,   step = 1L,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arange — torch_arange","text":"start (Number) starting value set points. Default: 0. end (Number) ending value set points step (Number) gap pair adjacent points. Default: 1. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Arange — torch_arange","text":"Returns 1-D tensor size \\(\\left\\lceil \\frac{\\mbox{end} - \\mbox{start}}{\\mbox{step}} \\right\\rceil\\) values interval [start, end) taken common difference step beginning start. Note non-integer step subject floating point rounding errors comparing end; avoid inconsistency, advise adding small epsilon end cases. $$     \\mbox{}_{{+1}} = \\mbox{}_{} + \\mbox{step} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arange — torch_arange","text":"","code":"if (torch_is_installed()) {  torch_arange(start = 0, end = 5) torch_arange(1, 4) torch_arange(1, 2.5, 0.5) } #> torch_tensor #>  1.0000 #>  1.5000 #>  2.0000 #>  2.5000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":null,"dir":"Reference","previous_headings":"","what":"Arccos — torch_arccos","title":"Arccos — torch_arccos","text":"Arccos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arccos — torch_arccos","text":"","code":"torch_arccos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arccos — torch_arccos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccos.html","id":"arccos-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arccos(input, *, out=None) -> Tensor","title":"Arccos — torch_arccos","text":"Alias torch_acos().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arccosh — torch_arccosh","title":"Arccosh — torch_arccosh","text":"Arccosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arccosh — torch_arccosh","text":"","code":"torch_arccosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arccosh — torch_arccosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arccosh.html","id":"arccosh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arccosh(input, *, out=None) -> Tensor","title":"Arccosh — torch_arccosh","text":"Alias torch_acosh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":null,"dir":"Reference","previous_headings":"","what":"Arcsin — torch_arcsin","title":"Arcsin — torch_arcsin","text":"Arcsin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arcsin — torch_arcsin","text":"","code":"torch_arcsin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arcsin — torch_arcsin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsin.html","id":"arcsin-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arcsin(input, *, out=None) -> Tensor","title":"Arcsin — torch_arcsin","text":"Alias torch_asin().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arcsinh — torch_arcsinh","title":"Arcsinh — torch_arcsinh","text":"Arcsinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arcsinh — torch_arcsinh","text":"","code":"torch_arcsinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arcsinh — torch_arcsinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arcsinh.html","id":"arcsinh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arcsinh(input, *, out=None) -> Tensor","title":"Arcsinh — torch_arcsinh","text":"Alias torch_asinh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctan — torch_arctan","title":"Arctan — torch_arctan","text":"Arctan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctan — torch_arctan","text":"","code":"torch_arctan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arctan — torch_arctan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctan.html","id":"arctan-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arctan(input, *, out=None) -> Tensor","title":"Arctan — torch_arctan","text":"Alias torch_atan().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctanh — torch_arctanh","title":"Arctanh — torch_arctanh","text":"Arctanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctanh — torch_arctanh","text":"","code":"torch_arctanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arctanh — torch_arctanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_arctanh.html","id":"arctanh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"arctanh(input, *, out=None) -> Tensor","title":"Arctanh — torch_arctanh","text":"Alias torch_atanh().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":null,"dir":"Reference","previous_headings":"","what":"Argmax — torch_argmax","title":"Argmax — torch_argmax","text":"Argmax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argmax — torch_argmax","text":"self (Tensor) input tensor. dim (int) dimension reduce. NULL, argmax flattened input returned. keepdim (bool) whether output tensor dim retained . Ignored dim=NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"argmax-input-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmax(input) -> LongTensor","title":"Argmax — torch_argmax","text":"Returns indices maximum value elements input tensor. second value returned torch_max. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"argmax-input-dim-keepdim-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmax(input, dim, keepdim=False) -> LongTensor","title":"Argmax — torch_argmax","text":"Returns indices maximum values tensor across dimension. second value returned torch_max. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argmax — torch_argmax","text":"","code":"if (torch_is_installed()) {  if (FALSE) { # \\dontrun{ a = torch_randn(c(4, 4)) a torch_argmax(a) } # }   a = torch_randn(c(4, 4)) a torch_argmax(a, dim=1) } #> torch_tensor #>  4 #>  1 #>  3 #>  4 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Argmin — torch_argmin","title":"Argmin — torch_argmin","text":"Argmin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argmin — torch_argmin","text":"self (Tensor) input tensor. dim (int) dimension reduce. NULL, argmin flattened input returned. keepdim (bool) whether output tensor dim retained . Ignored dim=NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"argmin-input-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmin(input) -> LongTensor","title":"Argmin — torch_argmin","text":"Returns indices minimum value elements input tensor. second value returned torch_min. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"argmin-input-dim-keepdim-false-out-null-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argmin(input, dim, keepdim=False, out=NULL) -> LongTensor","title":"Argmin — torch_argmin","text":"Returns indices minimum values tensor across dimension. second value returned torch_min. See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argmin — torch_argmin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 4)) a torch_argmin(a)   a = torch_randn(c(4, 4)) a torch_argmin(a, dim=1) } #> torch_tensor #>  4 #>  1 #>  2 #>  3 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":null,"dir":"Reference","previous_headings":"","what":"Argsort — torch_argsort","title":"Argsort — torch_argsort","text":"Argsort","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Argsort — torch_argsort","text":"","code":"torch_argsort(self, dim = -1L, descending = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Argsort — torch_argsort","text":"self (Tensor) input tensor. dim (int, optional) dimension sort along descending (bool, optional) controls sorting order (ascending descending)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"argsort-input-dim-descending-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"argsort(input, dim=-1, descending=False) -> LongTensor","title":"Argsort — torch_argsort","text":"Returns indices sort tensor along given dimension ascending order value. second value returned torch_sort.  See documentation exact semantics method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_argsort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Argsort — torch_argsort","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 4)) a torch_argsort(a, dim=1) } #> torch_tensor #>  1  3  1  4 #>  2  4  2  1 #>  4  1  3  2 #>  3  2  4  3 #> [ CPULongType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":null,"dir":"Reference","previous_headings":"","what":"As_strided — torch_as_strided","title":"As_strided — torch_as_strided","text":"As_strided","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"As_strided — torch_as_strided","text":"","code":"torch_as_strided(self, size, stride, storage_offset = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"As_strided — torch_as_strided","text":"self (Tensor) input tensor. size (tuple ints) shape output tensor stride (tuple ints) stride output tensor storage_offset (int, optional) offset underlying storage output tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"as-strided-input-size-stride-storage-offset-gt-tensor-","dir":"Reference","previous_headings":"","what":"as_strided(input, size, stride, storage_offset=0) -> Tensor","title":"As_strided — torch_as_strided","text":"Create view existing torch_Tensor input specified size, stride storage_offset.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"As_strided — torch_as_strided","text":"one element created tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":"Many PyTorch functions, which return a view of a tensor, are internally implemented with this function. Those functions, like `torch_Tensor.expand`, are easier to read and are therefore more advisable to use."},{"path":"https://torch.mlverse.org/docs/reference/torch_as_strided.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"As_strided — torch_as_strided","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 3)) x t = torch_as_strided(x, list(2, 2), list(1, 2)) t t = torch_as_strided(x, list(2, 2), list(1, 2), 1) t } #> torch_tensor #> -1.1008  1.8645 #> -0.5921  1.5321 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":null,"dir":"Reference","previous_headings":"","what":"Asin — torch_asin","title":"Asin — torch_asin","text":"Asin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asin — torch_asin","text":"","code":"torch_asin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asin — torch_asin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"asin-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"asin(input, out=NULL) -> Tensor","title":"Asin — torch_asin","text":"Returns new tensor arcsine  elements input. $$     \\mbox{}_{} = \\sin^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asin — torch_asin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_asin(a) } #> torch_tensor #> 0.01 * #>  7.9097 #> -9.9271 #>     nan #>     nan #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Asinh — torch_asinh","title":"Asinh — torch_asinh","text":"Asinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asinh — torch_asinh","text":"","code":"torch_asinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asinh — torch_asinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"asinh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"asinh(input, *, out=None) -> Tensor","title":"Asinh — torch_asinh","text":"Returns new tensor inverse hyperbolic sine elements input. $$     \\mbox{}_{} = \\sinh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_asinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asinh — torch_asinh","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(4)) a torch_asinh(a) } #> torch_tensor #> -0.2788 #> -0.3991 #>  1.0148 #>  0.3608 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":null,"dir":"Reference","previous_headings":"","what":"Atan — torch_atan","title":"Atan — torch_atan","text":"Atan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atan — torch_atan","text":"","code":"torch_atan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atan — torch_atan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"atan-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"atan(input, out=NULL) -> Tensor","title":"Atan — torch_atan","text":"Returns new tensor arctangent  elements input. $$     \\mbox{}_{} = \\tan^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atan — torch_atan","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_atan(a) } #> torch_tensor #> -0.3648 #>  0.8036 #> -0.5295 #>  0.7657 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":null,"dir":"Reference","previous_headings":"","what":"Atan2 — torch_atan2","title":"Atan2 — torch_atan2","text":"Atan2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atan2 — torch_atan2","text":"","code":"torch_atan2(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atan2 — torch_atan2","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"atan-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"atan2(input, other, out=NULL) -> Tensor","title":"Atan2 — torch_atan2","text":"Element-wise arctangent \\(\\mbox{input}_{} / \\mbox{}_{}\\) consideration quadrant. Returns new tensor signed angles radians vector \\((\\mbox{}_{}, \\mbox{input}_{})\\) vector \\((1, 0)\\). (Note \\(\\mbox{}_{}\\), second parameter, x-coordinate, \\(\\mbox{input}_{}\\), first parameter, y-coordinate.) shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atan2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atan2 — torch_atan2","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_atan2(a, torch_randn(c(4))) } #> torch_tensor #>  1.1075 #> -0.4082 #>  0.4862 #>  1.9404 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Atanh — torch_atanh","title":"Atanh — torch_atanh","text":"Atanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atanh — torch_atanh","text":"","code":"torch_atanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atanh — torch_atanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Atanh — torch_atanh","text":"domain inverse hyperbolic tangent (-1, 1) values outside range mapped NaN, except values 1 -1 output mapped +/-INF respectively. $$     \\mbox{}_{} = \\tanh^{-1}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"atanh-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"atanh(input, *, out=None) -> Tensor","title":"Atanh — torch_atanh","text":"Returns new tensor inverse hyperbolic tangent elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atanh — torch_atanh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4))$uniform_(-1, 1) a torch_atanh(a) } #> torch_tensor #>  2.6633 #> -0.3951 #> -0.2666 #>  0.7613 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_1d — torch_atleast_1d","title":"Atleast_1d — torch_atleast_1d","text":"Returns 1-dimensional view input tensor zero dimensions. Input tensors one dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_1d — torch_atleast_1d","text":"","code":"torch_atleast_1d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_1d — torch_atleast_1d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atleast_1d — torch_atleast_1d","text":"","code":"if (torch_is_installed()) {  x <- torch_randn(c(2)) x torch_atleast_1d(x) x <- torch_tensor(1.) x torch_atleast_1d(x) x <- torch_tensor(0.5) y <- torch_tensor(1.) torch_atleast_1d(list(x,y)) } #> [[1]] #> torch_tensor #>  0.5000 #> [ CPUFloatType{1} ] #>  #> [[2]] #> torch_tensor #>  1 #> [ CPUFloatType{1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_2d — torch_atleast_2d","title":"Atleast_2d — torch_atleast_2d","text":"Returns 2-dimensional view input tensor zero dimensions. Input tensors two dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_2d — torch_atleast_2d","text":"","code":"torch_atleast_2d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_2d — torch_atleast_2d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Atleast_2d — torch_atleast_2d","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(1.) x torch_atleast_2d(x) x <- torch_randn(c(2,2)) x torch_atleast_2d(x) x <- torch_tensor(0.5) y <- torch_tensor(1.) torch_atleast_2d(list(x,y)) } #> [[1]] #> torch_tensor #>  0.5000 #> [ CPUFloatType{1,1} ] #>  #> [[2]] #> torch_tensor #>  1 #> [ CPUFloatType{1,1} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Atleast_3d — torch_atleast_3d","title":"Atleast_3d — torch_atleast_3d","text":"Returns 3-dimensional view input tensor zero dimensions. Input tensors three dimensions returned -.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Atleast_3d — torch_atleast_3d","text":"","code":"torch_atleast_3d(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_atleast_3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Atleast_3d — torch_atleast_3d","text":"self (Tensor list Tensors)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Avg_pool1d — torch_avg_pool1d","title":"Avg_pool1d — torch_avg_pool1d","text":"Avg_pool1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avg_pool1d — torch_avg_pool1d","text":"","code":"torch_avg_pool1d(   self,   kernel_size,   stride = list(),   padding = 0L,   ceil_mode = FALSE,   count_include_pad = TRUE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Avg_pool1d — torch_avg_pool1d","text":"self input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) kernel_size size window. Can single number tuple (kW,) stride stride window. Can single number tuple (sW,). Default: kernel_size padding implicit zero paddings sides input. Can single number tuple (padW,). Default: 0 ceil_mode TRUE, use ceil instead floor compute output shape. Default: FALSE count_include_pad TRUE, include zero-padding averaging calculation. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_avg_pool1d.html","id":"avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -> Tensor","title":"Avg_pool1d — torch_avg_pool1d","text":"Applies 1D average pooling input signal composed several input planes. See nn_avg_pool1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Baddbmm — torch_baddbmm","title":"Baddbmm — torch_baddbmm","text":"Baddbmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baddbmm — torch_baddbmm","text":"","code":"torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baddbmm — torch_baddbmm","text":"self (Tensor) tensor added batch1 (Tensor) first batch matrices multiplied batch2 (Tensor) second batch matrices multiplied beta (Number, optional) multiplier input (\\(\\beta\\)) alpha (Number, optional) multiplier \\(\\mbox{batch1} \\mathbin{@} \\mbox{batch2}\\) (\\(\\alpha\\))","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> Tensor","title":"Baddbmm — torch_baddbmm","text":"Performs batch matrix-matrix product matrices batch1 batch2. input added final result. batch1 batch2 must 3-D tensors containing number matrices. batch1 \\((b \\times n \\times m)\\) tensor, batch2 \\((b \\times m \\times p)\\) tensor, input must broadcastable  \\((b \\times n \\times p)\\) tensor \\((b \\times n \\times p)\\) tensor. alpha beta mean scaling factors used torch_addbmm. $$     \\mbox{}_i = \\beta\\ \\mbox{input}_i + \\alpha\\ (\\mbox{batch1}_i \\mathbin{@} \\mbox{batch2}_i) $$ inputs type FloatTensor DoubleTensor, arguments beta alpha must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_baddbmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Baddbmm — torch_baddbmm","text":"","code":"if (torch_is_installed()) {  M = torch_randn(c(10, 3, 5)) batch1 = torch_randn(c(10, 3, 4)) batch2 = torch_randn(c(10, 4, 5)) torch_baddbmm(M, batch1, batch2) } #> torch_tensor #> (1,.,.) =  #>  -4.6742 -0.7577  2.6754  0.8191 -0.7530 #>   4.3995 -1.2198 -2.3838 -1.5699 -0.1833 #>  -2.8331 -1.6828 -0.1383  2.6218  0.2945 #>  #> (2,.,.) =  #>  -4.0776  2.4758 -2.4423  0.0407  1.0181 #>   0.9602 -0.7632 -1.9180 -1.5237  2.1434 #>  -4.3002 -4.8091 -1.5533  1.2892  0.8208 #>  #> (3,.,.) =  #>   1.8408 -0.2855 -1.1812 -0.8825  0.3052 #>  -2.4125 -1.0249  2.2428  1.0103  1.4386 #>  -0.8179  2.3056  0.6533 -0.3723  0.8718 #>  #> (4,.,.) =  #>    4.3023  10.9624   1.4803   0.7257  -3.6036 #>    2.6867   4.3710   2.7404  -0.6179  -0.0689 #>    3.8225   4.8994   2.5229   0.5117  -1.4507 #>  #> (5,.,.) =  #>   0.0914 -1.7040  0.5731 -3.0537 -2.3071 #>  -1.6430 -0.9083 -0.2146 -1.2739  1.5534 #>  -0.8725  1.0120 -0.3942 -0.9003 -3.1054 #>  #> (6,.,.) =  #>  -1.5916 -2.2534  3.0997  1.6804  3.2010 #>  -0.3672 -0.5168 -3.0351  3.4030  5.5081 #>   1.6120 -1.9197 -4.6837  2.6320  1.9936 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Bartlett_window — torch_bartlett_window","title":"Bartlett_window — torch_bartlett_window","text":"Bartlett_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bartlett_window — torch_bartlett_window","text":"","code":"torch_bartlett_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bartlett_window — torch_bartlett_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bartlett_window — torch_bartlett_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_bartlett_window.html","id":"bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Bartlett_window — torch_bartlett_window","text":"Bartlett window function. $$     w[n] = 1 - \\left| \\frac{2n}{N-1} - 1 \\right| = \\left\\{ \\begin{array}{ll}         \\frac{2n}{N - 1} & \\mbox{} 0 \\leq n \\leq \\frac{N - 1}{2} \\\\         2 - \\frac{2n}{N - 1} & \\mbox{} \\frac{N - 1}{2} < n < N \\\\     \\end{array}     \\right. , $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_bartlett_window(L, periodic=TRUE) equal torch_bartlett_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":null,"dir":"Reference","previous_headings":"","what":"Bernoulli — torch_bernoulli","title":"Bernoulli — torch_bernoulli","text":"Bernoulli","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bernoulli — torch_bernoulli","text":"","code":"torch_bernoulli(self, p, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bernoulli — torch_bernoulli","text":"self (Tensor) input tensor probability values Bernoulli distribution p (Number) probability value. p passed used instead values self tensor. generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"bernoulli-input-generator-null-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bernoulli(input, *, generator=NULL, out=NULL) -> Tensor","title":"Bernoulli — torch_bernoulli","text":"Draws binary random numbers (0 1) Bernoulli distribution. input tensor tensor containing probabilities used drawing binary random number. Hence, values input range: \\(0 \\leq \\mbox{input}_i \\leq 1\\). \\(\\mbox{}^{th}\\) element output tensor draw value \\(1\\) according \\(\\mbox{}^{th}\\) probability value given input. $$     \\mbox{}_{} \\sim \\mathrm{Bernoulli}(p = \\mbox{input}_{}) $$ returned tensor values 0 1 shape input. can integral dtype, input must floating point dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bernoulli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bernoulli — torch_bernoulli","text":"","code":"if (torch_is_installed()) {  a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1) a torch_bernoulli(a) a = torch_ones(c(3, 3)) # probability of drawing \"1\" is 1 torch_bernoulli(a) a = torch_zeros(c(3, 3)) # probability of drawing \"1\" is 0 torch_bernoulli(a) } #> torch_tensor #>  0  0  0 #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":null,"dir":"Reference","previous_headings":"","what":"Bincount — torch_bincount","title":"Bincount — torch_bincount","text":"Bincount","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bincount — torch_bincount","text":"self (Tensor) 1-d int tensor weights (Tensor) optional, weight value input tensor.        size input tensor. minlength (int) optional, minimum number bins. non-negative.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"bincount-input-weights-null-minlength-gt-tensor-","dir":"Reference","previous_headings":"","what":"bincount(input, weights=NULL, minlength=0) -> Tensor","title":"Bincount — torch_bincount","text":"Count frequency value array non-negative ints. number bins (size 1) one larger largest value input unless input empty, case result tensor size 0. minlength specified, number bins least minlength input empty, result tensor size minlength filled zeros. n value position , [n] += weights[] weights specified else [n] += 1. .. include:: cuda_deterministic.rst","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bincount.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bincount — torch_bincount","text":"","code":"if (torch_is_installed()) {  input = torch_randint(1, 8, list(5), dtype=torch_int64()) weights = torch_linspace(0, 1, steps=5) input weights torch_bincount(input, weights) input$bincount(weights) } #> torch_tensor #>  0.0000 #>  0.5000 #>  0.0000 #>  1.2500 #>  0.0000 #>  0.7500 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_and — torch_bitwise_and","title":"Bitwise_and — torch_bitwise_and","text":"Bitwise_and","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_and — torch_bitwise_and","text":"","code":"torch_bitwise_and(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_and — torch_bitwise_and","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_and.html","id":"bitwise-and-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_and(input, other, out=NULL) -> Tensor","title":"Bitwise_and — torch_bitwise_and","text":"Computes bitwise input . input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_not — torch_bitwise_not","title":"Bitwise_not — torch_bitwise_not","text":"Bitwise_not","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_not — torch_bitwise_not","text":"","code":"torch_bitwise_not(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_not — torch_bitwise_not","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_not.html","id":"bitwise-not-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_not(input, out=NULL) -> Tensor","title":"Bitwise_not — torch_bitwise_not","text":"Computes bitwise given input tensor. input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_or — torch_bitwise_or","title":"Bitwise_or — torch_bitwise_or","text":"Bitwise_or","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_or — torch_bitwise_or","text":"","code":"torch_bitwise_or(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_or — torch_bitwise_or","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_or.html","id":"bitwise-or-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_or(input, other, out=NULL) -> Tensor","title":"Bitwise_or — torch_bitwise_or","text":"Computes bitwise input . input tensor must integral Boolean types. bool tensors, computes logical .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Bitwise_xor — torch_bitwise_xor","title":"Bitwise_xor — torch_bitwise_xor","text":"Bitwise_xor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bitwise_xor — torch_bitwise_xor","text":"","code":"torch_bitwise_xor(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bitwise_xor — torch_bitwise_xor","text":"self NA first input tensor NA second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bitwise_xor.html","id":"bitwise-xor-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bitwise_xor(input, other, out=NULL) -> Tensor","title":"Bitwise_xor — torch_bitwise_xor","text":"Computes bitwise XOR input . input tensor must integral Boolean types. bool tensors, computes logical XOR.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Blackman_window — torch_blackman_window","title":"Blackman_window — torch_blackman_window","text":"Blackman_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blackman_window — torch_blackman_window","text":"","code":"torch_blackman_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blackman_window — torch_blackman_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Blackman_window — torch_blackman_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_blackman_window.html","id":"blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Blackman_window — torch_blackman_window","text":"Blackman window function. $$     w[n] = 0.42 - 0.5 \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right) + 0.08 \\cos \\left( \\frac{4 \\pi n}{N - 1} \\right) $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_blackman_window(L, periodic=TRUE) equal torch_blackman_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Block_diag — torch_block_diag","title":"Block_diag — torch_block_diag","text":"Create block diagonal matrix provided tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Block_diag — torch_block_diag","text":"","code":"torch_block_diag(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Block_diag — torch_block_diag","text":"tensors (list tensors) One tensors 0, 1, 2 dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_block_diag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Block_diag — torch_block_diag","text":"","code":"if (torch_is_installed()) {  A <- torch_tensor(rbind(c(0, 1), c(1, 0))) B <- torch_tensor(rbind(c(3, 4, 5), c(6, 7, 8))) C <- torch_tensor(7) D <- torch_tensor(c(1, 2, 3)) E <- torch_tensor(rbind(4, 5, 6)) torch_block_diag(list(A, B, C, D, E)) } #> torch_tensor #>  0  1  0  0  0  0  0  0  0  0 #>  1  0  0  0  0  0  0  0  0  0 #>  0  0  3  4  5  0  0  0  0  0 #>  0  0  6  7  8  0  0  0  0  0 #>  0  0  0  0  0  7  0  0  0  0 #>  0  0  0  0  0  0  1  2  3  0 #>  0  0  0  0  0  0  0  0  0  4 #>  0  0  0  0  0  0  0  0  0  5 #>  0  0  0  0  0  0  0  0  0  6 #> [ CPUFloatType{9,10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Bmm — torch_bmm","title":"Bmm — torch_bmm","text":"Bmm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bmm — torch_bmm","text":"","code":"torch_bmm(self, mat2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bmm — torch_bmm","text":"self (Tensor) first batch matrices multiplied mat2 (Tensor) second batch matrices multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Bmm — torch_bmm","text":"function broadcast . broadcasting matrix products, see torch_matmul.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"bmm-input-mat-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"bmm(input, mat2, out=NULL) -> Tensor","title":"Bmm — torch_bmm","text":"Performs batch matrix-matrix product matrices stored input mat2. input mat2 must 3-D tensors containing number matrices. input \\((b \\times n \\times m)\\) tensor, mat2 \\((b \\times m \\times p)\\) tensor, \\((b \\times n \\times p)\\) tensor. $$     \\mbox{}_i = \\mbox{input}_i \\mathbin{@} \\mbox{mat2}_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bmm — torch_bmm","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(10, 3, 4)) mat2 = torch_randn(c(10, 4, 5)) res = torch_bmm(input, mat2) res } #> torch_tensor #> (1,.,.) =  #>   0.3372 -0.9793 -0.9178  1.6752 -3.9694 #>   0.6731  0.4739  0.6238 -0.8550  1.3649 #>   1.2408 -1.9492  4.0812 -0.5648  1.4745 #>  #> (2,.,.) =  #>   0.4185 -1.6425  0.8958 -0.5948 -0.4059 #>   0.3336 -0.8206  1.6552  1.8691 -3.3935 #>  -1.9448 -1.6547  1.8130 -3.0308  0.8444 #>  #> (3,.,.) =  #>  -1.7618 -0.0694  0.8885 -0.2286  4.5873 #>   2.8709  1.3536  0.6855  1.7678 -1.4571 #>  -1.9785  2.2843  2.3418 -2.1916  5.1376 #>  #> (4,.,.) =  #>   1.0374  1.4050  0.1721  0.9362  1.3186 #>  -1.2310  0.5466  3.3942  2.2200 -3.1539 #>   1.9582 -0.0251 -0.6099 -0.8545 -0.6797 #>  #> (5,.,.) =  #>   0.7116  1.7785 -0.6294  4.2205 -0.1129 #>   0.2466  0.0895 -1.5727  5.1407  0.3615 #>   0.1041 -1.8901  1.4343 -5.1313 -0.3243 #>  #> (6,.,.) =  #>  -0.9272 -0.6083  1.1190  2.8363 -0.7331 #>  -0.1927  0.3642  0.0112  0.2584  0.7779 #>   0.9377 -0.2305 -2.8036 -3.4465 -0.5447 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":null,"dir":"Reference","previous_headings":"","what":"Broadcast_tensors — torch_broadcast_tensors","title":"Broadcast_tensors — torch_broadcast_tensors","text":"Broadcast_tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broadcast_tensors — torch_broadcast_tensors","text":"","code":"torch_broadcast_tensors(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broadcast_tensors — torch_broadcast_tensors","text":"tensors list containing number tensors type","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"broadcast-tensors-tensors-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"broadcast_tensors(tensors) -> List of Tensors","title":"Broadcast_tensors — torch_broadcast_tensors","text":"Broadcasts given tensors according broadcasting-semantics.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_broadcast_tensors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broadcast_tensors — torch_broadcast_tensors","text":"","code":"if (torch_is_installed()) {  x = torch_arange(0, 3)$view(c(1, 4)) y = torch_arange(0, 2)$view(c(3, 1)) out = torch_broadcast_tensors(list(x, y)) out[[1]] } #> torch_tensor #>  0  1  2  3 #>  0  1  2  3 #>  0  1  2  3 #> [ CPUFloatType{3,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucketize — torch_bucketize","title":"Bucketize — torch_bucketize","text":"Bucketize","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucketize — torch_bucketize","text":"","code":"torch_bucketize(self, boundaries, out_int32 = FALSE, right = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucketize — torch_bucketize","text":"self (Tensor Scalar) N-D tensor Scalar containing search value(s). boundaries (Tensor) 1-D tensor, must contain monotonically increasing sequence. out_int32 (bool, optional) – indicate output data type. torch_int32() True, torch_int64() otherwise. Default value FALSE, .e. default output data type torch_int64(). right (bool, optional) – False, return first suitable location found. True, return last index. suitable index found, return 0 non-numerical value (eg. nan, inf) size boundaries (one pass last index). words, False, gets lower bound index value input boundaries. True, gets upper bound index instead. Default value False.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"bucketize-input-boundaries-out-int-false-right-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor","title":"Bucketize — torch_bucketize","text":"Returns indices buckets value input belongs, boundaries buckets set boundaries. Return new tensor size input. right FALSE (default), left boundary closed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_bucketize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucketize — torch_bucketize","text":"","code":"if (torch_is_installed()) {  boundaries <- torch_tensor(c(1, 3, 5, 7, 9)) boundaries v <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9))) v torch_bucketize(v, boundaries) torch_bucketize(v, boundaries, right=TRUE) } #> torch_tensor #>  2  3  5 #>  2  3  5 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":null,"dir":"Reference","previous_headings":"","what":"Can_cast — torch_can_cast","title":"Can_cast — torch_can_cast","text":"Can_cast","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Can_cast — torch_can_cast","text":"","code":"torch_can_cast(from_, to)"},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Can_cast — torch_can_cast","text":"from_ (dtype) original torch_dtype. (dtype) target torch_dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"can-cast-from-to-gt-bool-","dir":"Reference","previous_headings":"","what":"can_cast(from, to) -> bool","title":"Can_cast — torch_can_cast","text":"Determines type conversion allowed PyTorch casting rules described type promotion documentation .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_can_cast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Can_cast — torch_can_cast","text":"","code":"if (torch_is_installed()) {  torch_can_cast(torch_double(), torch_float()) torch_can_cast(torch_float(), torch_int()) } #> [1] FALSE"},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Cartesian_prod — torch_cartesian_prod","title":"Cartesian_prod — torch_cartesian_prod","text":"cartesian product given sequence tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cartesian_prod — torch_cartesian_prod","text":"","code":"torch_cartesian_prod(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cartesian_prod — torch_cartesian_prod","text":"tensors list containing number 1 dimensional tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cartesian_prod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cartesian_prod — torch_cartesian_prod","text":"","code":"if (torch_is_installed()) {  a = c(1, 2, 3) b = c(4, 5) tensor_a = torch_tensor(a) tensor_b = torch_tensor(b) torch_cartesian_prod(list(tensor_a, tensor_b)) } #> torch_tensor #>  1  4 #>  1  5 #>  2  4 #>  2  5 #>  3  4 #>  3  5 #> [ CPUFloatType{6,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":null,"dir":"Reference","previous_headings":"","what":"Cat — torch_cat","title":"Cat — torch_cat","text":"Cat","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cat — torch_cat","text":"","code":"torch_cat(tensors, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cat — torch_cat","text":"tensors (sequence Tensors) python sequence tensors type.        Non-empty tensors provided must shape, except        cat dimension. dim (int, optional) dimension tensors concatenated","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"cat-tensors-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cat(tensors, dim=0, out=NULL) -> Tensor","title":"Cat — torch_cat","text":"Concatenates given sequence seq tensors given dimension. tensors must either shape (except concatenating dimension) empty. torch_cat can seen inverse operation torch_split() torch_chunk. torch_cat can best understood via examples.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cat — torch_cat","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2, 3)) x torch_cat(list(x, x, x), 1) torch_cat(list(x, x, x), 2) } #> torch_tensor #> -0.0014 -1.5512 -1.0807 -0.0014 -1.5512 -1.0807 -0.0014 -1.5512 -1.0807 #>  0.5571 -0.8866 -1.3969  0.5571 -0.8866 -1.3969  0.5571 -0.8866 -1.3969 #> [ CPUFloatType{2,9} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cdist — torch_cdist","title":"Cdist — torch_cdist","text":"Cdist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cdist — torch_cdist","text":"","code":"torch_cdist(x1, x2, p = 2L, compute_mode = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cdist — torch_cdist","text":"x1 (Tensor) input tensor shape \\(B \\times P \\times M\\). x2 (Tensor) input tensor shape \\(B \\times R \\times M\\). p NA p value p-norm distance calculate vector pair        \\(\\[0, \\infty]\\). compute_mode NA 'use_mm_for_euclid_dist_if_necessary' - use matrix multiplication approach calculate        euclidean distance (p = 2) P > 25 R > 25        'use_mm_for_euclid_dist' - always use matrix multiplication approach calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - never use matrix multiplication approach calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cdist.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Cdist — torch_cdist","text":"Computes batched p-norm distance pair two collections row vectors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":null,"dir":"Reference","previous_headings":"","what":"Ceil — torch_ceil","title":"Ceil — torch_ceil","text":"Ceil","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ceil — torch_ceil","text":"","code":"torch_ceil(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ceil — torch_ceil","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ceil-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ceil(input, out=NULL) -> Tensor","title":"Ceil — torch_ceil","text":"Returns new tensor ceil elements input, smallest integer greater equal element. $$     \\mbox{}_{} = \\left\\lceil \\mbox{input}_{} \\right\\rceil = \\left\\lfloor \\mbox{input}_{} \\right\\rfloor + 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ceil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ceil — torch_ceil","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_ceil(a) } #> torch_tensor #> -1 #> -0 #> -0 #> -0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu — torch_celu","title":"Celu — torch_celu","text":"Celu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu — torch_celu","text":"","code":"torch_celu(self, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu — torch_celu","text":"self input tensor alpha alpha value CELU formulation. Default: 1.0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu.html","id":"celu-input-alpha-gt-tensor-","dir":"Reference","previous_headings":"","what":"celu(input, alpha=1.) -> Tensor","title":"Celu — torch_celu","text":"See nnf_celu() info.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Celu_ — torch_celu_","title":"Celu_ — torch_celu_","text":"Celu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Celu_ — torch_celu_","text":"","code":"torch_celu_(self, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Celu_ — torch_celu_","text":"self input tensor alpha alpha value CELU formulation. Default: 1.0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_celu_.html","id":"celu-input-alpha-gt-tensor-","dir":"Reference","previous_headings":"","what":"celu_(input, alpha=1.) -> Tensor","title":"Celu_ — torch_celu_","text":"-place version torch_celu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Chain_matmul — torch_chain_matmul","title":"Chain_matmul — torch_chain_matmul","text":"Chain_matmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chain_matmul — torch_chain_matmul","text":"","code":"torch_chain_matmul(matrices)"},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chain_matmul — torch_chain_matmul","text":"matrices (Tensors...) sequence 2 2-D tensors whose product determined.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Chain_matmul — torch_chain_matmul","text":"Returns matrix product \\(N\\) 2-D tensors. product efficiently computed using matrix chain order algorithm selects order incurs lowest cost terms arithmetic operations ([CLRS]_). Note since function compute product, \\(N\\) needs greater equal 2; equal 2 trivial matrix-matrix product returned. \\(N\\) 1, -op - original matrix returned .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chain_matmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Chain_matmul — torch_chain_matmul","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 4)) b = torch_randn(c(4, 5)) c = torch_randn(c(5, 6)) d = torch_randn(c(6, 7)) torch_chain_matmul(list(a, b, c, d)) } #> torch_tensor #> -12.2407  23.0458   2.8975  -6.9677   3.9015   1.8938  -0.3433 #>  -7.4472  14.6260   3.6509  -2.8358   5.9263  -0.0407   1.2311 #>  -9.6405  -5.4312   9.8422  27.1031  12.3670   9.2524   6.2635 #> [ CPUFloatType{3,7} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Channel_shuffle — torch_channel_shuffle","title":"Channel_shuffle — torch_channel_shuffle","text":"Channel_shuffle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Channel_shuffle — torch_channel_shuffle","text":"","code":"torch_channel_shuffle(self, groups)"},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Channel_shuffle — torch_channel_shuffle","text":"self (Tensor) input tensor groups (int) number groups divide channels rearrange.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"divide-the-channels-in-a-tensor-of-shape-","dir":"Reference","previous_headings":"","what":"Divide the channels in a tensor of shape","title":"Channel_shuffle — torch_channel_shuffle","text":"math:(*, C , H, W) : Divide channels tensor shape \\((*, C , H, W)\\) g groups rearrange \\((*, C \\frac g, g, H, W)\\), keeping original tensor shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_channel_shuffle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Channel_shuffle — torch_channel_shuffle","text":"","code":"if (torch_is_installed()) {  input <- torch_randn(c(1, 4, 2, 2)) print(input) output <- torch_channel_shuffle(input, 2) print(output) } #> torch_tensor #> (1,1,.,.) =  #>  -1.7662  0.9961 #>  -1.3459 -0.3866 #>  #> (1,2,.,.) =  #>  -0.0192 -0.6689 #>   1.0240  1.2698 #>  #> (1,3,.,.) =  #>  -0.1894  0.9790 #>   0.8418  0.3206 #>  #> (1,4,.,.) =  #>  -0.1993 -0.6290 #>  -0.7123 -0.5530 #> [ CPUFloatType{1,4,2,2} ] #> torch_tensor #> (1,1,.,.) =  #>  -1.7662  0.9961 #>  -1.3459 -0.3866 #>  #> (1,2,.,.) =  #>  -0.1894  0.9790 #>   0.8418  0.3206 #>  #> (1,3,.,.) =  #>  -0.0192 -0.6689 #>   1.0240  1.2698 #>  #> (1,4,.,.) =  #>  -0.1993 -0.6290 #>  -0.7123 -0.5530 #> [ CPUFloatType{1,4,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky — torch_cholesky","title":"Cholesky — torch_cholesky","text":"Cholesky","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky — torch_cholesky","text":"","code":"torch_cholesky(self, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky — torch_cholesky","text":"self (Tensor) input tensor \\(\\) size \\((*, n, n)\\) * zero batch dimensions consisting symmetric positive-definite matrices. upper (bool, optional) flag indicates whether return upper lower triangular matrix. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"cholesky-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky(input, upper=False, out=NULL) -> Tensor","title":"Cholesky — torch_cholesky","text":"Computes Cholesky decomposition symmetric positive-definite matrix \\(\\) batches symmetric positive-definite matrices. upper TRUE, returned matrix U upper-triangular, decomposition form: $$   = U^TU $$ upper FALSE, returned matrix L lower-triangular, decomposition form: $$     = LL^T $$ upper TRUE, \\(\\) batch symmetric positive-definite matrices, returned tensor composed upper-triangular Cholesky factors individual matrices. Similarly, upper FALSE, returned tensor composed lower-triangular Cholesky factors individual matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky — torch_cholesky","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) # make symmetric positive-definite l = torch_cholesky(a) a l torch_mm(l, l$t()) a = torch_randn(c(3, 2, 2)) if (FALSE) { # \\dontrun{ a = torch_matmul(a, a$transpose(-1, -2)) + 1e-03 # make symmetric positive-definite l = torch_cholesky(a) z = torch_matmul(l, l$transpose(-1, -2)) torch_max(torch_abs(z - a)) # Max non-zero } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky_inverse — torch_cholesky_inverse","title":"Cholesky_inverse — torch_cholesky_inverse","text":"Cholesky_inverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky_inverse — torch_cholesky_inverse","text":"","code":"torch_cholesky_inverse(self, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky_inverse — torch_cholesky_inverse","text":"self (Tensor) input 2-D tensor \\(u\\), upper lower triangular           Cholesky factor upper (bool, optional) whether return lower (default) upper triangular matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"cholesky-inverse-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky_inverse(input, upper=False, out=NULL) -> Tensor","title":"Cholesky_inverse — torch_cholesky_inverse","text":"Computes inverse symmetric positive-definite matrix \\(\\) using Cholesky factor \\(u\\): returns matrix inv. inverse computed using LAPACK routines dpotri spotri (corresponding MAGMA routines). upper FALSE, \\(u\\) lower triangular returned tensor $$     inv = (uu^{{T}})^{{-1}} $$ upper TRUE provided, \\(u\\) upper triangular returned tensor $$     inv = (u^T u)^{{-1}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky_inverse — torch_cholesky_inverse","text":"","code":"if (torch_is_installed()) {  if (FALSE) { # \\dontrun{ a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) + 1e-05 * torch_eye(3) # make symmetric positive definite u = torch_cholesky(a) a torch_cholesky_inverse(u) a$inverse() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Cholesky_solve — torch_cholesky_solve","title":"Cholesky_solve — torch_cholesky_solve","text":"Cholesky_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cholesky_solve — torch_cholesky_solve","text":"","code":"torch_cholesky_solve(self, input2, upper = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cholesky_solve — torch_cholesky_solve","text":"self (Tensor) input matrix \\(b\\) size \\((*, m, k)\\),                \\(*\\) zero batch dimensions input2 (Tensor) input matrix \\(u\\) size \\((*, m, m)\\),                \\(*\\) zero batch dimensions composed                upper lower triangular Cholesky factor upper (bool, optional) whether consider Cholesky factor                            lower upper triangular matrix. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"cholesky-solve-input-input-upper-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cholesky_solve(input, input2, upper=False, out=NULL) -> Tensor","title":"Cholesky_solve — torch_cholesky_solve","text":"Solves linear system equations positive semidefinite matrix inverted given Cholesky factor matrix \\(u\\). upper FALSE, \\(u\\) lower triangular c returned : $$     c = (u u^T)^{{-1}} b $$ upper TRUE provided, \\(u\\) upper triangular c returned : $$     c = (u^T u)^{{-1}} b $$ torch_cholesky_solve(b, u) can take 2D inputs b, u inputs batches 2D matrices. inputs batches, returns batched outputs c","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cholesky_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cholesky_solve — torch_cholesky_solve","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a = torch_mm(a, a$t()) # make symmetric positive definite u = torch_cholesky(a) a b = torch_randn(c(3, 2)) b torch_cholesky_solve(b, u) torch_mm(a$inverse(), b) } #> torch_tensor #>  0.2588 -0.0557 #> -0.1410  0.0636 #> -0.3984  0.9358 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":null,"dir":"Reference","previous_headings":"","what":"Chunk — torch_chunk","title":"Chunk — torch_chunk","text":"Chunk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chunk — torch_chunk","text":"","code":"torch_chunk(self, chunks, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chunk — torch_chunk","text":"self (Tensor) tensor split chunks (int) number chunks return dim (int) dimension along split tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_chunk.html","id":"chunk-input-chunks-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"chunk(input, chunks, dim=0) -> List of Tensors","title":"Chunk — torch_chunk","text":"Splits tensor specific number chunks. chunk view input tensor. Last chunk smaller tensor size along given dimension dim divisible chunks.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":null,"dir":"Reference","previous_headings":"","what":"Clamp — torch_clamp","title":"Clamp — torch_clamp","text":"Clamp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clamp — torch_clamp","text":"","code":"torch_clamp(self, min = NULL, max = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clamp — torch_clamp","text":"self (Tensor) input tensor. min (Number) lower-bound range clamped max (Number) upper-bound range clamped ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-min-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, min, max, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamp elements input range [ min, max ] return resulting tensor: $$     y_i = \\left\\{ \\begin{array}{ll}         \\mbox{min} & \\mbox{} x_i < \\mbox{min} \\\\         x_i & \\mbox{} \\mbox{min} \\leq x_i \\leq \\mbox{max} \\\\         \\mbox{max} & \\mbox{} x_i > \\mbox{max}     \\end{array}     \\right. $$ input type FloatTensor DoubleTensor, args min max must real numbers, otherwise integers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-min-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, *, min, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamps elements input larger equal min. input type FloatTensor DoubleTensor, value real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"clamp-input-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"clamp(input, *, max, out=NULL) -> Tensor","title":"Clamp — torch_clamp","text":"Clamps elements input smaller equal max. input type FloatTensor DoubleTensor, value real number, otherwise integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clamp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clamp — torch_clamp","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_clamp(a, min=-0.5, max=0.5)   a = torch_randn(c(4)) a torch_clamp(a, min=0.5)   a = torch_randn(c(4)) a torch_clamp(a, max=0.5) } #> torch_tensor #> -0.7677 #>  0.5000 #>  0.5000 #> -0.3665 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":null,"dir":"Reference","previous_headings":"","what":"Clip — torch_clip","title":"Clip — torch_clip","text":"Clip","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clip — torch_clip","text":"","code":"torch_clip(self, min = NULL, max = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clip — torch_clip","text":"self (Tensor) input tensor. min (Number) lower-bound range clamped max (Number) upper-bound range clamped ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clip.html","id":"clip-input-min-max-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"clip(input, min, max, *, out=None) -> Tensor","title":"Clip — torch_clip","text":"Alias torch_clamp().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":null,"dir":"Reference","previous_headings":"","what":"Clone — torch_clone","title":"Clone — torch_clone","text":"Clone","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clone — torch_clone","text":"","code":"torch_clone(self, memory_format = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clone — torch_clone","text":"self (Tensor) input tensor. memory_format torch memory format. see torch_preserve_format().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Clone — torch_clone","text":"function differentiable, gradients flow back result operation input. create tensor without autograd relationship input see Tensor$detach.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_clone.html","id":"clone-input-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"clone(input, *, memory_format=torch.preserve_format) -> Tensor","title":"Clone — torch_clone","text":"Returns copy input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Combinations — torch_combinations","title":"Combinations — torch_combinations","text":"Combinations","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combinations — torch_combinations","text":"","code":"torch_combinations(self, r = 2L, with_replacement = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combinations — torch_combinations","text":"self (Tensor) 1D vector. r (int, optional) number elements combine with_replacement (boolean, optional) whether allow duplication combination","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"combinations-input-r-with-replacement-false-gt-seq-","dir":"Reference","previous_headings":"","what":"combinations(input, r=2, with_replacement=False) -> seq","title":"Combinations — torch_combinations","text":"Compute combinations length \\(r\\) given tensor. behavior similar python's itertools.combinations with_replacement set False, itertools.combinations_with_replacement with_replacement set TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combinations — torch_combinations","text":"","code":"if (torch_is_installed()) {  a = c(1, 2, 3) tensor_a = torch_tensor(a) torch_combinations(tensor_a) torch_combinations(tensor_a, r=3) torch_combinations(tensor_a, with_replacement=TRUE) } #> torch_tensor #>  1  1 #>  1  2 #>  1  3 #>  2  2 #>  2  3 #>  3  3 #> [ CPUFloatType{6,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"Complex — torch_complex","title":"Complex — torch_complex","text":"Complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Complex — torch_complex","text":"","code":"torch_complex(real, imag)"},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Complex — torch_complex","text":"real (Tensor) real part complex tensor. Must float double. imag (Tensor) imaginary part complex tensor. Must dtype real.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"complex-real-imag-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"complex(real, imag, *, out=None) -> Tensor","title":"Complex — torch_complex","text":"Constructs complex tensor real part equal real imaginary part equal imag.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_complex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Complex — torch_complex","text":"","code":"if (torch_is_installed()) {  real <- torch_tensor(c(1, 2), dtype=torch_float32()) imag <- torch_tensor(c(3, 4), dtype=torch_float32()) z <- torch_complex(real, imag) z z$dtype } #> torch_ComplexFloat"},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":null,"dir":"Reference","previous_headings":"","what":"Conj — torch_conj","title":"Conj — torch_conj","text":"Conj","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conj — torch_conj","text":"","code":"torch_conj(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conj — torch_conj","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"conj-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"conj(input) -> Tensor","title":"Conj — torch_conj","text":"Computes element-wise conjugate given input tensor. $$     \\mbox{}_{} = conj(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conj — torch_conj","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ torch_conj(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv1d — torch_conv1d","title":"Conv1d — torch_conv1d","text":"Conv1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv1d — torch_conv1d","text":"","code":"torch_conv1d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv1d — torch_conv1d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      one-element tuple (sW,). Default: 1 padding implicit paddings sides input. Can      single number one-element tuple (padW,). Default: 0 dilation spacing kernel elements. Can single number      one-element tuple (dW,). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv1d — torch_conv1d","text":"Applies 1D convolution input signal composed several input planes. See nn_conv1d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv1d — torch_conv1d","text":"","code":"if (torch_is_installed()) {  filters = torch_randn(c(33, 16, 3)) inputs = torch_randn(c(20, 16, 50)) nnf_conv1d(inputs, filters) } #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 8  -4.2799  -4.4055  -7.6185  -3.6413  -7.5131   4.4503  12.4302  -0.6246 #>    1.7820  11.2815  15.3058   2.5497   7.3234   0.0027 -10.2557   6.1521 #>    6.7319  -7.6383   0.4122  -3.1282   0.5867 -13.2591  -3.8448  -5.2541 #>   -4.5234   5.8615   5.2040   2.6400   1.5504  -5.1288   7.8689   9.1657 #>    1.5921  -9.1527  -7.8412   6.6396   4.7601  -1.1723  -6.4607  -3.6257 #>    5.3255  -7.3895   0.9591  -0.0180  -4.1197   6.3202   6.3130   8.4903 #>   -3.0993   6.3749   8.7024  12.7569   6.9660  -6.5842   3.9414   0.4054 #>    7.9684   7.1598  -9.6735  -0.4639  -6.5569   2.3603   9.3425   2.4212 #>   -7.4006  -0.8673 -11.1602   3.0148   5.2737   1.9895  18.0035  -7.5815 #>    4.1510   0.0882  -0.8894   2.7853  -6.1612  -4.3666  15.8519   6.8035 #>    0.0551  -5.5998  -5.5718  -5.0208  -2.6086  -1.5798   3.4159   3.6958 #>    4.9326   3.3454   4.7694 -20.7927  -4.5259  -8.8677  -6.1775   0.2884 #>   -9.2756 -11.0536  10.1570  -8.5320   7.1997   7.5796 -13.5387   9.1202 #>    7.6942   2.6654   7.1931  -2.1197  -5.2211 -10.6524   1.2795  -2.3806 #>    6.9804   1.1636 -11.7477  -0.4564   5.4912   4.7557   7.8871  -5.5879 #>    1.3042  -0.0151  -3.8278  -9.6159 -13.3710  -5.5168   3.3523   4.1783 #>    6.7552 -10.0832  -3.2173   2.9196   7.2266   9.8423   1.2781  -2.5732 #>    6.1371  -0.0244   7.5641  -0.5036   4.0956  -6.5192  -2.8360   7.7989 #>    2.2528   9.6603  10.6675   8.4282  -6.9757 -14.2541  -8.1621  -6.9910 #>    8.8926  -2.7399 -10.1791   0.7602   0.3328  -1.9903   2.8115  -7.6494 #>    6.0896   0.4523  -3.8656  -1.9457  -2.5374  -2.1337   6.4756   1.8318 #>   -5.5806  -7.6214  -0.0251 -15.2107   0.9737  -5.4552  -1.9320   3.1738 #>   -0.2481   0.0856   7.8390   9.7375  -8.8718  -7.1358  -5.8276  -3.8133 #>   -3.9938 -11.1520   2.7522   5.5041  -8.3037  -6.4006  -7.6681  -6.7562 #>  -11.1665   5.2830  -4.3023  -1.4715   0.5730  -2.5748  -8.8762   6.7086 #>   -5.3044  -1.7767   2.1370   0.3335  -4.1531  -8.9707 -10.8596  -5.2838 #>    4.3987  -4.4244  -1.9234  -7.0934   2.9753   3.3248   1.1382   3.2683 #>   14.1857   9.1710   8.3437 -10.4215  -4.2963 -12.3506   5.2811   5.7495 #>    8.5927   4.8413  -5.3993 -11.4106 -12.2129  -2.8460   5.5478   3.4733 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{20,33,48} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv2d — torch_conv2d","title":"Conv2d — torch_conv2d","text":"Conv2d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv2d — torch_conv2d","text":"","code":"torch_conv2d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv2d — torch_conv2d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kH , kW)\\) bias optional bias tensor shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sH, sW). Default: 1 padding implicit paddings sides input. Can      single number tuple (padH, padW). Default: 0 dilation spacing kernel elements. Can single number      tuple (dH, dW). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv2d — torch_conv2d","text":"Applies 2D convolution input image composed several input planes. See nn_conv2d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv2d — torch_conv2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride filters = torch_randn(c(8,4,3,3)) inputs = torch_randn(c(1,4,5,5)) nnf_conv2d(inputs, filters, padding=1) } #> torch_tensor #> (1,1,.,.) =  #>   -1.6762  -1.4722  -1.6436   4.5126  -2.3594 #>   -6.1267  -0.6607   0.5919   9.5704  -3.9356 #>   -1.7615   6.3741  -2.8814   1.8651   2.6326 #>   -3.0181   6.3555  10.1203  -8.3985  -4.0173 #>    4.3111  -0.7174   2.8917  -3.1391  -8.9127 #>  #> (1,2,.,.) =  #>    8.9800   1.7654   7.7636  -6.3208  -8.4194 #>    5.8093   6.0707   9.6270  -3.9311   1.4963 #>   11.6820  -7.1384 -13.1026   0.6878   2.1252 #>   -2.5556  -5.7388   4.2203  -0.1439   0.9164 #>   -1.1838  -2.2137  -5.6184  -1.2443   0.6594 #>  #> (1,3,.,.) =  #>   0.7132  0.6092 -5.2210  7.3686  1.3817 #>  -0.1427  3.6031 -8.0537  2.5274 -4.5634 #>   4.1523  2.8623 -1.0692 -6.0115 -5.0826 #>   3.1303 -2.6635  0.9652 -4.9996 -1.5039 #>   4.5513 -6.7425  5.8636  0.2757 -5.1578 #>  #> (1,4,.,.) =  #>   -1.9208   7.2958   1.0516   2.1344   0.6837 #>    1.1177   2.2499   3.0147  -1.1611  -3.3016 #>   -3.3033  13.4223   2.4072  -9.5020   0.6424 #>    5.1416  -0.7583  -7.2147   8.3988   3.6592 #>   -1.6494   1.9372  -3.8303  -5.4349  -1.8648 #>  #> (1,5,.,.) =  #>   3.0466 -6.5147  2.8607 -4.0111  1.6115 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{1,8,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv3d — torch_conv3d","title":"Conv3d — torch_conv3d","text":"Conv3d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv3d — torch_conv3d","text":"","code":"torch_conv3d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   dilation = 1L,   groups = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv3d — torch_conv3d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kT , kH , kW)\\) bias optional bias tensor shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sT, sH, sW). Default: 1 padding implicit paddings sides input. Can      single number tuple (padT, padH, padW). Default: 0 dilation spacing kernel elements. Can single number      tuple (dT, dH, dW). Default: 1 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -> Tensor","title":"Conv3d — torch_conv3d","text":"Applies 3D convolution input image composed several input planes. See nn_conv3d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv3d — torch_conv3d","text":"","code":"if (torch_is_installed()) {  # filters = torch_randn(c(33, 16, 3, 3, 3)) # inputs = torch_randn(c(20, 16, 50, 10, 20)) # nnf_conv3d(inputs, filters) } #> NULL"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_tbc — torch_conv_tbc","title":"Conv_tbc — torch_conv_tbc","text":"Conv_tbc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_tbc — torch_conv_tbc","text":"","code":"torch_conv_tbc(self, weight, bias, pad = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_tbc — torch_conv_tbc","text":"self NA input tensor shape \\((\\mbox{sequence length} \\times batch \\times \\mbox{\\_channels})\\) weight NA filter shape (\\(\\mbox{kernel width} \\times \\mbox{\\_channels} \\times \\mbox{\\_channels}\\)) bias NA bias shape (\\(\\mbox{\\_channels}\\)) pad NA number timesteps pad. Default: 0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_tbc.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Conv_tbc — torch_conv_tbc","text":"Applies 1-dimensional sequence convolution input sequence. Input output dimensions (Time, Batch, Channels) - hence TBC.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose1d — torch_conv_transpose1d","title":"Conv_transpose1d — torch_conv_transpose1d","text":"Conv_transpose1d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose1d — torch_conv_transpose1d","text":"","code":"torch_conv_transpose1d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose1d — torch_conv_transpose1d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sW,). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padW,). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple (out_padW). Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dW,). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose1d — torch_conv_transpose1d","text":"Applies 1D transposed convolution operator input signal composed several input planes, sometimes also called \"deconvolution\". See nn_conv_transpose1d()  details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose1d — torch_conv_transpose1d","text":"","code":"if (torch_is_installed()) {  inputs = torch_randn(c(20, 16, 50)) weights = torch_randn(c(16, 33, 5)) nnf_conv_transpose1d(inputs, weights) } #> torch_tensor #> (1,.,.) =  #>  Columns 1 to 8  -2.7172  -5.5210   7.9374   3.0906  21.3580 -12.6189  -4.3258   5.3093 #>    1.3885 -12.0791 -11.1986   7.9128   2.8482   1.6760  -3.1183   7.3039 #>    2.4756  -7.9809  -6.5201  12.1861   7.5359  -6.6329  11.6449  -1.0432 #>    0.4642   0.9392   2.3865  -3.0983  -3.7857  -5.9992  -3.8601 -10.1172 #>   -1.1430   0.7860  -7.5414  -4.1419  -4.2946  -0.9784   3.2830   3.0879 #>    9.2455  -0.1982   1.3323  -4.7387   5.7670   1.2136   3.1590  -2.7698 #>   -2.5656  -3.8974   2.4592  -3.4115  11.9612   2.9927  -4.9330  10.8563 #>    0.8006   2.4989  -4.9836  10.0608  -0.5890   1.9800   8.0270   0.5575 #>    0.9505   3.0838  -2.3031   7.4922   3.3415   4.7355   1.3100   6.0895 #>    5.1837  -7.9399   1.3173   3.6236   0.7248  -6.4314  -0.7245  -9.9812 #>   -0.5216  -1.5727   1.0716  -8.4569  -6.8553  -6.1901   1.4440   4.1978 #>   -1.6952  -8.3790  -6.1057 -13.4262  -2.9027  -8.2298   7.7478  -5.3998 #>    5.6673  -4.3327  -0.4105   3.7569 -10.2672   4.3275  17.1337  15.0232 #>   -1.9419  -4.5694 -11.3853  12.0499   3.9334  -5.0898  -1.2279   0.6043 #>   -6.8327  -3.8372   1.8230 -21.0385  -7.1487   1.6563  -4.2593  -0.3652 #>   -0.9802   1.3503   3.7497   0.7901   2.9292   1.3497   7.8661  -0.7266 #>   -0.8863  -7.5690   4.4432 -14.6916   5.3799 -15.6941 -14.4122 -15.9588 #>   -4.2796  -2.7611  -6.4002   4.9127 -12.0253  -4.3114  -5.5047  -1.5682 #>    4.5966  -1.8511  -7.2233  -8.8994   3.2151  -0.4425  -8.4078  -1.6743 #>    2.8360  -0.9185  -5.6618  24.6701   2.6244  -3.7442   7.8791 -10.1859 #>   -1.5336   0.0708   2.7975   5.0025  -4.7435   2.2739   5.3512  -0.6133 #>    3.8462  -2.4170  14.4165  -5.7716   0.6435  -3.8622   2.6614  -0.1407 #>   -2.0673  -2.9854   3.5136  11.7400  11.0300  -6.6886  -7.5463   7.8790 #>   -1.8618  -6.4786  -1.9332  -5.9230  -0.6521  -9.2760  17.0369  10.8966 #>    5.3522  -4.3344  11.2488   4.6717   0.6802   2.8945   3.6235 -13.5183 #>   -1.6061   3.7639  -4.4445  -6.9303   8.4169   1.2716  -0.9037   6.4759 #>    2.1588  -0.3700  -5.0320  -6.8052   8.9850  -7.2337 -15.6611   2.8269 #>    1.8823   0.5136  17.5939   3.1880  13.8826  20.2181  -2.5064   2.9825 #>    6.7815   0.2294  -6.3192   7.3882  -2.7959   5.7683 -16.8174   3.9043 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{20,33,54} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose2d — torch_conv_transpose2d","title":"Conv_transpose2d — torch_conv_transpose2d","text":"Conv_transpose2d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose2d — torch_conv_transpose2d","text":"","code":"torch_conv_transpose2d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose2d — torch_conv_transpose2d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kH , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sH, sW). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padH, padW). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple (out_padH, out_padW).      Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose2d — torch_conv_transpose2d","text":"Applies 2D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\". See nn_conv_transpose2d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose2d — torch_conv_transpose2d","text":"","code":"if (torch_is_installed()) {  # With square kernels and equal stride inputs = torch_randn(c(1, 4, 5, 5)) weights = torch_randn(c(4, 8, 3, 3)) nnf_conv_transpose2d(inputs, weights, padding=1) } #> torch_tensor #> (1,1,.,.) =  #>   -5.9030   2.3719   3.2671  -6.3717   4.9661 #>   -9.3490  -2.2151   2.7751  -1.1339   1.3596 #>   -3.3151   2.1332  11.8538  -1.7507  -3.1123 #>    1.8214  -5.5401   5.5321  -2.6120   4.0258 #>   -7.6614   4.7752   2.8485   7.5207  -3.6628 #>  #> (1,2,.,.) =  #>    5.2193   2.6121  -1.2951   5.8279 -10.5490 #>    1.0212   3.0566   5.3418  -0.5528 -19.0809 #>   -2.4759   5.2707  -1.3049 -11.1098  -0.6705 #>   -0.8664   7.7155  -7.3916  13.1146   3.6660 #>    3.2671  -6.9516   8.2204   4.8681  -4.0088 #>  #> (1,3,.,.) =  #>   -8.2957   5.5987   3.3467  -3.6443   1.0645 #>    8.2492  -3.3375  -6.9863  -6.0146  -8.4941 #>    3.4540  -3.0300  -5.0501   5.6115   1.7198 #>    7.3158  -2.3231   0.1430  -0.7191  10.1638 #>   -0.5320  -0.4238  -2.4003   2.9976   3.9288 #>  #> (1,4,.,.) =  #>    8.2648   3.0973  10.2399   3.8716  -6.8306 #>    1.4585   3.1623  -1.6594   3.6279   1.1162 #>    2.4186  -4.9536   4.4081  -3.1237  -8.9828 #>   -7.3744  -2.3638   0.2175   8.9855   1.6173 #>    1.3701   3.9888   3.5332   2.9809   0.5901 #>  #> (1,5,.,.) =  #>  -3.8535 -3.2569  7.7058 -1.4514 -1.5325 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{1,8,5,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":null,"dir":"Reference","previous_headings":"","what":"Conv_transpose3d — torch_conv_transpose3d","title":"Conv_transpose3d — torch_conv_transpose3d","text":"Conv_transpose3d","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conv_transpose3d — torch_conv_transpose3d","text":"","code":"torch_conv_transpose3d(   input,   weight,   bias = list(),   stride = 1L,   padding = 0L,   output_padding = 0L,   groups = 1L,   dilation = 1L )"},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conv_transpose3d — torch_conv_transpose3d","text":"input input tensor shape \\((\\mbox{minibatch} , \\mbox{\\_channels} , , iH , iW)\\) weight filters shape \\((\\mbox{\\_channels} , \\frac{\\mbox{\\_channels}}{\\mbox{groups}} , kT , kH , kW)\\) bias optional bias shape \\((\\mbox{\\_channels})\\). Default: NULL stride stride convolving kernel. Can single number      tuple (sT, sH, sW). Default: 1 padding dilation * (kernel_size - 1) - padding zero-padding added      sides dimension input. Can single number tuple      (padT, padH, padW). Default: 0 output_padding additional size added one side dimension      output shape. Can single number tuple      (out_padT, out_padH, out_padW). Default: 0 groups split input groups, \\(\\mbox{\\_channels}\\) divisible      number groups. Default: 1 dilation spacing kernel elements. Can single number      tuple (dT, dH, dW). Default: 1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-","dir":"Reference","previous_headings":"","what":"conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor","title":"Conv_transpose3d — torch_conv_transpose3d","text":"Applies 3D transposed convolution operator input image composed several input planes, sometimes also called \"deconvolution\" See nn_conv_transpose3d() details output shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_conv_transpose3d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conv_transpose3d — torch_conv_transpose3d","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ inputs = torch_randn(c(20, 16, 50, 10, 20)) weights = torch_randn(c(16, 33, 3, 3, 3)) nnf_conv_transpose3d(inputs, weights) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":null,"dir":"Reference","previous_headings":"","what":"Cos — torch_cos","title":"Cos — torch_cos","text":"Cos","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cos — torch_cos","text":"","code":"torch_cos(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cos — torch_cos","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"cos-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cos(input, out=NULL) -> Tensor","title":"Cos — torch_cos","text":"Returns new tensor cosine  elements input. $$     \\mbox{}_{} = \\cos(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cos — torch_cos","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_cos(a) } #> torch_tensor #>  0.6416 #>  0.9991 #>  0.9324 #>  0.9880 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosh — torch_cosh","title":"Cosh — torch_cosh","text":"Cosh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosh — torch_cosh","text":"","code":"torch_cosh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosh — torch_cosh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"cosh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cosh(input, out=NULL) -> Tensor","title":"Cosh — torch_cosh","text":"Returns new tensor hyperbolic cosine  elements input. $$     \\mbox{}_{} = \\cosh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cosh — torch_cosh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_cosh(a) } #> torch_tensor #>  3.8436 #>  3.7721 #>  2.2416 #>  1.0714 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Cosine_similarity — torch_cosine_similarity","title":"Cosine_similarity — torch_cosine_similarity","text":"Cosine_similarity","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cosine_similarity — torch_cosine_similarity","text":"","code":"torch_cosine_similarity(x1, x2, dim = 2L, eps = 1e-08)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cosine_similarity — torch_cosine_similarity","text":"x1 (Tensor) First input. x2 (Tensor) Second input (size matching x1). dim (int, optional) Dimension vectors. Default: 1 eps (float, optional) Small value avoid division zero.        Default: 1e-8","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"cosine-similarity-x-x-dim-eps-e-gt-tensor-","dir":"Reference","previous_headings":"","what":"cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor","title":"Cosine_similarity — torch_cosine_similarity","text":"Returns cosine similarity x1 x2, computed along dim. $$     \\mbox{similarity} = \\frac{x_1 \\cdot x_2}{\\max(\\Vert x_1 \\Vert _2 \\cdot \\Vert x_2 \\Vert _2, \\epsilon)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cosine_similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cosine_similarity — torch_cosine_similarity","text":"","code":"if (torch_is_installed()) {  input1 = torch_randn(c(100, 128)) input2 = torch_randn(c(100, 128)) output = torch_cosine_similarity(input1, input2) output } #> torch_tensor #> -0.0518 #> -0.1353 #>  0.0055 #>  0.1350 #>  0.0106 #>  0.0236 #>  0.0152 #> -0.0359 #> -0.0339 #>  0.0344 #>  0.0024 #>  0.0932 #>  0.0735 #>  0.0522 #>  0.0258 #>  0.0779 #> -0.1295 #> -0.0928 #>  0.0746 #>  0.0432 #>  0.0355 #>  0.0251 #>  0.0303 #> -0.0159 #>  0.1192 #>  0.0507 #>  0.1229 #> -0.0884 #> -0.0054 #> -0.0657 #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{100} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Count_nonzero — torch_count_nonzero","title":"Count_nonzero — torch_count_nonzero","text":"Count_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count_nonzero — torch_count_nonzero","text":"","code":"torch_count_nonzero(self, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count_nonzero — torch_count_nonzero","text":"self (Tensor) input tensor. dim (int tuple ints, optional) Dim tuple dims along count non-zeros.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"count-nonzero-input-dim-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"count_nonzero(input, dim=None) -> Tensor","title":"Count_nonzero — torch_count_nonzero","text":"Counts number non-zero values tensor input along given dim. dim specified non-zeros tensor counted.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_count_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count_nonzero — torch_count_nonzero","text":"","code":"if (torch_is_installed()) {  x <- torch_zeros(3,3) x[torch_randn(3,3) > 0.5] = 1 x torch_count_nonzero(x) torch_count_nonzero(x, dim=1) } #> torch_tensor #>  1 #>  1 #>  0 #> [ CPULongType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross — torch_cross","title":"Cross — torch_cross","text":"Cross","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross — torch_cross","text":"","code":"torch_cross(self, other, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross — torch_cross","text":"self (Tensor) input tensor. (Tensor) second input tensor dim (int, optional) dimension take cross-product .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"cross-input-other-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cross(input, other, dim=-1, out=NULL) -> Tensor","title":"Cross — torch_cross","text":"Returns cross product vectors dimension dim input . input must size, size dim dimension 3. dim given, defaults first dimension found size 3.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross — torch_cross","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4, 3)) a b = torch_randn(c(4, 3)) b torch_cross(a, b, dim=2) torch_cross(a, b) } #> torch_tensor #> -1.0034 -0.4132 -3.8712 #> -1.6889  1.9374  2.2741 #>  1.7125 -0.7639 -0.0354 #>  0.4060  0.7154  1.5142 #> [ CPUFloatType{4,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":null,"dir":"Reference","previous_headings":"","what":"Cummax — torch_cummax","title":"Cummax — torch_cummax","text":"Cummax","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cummax — torch_cummax","text":"","code":"torch_cummax(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cummax — torch_cummax","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"cummax-input-dim-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"cummax(input, dim) -> (Tensor, LongTensor)","title":"Cummax — torch_cummax","text":"Returns namedtuple (values, indices) values cumulative maximum elements input dimension dim. indices index location maximum value found dimension dim. $$     y_i = max(x_1, x_2, x_3, \\dots, x_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummax.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cummax — torch_cummax","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cummax(a, dim=1) } #> [[1]] #> torch_tensor #> -0.0764 #>  0.4207 #>  0.5863 #>  0.8667 #>  2.5016 #>  2.5016 #>  2.5016 #>  2.5016 #>  2.5016 #>  2.5016 #> [ CPUFloatType{10} ] #>  #> [[2]] #> torch_tensor #>  0 #>  1 #>  2 #>  3 #>  4 #>  4 #>  4 #>  4 #>  4 #>  4 #> [ CPULongType{10} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":null,"dir":"Reference","previous_headings":"","what":"Cummin — torch_cummin","title":"Cummin — torch_cummin","text":"Cummin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cummin — torch_cummin","text":"","code":"torch_cummin(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cummin — torch_cummin","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"cummin-input-dim-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"cummin(input, dim) -> (Tensor, LongTensor)","title":"Cummin — torch_cummin","text":"Returns namedtuple (values, indices) values cumulative minimum elements input dimension dim. indices index location maximum value found dimension dim. $$     y_i = min(x_1, x_2, x_3, \\dots, x_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cummin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cummin — torch_cummin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cummin(a, dim=1) } #> [[1]] #> torch_tensor #>  0.1865 #>  0.1865 #> -1.5402 #> -1.5402 #> -1.5402 #> -1.5402 #> -1.5402 #> -1.5402 #> -1.5402 #> -1.5402 #> [ CPUFloatType{10} ] #>  #> [[2]] #> torch_tensor #>  0 #>  0 #>  2 #>  2 #>  2 #>  2 #>  2 #>  2 #>  2 #>  2 #> [ CPULongType{10} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumprod — torch_cumprod","title":"Cumprod — torch_cumprod","text":"Cumprod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumprod — torch_cumprod","text":"","code":"torch_cumprod(self, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumprod — torch_cumprod","text":"self (Tensor) input tensor. dim (int) dimension operation dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"cumprod-input-dim-out-null-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cumprod(input, dim, out=NULL, dtype=NULL) -> Tensor","title":"Cumprod — torch_cumprod","text":"Returns cumulative product elements input dimension dim. example, input vector size N, result also vector size N, elements. $$     y_i = x_1 \\times x_2\\times x_3\\times \\dots \\times x_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumprod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumprod — torch_cumprod","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cumprod(a, dim=1) } #> torch_tensor #>  1.6083 #>  1.3658 #> -0.3418 #> -0.4265 #>  0.0020 #>  0.0025 #> -0.0005 #>  0.0006 #>  0.0005 #> -0.0005 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumsum — torch_cumsum","title":"Cumsum — torch_cumsum","text":"Cumsum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumsum — torch_cumsum","text":"","code":"torch_cumsum(self, dim, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumsum — torch_cumsum","text":"self (Tensor) input tensor. dim (int) dimension operation dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"cumsum-input-dim-out-null-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"cumsum(input, dim, out=NULL, dtype=NULL) -> Tensor","title":"Cumsum — torch_cumsum","text":"Returns cumulative sum elements input dimension dim. example, input vector size N, result also vector size N, elements. $$     y_i = x_1 + x_2 + x_3 + \\dots + x_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_cumsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumsum — torch_cumsum","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(10)) a torch_cumsum(a, dim=1) } #> torch_tensor #> -1.2499 #> -0.6899 #> -2.9677 #> -2.2579 #> -1.8257 #> -1.3672 #>  0.0859 #> -0.2341 #>  0.0627 #> -0.1228 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":null,"dir":"Reference","previous_headings":"","what":"Deg2rad — torch_deg2rad","title":"Deg2rad — torch_deg2rad","text":"Deg2rad","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deg2rad — torch_deg2rad","text":"","code":"torch_deg2rad(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deg2rad — torch_deg2rad","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"deg-rad-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"deg2rad(input, *, out=None) -> Tensor","title":"Deg2rad — torch_deg2rad","text":"Returns new tensor elements input converted angles degrees radians.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_deg2rad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Deg2rad — torch_deg2rad","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(rbind(c(180.0, -180.0), c(360.0, -360.0), c(90.0, -90.0))) torch_deg2rad(a) } #> torch_tensor #>  3.1416 -3.1416 #>  6.2832 -6.2832 #>  1.5708 -1.5708 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":null,"dir":"Reference","previous_headings":"","what":"Dequantize — torch_dequantize","title":"Dequantize — torch_dequantize","text":"Dequantize","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dequantize — torch_dequantize","text":"","code":"torch_dequantize(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dequantize — torch_dequantize","text":"tensor (Tensor) quantized Tensor list oof quantized tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"dequantize-tensor-gt-tensor-","dir":"Reference","previous_headings":"","what":"dequantize(tensor) -> Tensor","title":"Dequantize — torch_dequantize","text":"Returns fp32 Tensor dequantizing quantized Tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dequantize.html","id":"dequantize-tensors-gt-sequence-of-tensors-","dir":"Reference","previous_headings":"","what":"dequantize(tensors) -> sequence of Tensors","title":"Dequantize — torch_dequantize","text":"Given list quantized Tensors, dequantize return list fp32 Tensors","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":null,"dir":"Reference","previous_headings":"","what":"Det — torch_det","title":"Det — torch_det","text":"Det","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Det — torch_det","text":"","code":"torch_det(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Det — torch_det","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Det — torch_det","text":"","code":"Backward through `det` internally uses SVD results when `input` is not invertible. In this case, double backward through `det` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"det-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"det(input) -> Tensor","title":"Det — torch_det","text":"Calculates determinant square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_det.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Det — torch_det","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) torch_det(A) A = torch_randn(c(3, 2, 2)) A A$det() } #> torch_tensor #>  1.6516 #>  2.0722 #> -1.1292 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Device object — torch_device","title":"Create a Device object — torch_device","text":"torch_device  object representing device torch_tensor allocated.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Device object — torch_device","text":"","code":"torch_device(type, index = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Device object — torch_device","text":"type (character) device type \"cuda\" \"cpu\" index (integer) optional device ordinal device type.  device ordinal present, object always represent current device device type, even torch_cuda_set_device() called; e.g., torch_tensor constructed device 'cuda' equivalent 'cuda:X' X result torch_cuda_current_device(). torch_device can constructed via string via string device ordinal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_device.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Device object — torch_device","text":"","code":"if (torch_is_installed()) {  # Via string torch_device(\"cuda:1\") torch_device(\"cpu\") torch_device(\"cuda\") # current cuda device  # Via string and device ordinal torch_device(\"cuda\", 0) torch_device(\"cpu\", 0) } #> torch_device(type='cpu', index=0)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Diag — torch_diag","title":"Diag — torch_diag","text":"Diag","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diag — torch_diag","text":"","code":"torch_diag(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diag — torch_diag","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag.html","id":"diag-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"diag(input, diagonal=0, out=NULL) -> Tensor","title":"Diag — torch_diag","text":"input vector (1-D tensor), returns 2-D square tensor elements input diagonal. input matrix (2-D tensor), returns 1-D tensor diagonal elements input. argument diagonal controls diagonal consider: diagonal = 0, main diagonal. diagonal > 0, main diagonal. diagonal < 0, main diagonal.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":null,"dir":"Reference","previous_headings":"","what":"Diag_embed — torch_diag_embed","title":"Diag_embed — torch_diag_embed","text":"Diag_embed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diag_embed — torch_diag_embed","text":"","code":"torch_diag_embed(self, offset = 0L, dim1 = -2L, dim2 = -1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diag_embed — torch_diag_embed","text":"self (Tensor) input tensor. Must least 1-dimensional. offset (int, optional) diagonal consider. Default: 0        (main diagonal). dim1 (int, optional) first dimension respect        take diagonal. Default: -2. dim2 (int, optional) second dimension respect        take diagonal. Default: -1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"diag-embed-input-offset-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor","title":"Diag_embed — torch_diag_embed","text":"Creates tensor whose diagonals certain 2D planes (specified dim1 dim2) filled input. facilitate creating batched diagonal matrices, 2D planes formed last two dimensions returned tensor chosen default. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal. size new matrix calculated make specified diagonal size last input dimension. Note offset \\(0\\), order dim1 dim2 matters. Exchanging equivalent changing sign offset. Applying torch_diagonal output function arguments yields matrix identical input. However, torch_diagonal different default dimensions, need explicitly specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diag_embed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diag_embed — torch_diag_embed","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(2, 3)) torch_diag_embed(a) torch_diag_embed(a, offset=1, dim1=1, dim2=3) } #> torch_tensor #> (1,.,.) =  #>   0.0000 -0.1034  0.0000  0.0000 #>   0.0000 -1.9597  0.0000  0.0000 #>  #> (2,.,.) =  #>   0.0000  0.0000  0.4405  0.0000 #>   0.0000  0.0000 -0.2237  0.0000 #>  #> (3,.,.) =  #>   0.0000  0.0000  0.0000 -0.1494 #>   0.0000  0.0000  0.0000 -0.4696 #>  #> (4,.,.) =  #>   0  0  0  0 #>   0  0  0  0 #> [ CPUFloatType{4,2,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagflat — torch_diagflat","title":"Diagflat — torch_diagflat","text":"Diagflat","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagflat — torch_diagflat","text":"","code":"torch_diagflat(self, offset = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagflat — torch_diagflat","text":"self (Tensor) input tensor. offset (int, optional) diagonal consider. Default: 0 (main        diagonal).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"diagflat-input-offset-gt-tensor-","dir":"Reference","previous_headings":"","what":"diagflat(input, offset=0) -> Tensor","title":"Diagflat — torch_diagflat","text":"input vector (1-D tensor), returns 2-D square tensor elements input diagonal. input tensor one dimension, returns 2-D tensor diagonal elements equal flattened input. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagflat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagflat — torch_diagflat","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3)) a torch_diagflat(a) torch_diagflat(a, 1) a = torch_randn(c(2, 2)) a torch_diagflat(a) } #> torch_tensor #>  1.7448  0.0000  0.0000  0.0000 #>  0.0000 -0.9938  0.0000  0.0000 #>  0.0000  0.0000  0.6135  0.0000 #>  0.0000  0.0000  0.0000 -0.7422 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagonal — torch_diagonal","title":"Diagonal — torch_diagonal","text":"Diagonal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagonal — torch_diagonal","text":"","code":"torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagonal — torch_diagonal","text":"self (Tensor) input tensor. Must least 2-dimensional. outdim dimension name self named tensor. dim1 (int, optional) first dimension respect        take diagonal. Default: 0. dim2 (int, optional) second dimension respect        take diagonal. Default: 1. offset (int, optional) diagonal consider. Default: 0        (main diagonal).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"diagonal-input-offset-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor","title":"Diagonal — torch_diagonal","text":"Returns partial view input diagonal elements respect dim1 dim2 appended dimension end shape. argument offset controls diagonal consider: offset = 0, main diagonal. offset > 0, main diagonal. offset < 0, main diagonal. Applying torch_diag_embed output function arguments yields diagonal matrix diagonal entries input. However, torch_diag_embed different default dimensions, need explicitly specified.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diagonal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagonal — torch_diagonal","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_diagonal(a, offset = 0) torch_diagonal(a, offset = 1) x = torch_randn(c(2, 5, 4, 2)) torch_diagonal(x, offset=-1, dim1=1, dim2=2) } #> torch_tensor #> (1,.,.) =  #>  -0.7543 #>  -0.5912 #>  #> (2,.,.) =  #>   1.1799 #>  -1.7287 #>  #> (3,.,.) =  #>   0.2354 #>   0.4116 #>  #> (4,.,.) =  #>   0.4167 #>  -0.2596 #> [ CPUFloatType{4,2,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes the n-th forward difference along the given dimension. — torch_diff","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"first-order differences given [] = input[+ 1] - input[]. Higher-order differences calculated using torch_diff() recursively.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"","code":"torch_diff(self, n = 1L, dim = -1L, prepend = list(), append = list())"},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"self tensor compute differences n number times recursively compute difference dim dimension compute difference along. Default last dimension. prepend values prepend input along dim computing difference. dimensions must equivalent input, shapes must match input’s shape except dim. append values append input along dim computing difference. dimensions must equivalent input, shapes must match input’s shape except dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"n = 1 currently supported","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computes the n-th forward difference along the given dimension. — torch_diff","text":"","code":"if (torch_is_installed()) { a <- torch_tensor(c(1,2,3)) torch_diff(a)  b <- torch_tensor(c(4, 5)) torch_diff(a, append = b)  c <- torch_tensor(rbind(c(1,2,3), c(3,4,5))) torch_diff(c, dim = 1) torch_diff(c, dim = 2)   } #> torch_tensor #>  1  1 #>  1  1 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Digamma — torch_digamma","title":"Digamma — torch_digamma","text":"Digamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Digamma — torch_digamma","text":"","code":"torch_digamma(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Digamma — torch_digamma","text":"self (Tensor) tensor compute digamma function ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"digamma-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"digamma(input, out=NULL) -> Tensor","title":"Digamma — torch_digamma","text":"Computes logarithmic derivative gamma function input. $$     \\psi(x) = \\frac{d}{dx} \\ln\\left(\\Gamma\\left(x\\right)\\right) = \\frac{\\Gamma'(x)}{\\Gamma(x)} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_digamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Digamma — torch_digamma","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(1, 0.5)) torch_digamma(a) } #> torch_tensor #> -0.5772 #> -1.9635 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Dist — torch_dist","title":"Dist — torch_dist","text":"Dist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dist — torch_dist","text":"","code":"torch_dist(self, other, p = 2L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dist — torch_dist","text":"self (Tensor) input tensor. (Tensor) Right-hand-side input tensor p (float, optional) norm computed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"dist-input-other-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"dist(input, other, p=2) -> Tensor","title":"Dist — torch_dist","text":"Returns p-norm (input - ) shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dist — torch_dist","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(4)) x y = torch_randn(c(4)) y torch_dist(x, y, 3.5) torch_dist(x, y, 3) torch_dist(x, y, 0) torch_dist(x, y, 1) } #> torch_tensor #> 1.93941 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Div — torch_div","title":"Div — torch_div","text":"Div","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Div — torch_div","text":"","code":"torch_div(self, other, rounding_mode)"},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Div — torch_div","text":"self (Tensor) input tensor. (Number) number divided element input rounding_mode (str, optional) – Type rounding applied result: NULL - default behavior. Performs rounding , input integer types, promotes inputs default scalar type. Equivalent true division Python (/ operator) NumPy’s np.true_divide. \"trunc\" - rounds results division towards zero. Equivalent C-style integer division. \"floor\" - rounds results division . Equivalent floor division Python (// operator) NumPy’s np.floor_divide.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"div-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"div(input, other, out=NULL) -> Tensor","title":"Div — torch_div","text":"Divides element input input scalar returns new resulting tensor. element tensor input divided element tensor . resulting tensor returned. $$     \\mbox{}_i = \\frac{\\mbox{input}_i}{\\mbox{}_i} $$ shapes input must broadcastable . torch_dtype input differ, torch_dtype result tensor determined following rules described type promotion documentation . specified, result must castable  torch_dtype specified output tensor. Integral division zero leads undefined behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Div — torch_div","text":"Integer division using div deprecated, future release div perform true division like torch_true_divide(). Use torch_floor_divide() perform integer division, instead. $$     \\mbox{}_i = \\frac{\\mbox{input}_i}{\\mbox{}} $$ torch_dtype input differ, torch_dtype result tensor determined following rules described type promotion documentation . specified, result must castable torch_dtype specified output tensor. Integral division zero leads undefined behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_div.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Div — torch_div","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_div(a, 0.5)   a = torch_randn(c(4, 4)) a b = torch_randn(c(4)) b torch_div(a, b) } #> torch_tensor #> -0.8633  0.0851 -3.1823 -0.1029 #> -0.4324 -0.7425  4.7351 -0.7904 #> -0.7002  0.9732  2.1361  1.3438 #>  2.2862 -2.4670  4.6140 -0.1961 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Divide — torch_divide","title":"Divide — torch_divide","text":"Divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Divide — torch_divide","text":"","code":"torch_divide(self, other, rounding_mode)"},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Divide — torch_divide","text":"self (Tensor) input tensor. (Number) number divided element input rounding_mode (str, optional) – Type rounding applied result: NULL - default behavior. Performs rounding , input integer types, promotes inputs default scalar type. Equivalent true division Python (/ operator) NumPy’s np.true_divide. \"trunc\" - rounds results division towards zero. Equivalent C-style integer division. \"floor\" - rounds results division . Equivalent floor division Python (// operator) NumPy’s np.floor_divide.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_divide.html","id":"divide-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"divide(input, other, *, out=None) -> Tensor","title":"Divide — torch_divide","text":"Alias torch_div().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Dot — torch_dot","title":"Dot — torch_dot","text":"Dot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dot — torch_dot","text":"","code":"torch_dot(self, tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dot — torch_dot","text":"self input tensor tensor input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Dot — torch_dot","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"dot-input-tensor-gt-tensor-","dir":"Reference","previous_headings":"","what":"dot(input, tensor) -> Tensor","title":"Dot — torch_dot","text":"Computes dot product (inner product) two tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dot — torch_dot","text":"","code":"if (torch_is_installed()) {  torch_dot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1))) } #> torch_tensor #> 7 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Dstack — torch_dstack","title":"Dstack — torch_dstack","text":"Dstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dstack — torch_dstack","text":"","code":"torch_dstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dstack — torch_dstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"dstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"dstack(tensors, *, out=None) -> Tensor","title":"Dstack — torch_dstack","text":"Stack tensors sequence depthwise (along third axis). equivalent concatenation along third axis 1-D 2-D tensors reshaped torch_atleast_3d().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dstack — torch_dstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_dstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_dstack(list(a,b)) } #> torch_tensor #> (1,.,.) =  #>   1  4 #>  #> (2,.,.) =  #>   2  5 #>  #> (3,.,.) =  #>   3  6 #> [ CPUFloatType{3,1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_dtype.html","id":null,"dir":"Reference","previous_headings":"","what":"Torch data types — torch_dtype","title":"Torch data types — torch_dtype","text":"Returns correspondent data type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_dtype.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Torch data types — torch_dtype","text":"","code":"torch_float32()  torch_float()  torch_float64()  torch_double()  torch_cfloat32()  torch_chalf()  torch_cfloat()  torch_cfloat64()  torch_cdouble()  torch_cfloat128()  torch_float16()  torch_half()  torch_uint8()  torch_int8()  torch_int16()  torch_short()  torch_int32()  torch_int()  torch_int64()  torch_long()  torch_bool()  torch_quint8()  torch_qint8()  torch_qint32()"},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":null,"dir":"Reference","previous_headings":"","what":"Eig — torch_eig","title":"Eig — torch_eig","text":"Eig","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eig — torch_eig","text":"self (Tensor) square matrix shape \\((n \\times n)\\) eigenvalues eigenvectors        computed eigenvectors (bool) TRUE compute eigenvalues eigenvectors;        otherwise, eigenvalues computed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eig.html","id":"eig-input-eigenvectors-false-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"eig(input, eigenvectors=False, out=NULL) -> (Tensor, Tensor)","title":"Eig — torch_eig","text":"Computes eigenvalues eigenvectors real square matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":null,"dir":"Reference","previous_headings":"","what":"Einsum — torch_einsum","title":"Einsum — torch_einsum","text":"Einsum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Einsum — torch_einsum","text":"","code":"torch_einsum(equation, tensors, path = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Einsum — torch_einsum","text":"equation (string) equation given terms lower case letters (indices) associated           dimension operands result. left hand side lists operands           dimensions, separated commas. one index letter per tensor dimension.           right hand side follows -> gives indices output.           -> right hand side omitted, implicitly defined alphabetically           sorted list indices appearing exactly left hand side.           indices apprearing output summed multiplying operands           entries.           index appears several times operand, diagonal taken.           Ellipses ... represent fixed number dimensions. right hand side inferred,           ellipsis dimensions beginning output. tensors (Tensor) operands compute Einstein sum . path (int) function uses opt_einsum speed computation consume less memory optimizing contraction order. optimization occurs least three inputs, since order matter otherwise. Note finding optimal path NP-hard problem, thus, opt_einsum relies different heuristics achieve near-optimal results. opt_einsum available, default order contract left right. path argument used changed default, set advanced users.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"einsum-equation-operands-gt-tensor-","dir":"Reference","previous_headings":"","what":"einsum(equation, *operands) -> Tensor","title":"Einsum — torch_einsum","text":"function provides way computing multilinear expressions (.e. sums products) using Einstein summation convention.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_einsum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Einsum — torch_einsum","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(5)) y = torch_randn(c(4)) torch_einsum('i,j->ij', list(x, y))  # outer product A = torch_randn(c(3,5,4)) l = torch_randn(c(2,5)) r = torch_randn(c(2,4)) torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear As = torch_randn(c(3,2,5)) Bs = torch_randn(c(3,5,4)) torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication A = torch_randn(c(3, 3)) torch_einsum('ii->i', list(A)) # diagonal A = torch_randn(c(4, 3, 3)) torch_einsum('...ii->...i', list(A)) # batch diagonal A = torch_randn(c(2, 3, 4, 5)) torch_einsum('...ij->...ji', list(A))$shape # batch permute  } #> [1] 2 3 5 4"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty — torch_empty","title":"Empty — torch_empty","text":"Empty","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty — torch_empty","text":"","code":"torch_empty(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty — torch_empty","text":"... sequence integers defining shape output tensor. names optional character vector naming dimension. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -> Tensor","title":"Empty — torch_empty","text":"Returns tensor filled uninitialized data. shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty — torch_empty","text":"","code":"if (torch_is_installed()) {  torch_empty(c(2, 3)) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty_like — torch_empty_like","title":"Empty_like — torch_empty_like","text":"Empty_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty_like — torch_empty_like","text":"","code":"torch_empty_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty_like — torch_empty_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Empty_like — torch_empty_like","text":"Returns uninitialized tensor size input. torch_empty_like(input) equivalent torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty_like — torch_empty_like","text":"","code":"if (torch_is_installed()) {  torch_empty(list(2,3), dtype = torch_int64()) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty_strided — torch_empty_strided","title":"Empty_strided — torch_empty_strided","text":"Empty_strided","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty_strided — torch_empty_strided","text":"","code":"torch_empty_strided(   size,   stride,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   pin_memory = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Empty_strided — torch_empty_strided","text":"size (tuple ints) shape output tensor stride (tuple ints) strides output tensor dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. pin_memory (bool, optional) set, returned tensor allocated        pinned memory. Works CPU tensors. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -> Tensor","title":"Empty_strided — torch_empty_strided","text":"Returns tensor filled uninitialized data. shape strides tensor defined variable argument size stride respectively. torch_empty_strided(size, stride) equivalent torch_empty(size).as_strided(size, stride).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Empty_strided — torch_empty_strided","text":"one element created tensor may refer single memory location. result, -place operations (especially ones vectorized) may result incorrect behavior. need write tensors, please clone first.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_empty_strided.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Empty_strided — torch_empty_strided","text":"","code":"if (torch_is_installed()) {  a = torch_empty_strided(list(2, 3), list(1, 2)) a a$stride(1) a$size(1) } #> [1] 2"},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":null,"dir":"Reference","previous_headings":"","what":"Eq — torch_eq","title":"Eq — torch_eq","text":"Eq","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eq — torch_eq","text":"","code":"torch_eq(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eq — torch_eq","text":"self (Tensor) tensor compare (Tensor float) tensor value compare Must ByteTensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"eq-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"eq(input, other, out=NULL) -> Tensor","title":"Eq — torch_eq","text":"Computes element-wise equality second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eq — torch_eq","text":"","code":"if (torch_is_installed()) {  torch_eq(torch_tensor(c(1,2,3,4)), torch_tensor(c(1, 3, 2, 4))) } #> torch_tensor #>  1 #>  0 #>  0 #>  1 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal — torch_equal","title":"Equal — torch_equal","text":"Equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal — torch_equal","text":"","code":"torch_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal — torch_equal","text":"self input tensor input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"equal-input-other-gt-bool-","dir":"Reference","previous_headings":"","what":"equal(input, other) -> bool","title":"Equal — torch_equal","text":"TRUE two tensors size elements, FALSE otherwise.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_equal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal — torch_equal","text":"","code":"if (torch_is_installed()) {  torch_equal(torch_tensor(c(1, 2)), torch_tensor(c(1, 2))) } #> [1] TRUE"},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":null,"dir":"Reference","previous_headings":"","what":"Erf — torch_erf","title":"Erf — torch_erf","text":"Erf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erf — torch_erf","text":"","code":"torch_erf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erf — torch_erf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"erf-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erf(input, out=NULL) -> Tensor","title":"Erf — torch_erf","text":"Computes error function element. error function defined follows: $$     \\mathrm{erf}(x) = \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x} e^{-t^2} dt $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erf — torch_erf","text":"","code":"if (torch_is_installed()) {  torch_erf(torch_tensor(c(0, -1., 10.))) } #> torch_tensor #>  0.0000 #> -0.8427 #>  1.0000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":null,"dir":"Reference","previous_headings":"","what":"Erfc — torch_erfc","title":"Erfc — torch_erfc","text":"Erfc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erfc — torch_erfc","text":"","code":"torch_erfc(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erfc — torch_erfc","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"erfc-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erfc(input, out=NULL) -> Tensor","title":"Erfc — torch_erfc","text":"Computes complementary error function element input. complementary error function defined follows: $$     \\mathrm{erfc}(x) = 1 - \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x} e^{-t^2} dt $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erfc — torch_erfc","text":"","code":"if (torch_is_installed()) {  torch_erfc(torch_tensor(c(0, -1., 10.))) } #> torch_tensor #>  1.0000e+00 #>  1.8427e+00 #>  2.8026e-45 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":null,"dir":"Reference","previous_headings":"","what":"Erfinv — torch_erfinv","title":"Erfinv — torch_erfinv","text":"Erfinv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Erfinv — torch_erfinv","text":"","code":"torch_erfinv(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Erfinv — torch_erfinv","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"erfinv-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"erfinv(input, out=NULL) -> Tensor","title":"Erfinv — torch_erfinv","text":"Computes inverse error function element input. inverse error function defined range \\((-1, 1)\\) : $$     \\mathrm{erfinv}(\\mathrm{erf}(x)) = x $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_erfinv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Erfinv — torch_erfinv","text":"","code":"if (torch_is_installed()) {  torch_erfinv(torch_tensor(c(0, 0.5, -1.))) } #> torch_tensor #>  0.0000 #>  0.4769 #>    -inf #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Exp — torch_exp","title":"Exp — torch_exp","text":"Exp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exp — torch_exp","text":"","code":"torch_exp(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exp — torch_exp","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"exp-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"exp(input, out=NULL) -> Tensor","title":"Exp — torch_exp","text":"Returns new tensor exponential elements input tensor input. $$     y_{} = e^{x_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exp — torch_exp","text":"","code":"if (torch_is_installed()) {  torch_exp(torch_tensor(c(0, log(2)))) } #> torch_tensor #>  1 #>  2 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":null,"dir":"Reference","previous_headings":"","what":"Exp2 — torch_exp2","title":"Exp2 — torch_exp2","text":"Exp2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exp2 — torch_exp2","text":"","code":"torch_exp2(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exp2 — torch_exp2","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"exp-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"exp2(input, *, out=None) -> Tensor","title":"Exp2 — torch_exp2","text":"Computes base two exponential function input. $$     y_{} = 2^{x_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_exp2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Exp2 — torch_exp2","text":"","code":"if (torch_is_installed()) {  torch_exp2(torch_tensor(c(0, log2(2.), 3, 4))) } #> torch_tensor #>   1 #>   2 #>   8 #>  16 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":null,"dir":"Reference","previous_headings":"","what":"Expm1 — torch_expm1","title":"Expm1 — torch_expm1","text":"Expm1","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expm1 — torch_expm1","text":"","code":"torch_expm1(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expm1 — torch_expm1","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"expm-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"expm1(input, out=NULL) -> Tensor","title":"Expm1 — torch_expm1","text":"Returns new tensor exponential elements minus 1 input. $$     y_{} = e^{x_{}} - 1 $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_expm1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expm1 — torch_expm1","text":"","code":"if (torch_is_installed()) {  torch_expm1(torch_tensor(c(0, log(2)))) } #> torch_tensor #>  0 #>  1 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":null,"dir":"Reference","previous_headings":"","what":"Eye — torch_eye","title":"Eye — torch_eye","text":"Eye","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Eye — torch_eye","text":"","code":"torch_eye(   n,   m = n,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Eye — torch_eye","text":"n (int) number rows m (int, optional) number columns default n dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Eye — torch_eye","text":"Returns 2-D tensor ones diagonal zeros elsewhere.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_eye.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Eye — torch_eye","text":"","code":"if (torch_is_installed()) {  torch_eye(3) } #> torch_tensor #>  1  0  0 #>  0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":null,"dir":"Reference","previous_headings":"","what":"Fft — torch_fft_fft","title":"Fft — torch_fft_fft","text":"Computes one dimensional discrete Fourier transform input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fft — torch_fft_fft","text":"","code":"torch_fft_fft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fft — torch_fft_fft","text":"self (Tensor) input tensor n (int) Signal length. given, input either zero-padded trimmed length computing FFT. dim (int, optional) dimension along take one dimensional FFT. norm (str, optional) Normalization mode. forward transform, correspond : \"forward\" - normalize 1/n \"backward\" - normalization \"ortho\" - normalize 1/sqrt(n) (making FFT orthonormal) Calling backward transform (ifft()) normalization mode apply overall normalization 1/n two transforms. required make IFFT exact inverse. Default \"backward\" (normalization).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fft — torch_fft_fft","text":"Fourier domain representation real signal satisfies Hermitian property: X[] = conj(X[-]). function always returns positive negative frequency terms even though, real inputs, negative frequencies redundant. rfft() returns compact one-sided representation positive frequencies returned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fft — torch_fft_fft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) t torch_fft_fft(t, norm = \"backward\")  } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":null,"dir":"Reference","previous_headings":"","what":"fftfreq — torch_fft_fftfreq","title":"fftfreq — torch_fft_fftfreq","text":"Computes discrete Fourier Transform sample frequencies signal size n.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"fftfreq — torch_fft_fftfreq","text":"","code":"torch_fft_fftfreq(   n,   d = 1,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"fftfreq — torch_fft_fftfreq","text":"n (integer) – FFT length d (float, optional) – sampling length scale. spacing individual samples FFT input. default assumes unit spacing, dividing result actual spacing gives result physical frequency units. dtype (default: torch_get_default_dtype()) desired data type returned tensor. layout (default: torch_strided()) desired layout returned tensor. device (default: NULL) desired device returned tensor.  Default: NULL, uses current device default tensor type. requires_grad (default: FALSE)  autograd record operations returned tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"fftfreq — torch_fft_fftfreq","text":"convention, torch_fft_fft() returns positive frequency terms first, followed negative frequencies reverse order, f[-] 0 < <= n/2 gives negative frequency terms. FFT length n inputs spaced length unit d, frequencies : f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n) even lengths, Nyquist frequency f[n/2] can thought either negative positive. fftfreq() follows NumPy’s convention taking negative.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_fftfreq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"fftfreq — torch_fft_fftfreq","text":"","code":"if (torch_is_installed()) { torch_fft_fftfreq(5) # Nyquist frequency at f[3] is positive torch_fft_fftfreq(4) # Nyquist frequency at f[3] is given as negative  } #> torch_tensor #>  0.0000 #>  0.2500 #> -0.5000 #> -0.2500 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":null,"dir":"Reference","previous_headings":"","what":"Ifft — torch_fft_ifft","title":"Ifft — torch_fft_ifft","text":"Computes one dimensional inverse discrete Fourier transform input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ifft — torch_fft_ifft","text":"","code":"torch_fft_ifft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ifft — torch_fft_ifft","text":"self (Tensor) input tensor n (int, optional) – Signal length. given, input either zero-padded trimmed length computing IFFT. dim (int, optional) – dimension along take one dimensional IFFT. norm (str, optional) – Normalization mode. backward transform, correspond : \"forward\" - normalization \"backward\" - normalize 1/n \"ortho\" - normalize 1/sqrt(n) (making IFFT orthonormal) Calling forward transform normalization mode apply overall normalization 1/n two transforms. required make ifft() exact inverse. Default \"backward\" (normalize 1/n).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_ifft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ifft — torch_fft_ifft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) t x <- torch_fft_fft(t, norm = \"backward\") torch_fft_ifft(x)   } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Irfft — torch_fft_irfft","title":"Irfft — torch_fft_irfft","text":"Computes inverse torch_fft_rfft(). Input interpreted one-sided Hermitian signal Fourier domain, produced torch_fft_rfft(). Hermitian property, output real-valued.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Irfft — torch_fft_irfft","text":"","code":"torch_fft_irfft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Irfft — torch_fft_irfft","text":"self (Tensor) input tensor representing half-Hermitian signal n (int) Output signal length. determines length output signal. given, input either zero-padded trimmed length computing real IFFT. Defaults even output: n=2*(input.size(dim) - 1). dim (int, optional) – dimension along take one dimensional real IFFT. norm (str, optional) – Normalization mode. backward transform, correspond : \"forward\" - normalization \"backward\" - normalize 1/n \"ortho\" - normalize 1/sqrt(n) (making real IFFT orthonormal) Calling forward transform (torch_fft_rfft()) normalization mode apply overall normalization 1/n two transforms. required make irfft() exact inverse. Default \"backward\" (normalize 1/n).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Irfft — torch_fft_irfft","text":"input frequencies must real-valued satisfy Hermitian property. cases imaginary component ignored. example, imaginary component zero-frequency term represented real output always ignored. correct interpretation Hermitian input depends length original data, given n. input shape correspond either odd even length signal. default, signal assumed even length odd signals round-trip properly. , recommended always pass signal length n.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_irfft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Irfft — torch_fft_irfft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 4) x <- torch_fft_rfft(t) torch_fft_irfft(x) torch_fft_irfft(x, n = t$numel())  } #> torch_tensor #>  0.0000 #>  1.0000 #>  2.0000 #>  3.0000 #>  4.0000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":null,"dir":"Reference","previous_headings":"","what":"Rfft — torch_fft_rfft","title":"Rfft — torch_fft_rfft","text":"Computes one dimensional Fourier transform real-valued input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rfft — torch_fft_rfft","text":"","code":"torch_fft_rfft(self, n = NULL, dim = -1L, norm = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rfft — torch_fft_rfft","text":"self (Tensor)  real input tensor n (int) Signal length. given, input either zero-padded trimmed length computing real FFT. dim (int, optional) – dimension along take one dimensional real FFT. norm norm (str, optional) – Normalization mode. forward transform, correspond : \"forward\" - normalize 1/n \"backward\" - normalization \"ortho\" - normalize 1/sqrt(n) (making FFT orthonormal) Calling backward transform (torch_fft_irfft()) normalization mode apply overall normalization 1/n two transforms. required make irfft() exact inverse. Default \"backward\" (normalization).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rfft — torch_fft_rfft","text":"FFT real signal Hermitian-symmetric, X[] = conj(X[-]) output contains positive frequencies Nyquist frequency. compute full output, use torch_fft_fft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fft_rfft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rfft — torch_fft_rfft","text":"","code":"if (torch_is_installed()) { t <- torch_arange(start = 0, end = 3) torch_fft_rfft(t)  } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Floating point type info — torch_finfo","title":"Floating point type info — torch_finfo","text":"list represents numerical properties floating point torch.dtype","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floating point type info — torch_finfo","text":"","code":"torch_finfo(dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_finfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floating point type info — torch_finfo","text":"dtype dtype check information","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":null,"dir":"Reference","previous_headings":"","what":"Fix — torch_fix","title":"Fix — torch_fix","text":"Fix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fix — torch_fix","text":"","code":"torch_fix(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fix — torch_fix","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fix.html","id":"fix-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"fix(input, *, out=None) -> Tensor","title":"Fix — torch_fix","text":"Alias torch_trunc()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten — torch_flatten","title":"Flatten — torch_flatten","text":"Flatten","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten — torch_flatten","text":"","code":"torch_flatten(self, dims, start_dim = 1L, end_dim = -1L, out_dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten — torch_flatten","text":"self (Tensor) input tensor. dims tensor named can pass name dimensions flatten start_dim (int) first dim flatten end_dim (int) last dim flatten out_dim name resulting dimension named tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"flatten-input-start-dim-end-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"flatten(input, start_dim=0, end_dim=-1) -> Tensor","title":"Flatten — torch_flatten","text":"Flattens contiguous range dims tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flatten.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flatten — torch_flatten","text":"","code":"if (torch_is_installed()) {  t = torch_tensor(matrix(c(1, 2), ncol = 2)) torch_flatten(t) torch_flatten(t, start_dim=2) } #> torch_tensor #>  1  2 #> [ CPUFloatType{1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":null,"dir":"Reference","previous_headings":"","what":"Flip — torch_flip","title":"Flip — torch_flip","text":"Flip","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flip — torch_flip","text":"","code":"torch_flip(self, dims)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flip — torch_flip","text":"self (Tensor) input tensor. dims (list tuple) axis flip ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"flip-input-dims-gt-tensor-","dir":"Reference","previous_headings":"","what":"flip(input, dims) -> Tensor","title":"Flip — torch_flip","text":"Reverse order n-D tensor along given axis dims.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flip — torch_flip","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 8)$view(c(2, 2, 2)) x torch_flip(x, c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   7  8 #>   5  6 #>  #> (2,.,.) =  #>   3  4 #>   1  2 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":null,"dir":"Reference","previous_headings":"","what":"Fliplr — torch_fliplr","title":"Fliplr — torch_fliplr","text":"Fliplr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fliplr — torch_fliplr","text":"","code":"torch_fliplr(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fliplr — torch_fliplr","text":"self (Tensor) Must least 2-dimensional.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fliplr — torch_fliplr","text":"Equivalent input[,-1]. Requires array least 2-D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"fliplr-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"fliplr(input) -> Tensor","title":"Fliplr — torch_fliplr","text":"Flip array left/right direction, returning new tensor. Flip entries row left/right direction. Columns preserved, appear different order .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fliplr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fliplr — torch_fliplr","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(start = 1, end = 4)$view(c(2, 2)) x torch_fliplr(x) } #> torch_tensor #>  2  1 #>  4  3 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":null,"dir":"Reference","previous_headings":"","what":"Flipud — torch_flipud","title":"Flipud — torch_flipud","text":"Flipud","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flipud — torch_flipud","text":"","code":"torch_flipud(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flipud — torch_flipud","text":"self (Tensor) Must least 1-dimensional.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Flipud — torch_flipud","text":"Equivalent input[-1,]. Requires array least 1-D.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"flipud-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"flipud(input) -> Tensor","title":"Flipud — torch_flipud","text":"Flip array /direction, returning new tensor. Flip entries column /direction. Rows preserved, appear different order .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_flipud.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Flipud — torch_flipud","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(start = 1, end = 4)$view(c(2, 2)) x torch_flipud(x) } #> torch_tensor #>  3  4 #>  1  2 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":null,"dir":"Reference","previous_headings":"","what":"Floor — torch_floor","title":"Floor — torch_floor","text":"Floor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floor — torch_floor","text":"","code":"torch_floor(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floor — torch_floor","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"floor-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"floor(input, out=NULL) -> Tensor","title":"Floor — torch_floor","text":"Returns new tensor floor elements input, largest integer less equal element. $$     \\mbox{}_{} = \\left\\lfloor \\mbox{input}_{} \\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Floor — torch_floor","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_floor(a) } #> torch_tensor #>  1 #>  0 #>  1 #> -2 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"Floor_divide — torch_floor_divide","title":"Floor_divide — torch_floor_divide","text":"Floor_divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Floor_divide — torch_floor_divide","text":"","code":"torch_floor_divide(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Floor_divide — torch_floor_divide","text":"self (Tensor) numerator tensor (Tensor Scalar) denominator","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"floor-divide-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"floor_divide(input, other, out=NULL) -> Tensor","title":"Floor_divide — torch_floor_divide","text":"Return division inputs rounded nearest integer. See torch_div type promotion broadcasting rules. $$     \\mbox{{}}_i = \\left\\lfloor \\frac{{\\mbox{{input}}_i}}{{\\mbox{{}}_i}} \\right\\rfloor $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_floor_divide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Floor_divide — torch_floor_divide","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(4.0, 3.0)) b = torch_tensor(c(2.0, 2.0)) torch_floor_divide(a, b) torch_floor_divide(a, 1.4) } #> torch_tensor #>  2 #>  2 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":null,"dir":"Reference","previous_headings":"","what":"Fmod — torch_fmod","title":"Fmod — torch_fmod","text":"Fmod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fmod — torch_fmod","text":"","code":"torch_fmod(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fmod — torch_fmod","text":"self (Tensor) dividend (Tensor float) divisor, may either number tensor shape dividend","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"fmod-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"fmod(input, other, out=NULL) -> Tensor","title":"Fmod — torch_fmod","text":"Computes element-wise remainder division. dividend divisor may contain integer floating point numbers. remainder sign dividend input. tensor, shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_fmod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fmod — torch_fmod","text":"","code":"if (torch_is_installed()) {  torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2) torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5) } #> torch_tensor #>  1.0000 #>  0.5000 #>  0.0000 #>  1.0000 #>  0.5000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":null,"dir":"Reference","previous_headings":"","what":"Frac — torch_frac","title":"Frac — torch_frac","text":"Frac","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frac — torch_frac","text":"","code":"torch_frac(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frac — torch_frac","text":"self input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"frac-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"frac(input, out=NULL) -> Tensor","title":"Frac — torch_frac","text":"Computes fractional portion element input. $$     \\mbox{}_{} = \\mbox{input}_{} - \\left\\lfloor |\\mbox{input}_{}| \\right\\rfloor * \\mbox{sgn}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_frac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frac — torch_frac","text":"","code":"if (torch_is_installed()) {  torch_frac(torch_tensor(c(1, 2.5, -3.2))) } #> torch_tensor #>  0.0000 #>  0.5000 #> -0.2000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Full — torch_full","title":"Full — torch_full","text":"Full","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Full — torch_full","text":"","code":"torch_full(   size,   fill_value,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Full — torch_full","text":"size (int...) list, tuple, torch_Size integers defining        shape output tensor. fill_value NA number fill output tensor . names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Full — torch_full","text":"Returns tensor size size filled fill_value.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Full — torch_full","text":"PyTorch 1.5 bool integral fill_value produce warning dtype set. future PyTorch release, dtype set bool fill_value return tensor torch.bool dtype, integral fill_value return tensor torch.long dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Full — torch_full","text":"","code":"if (torch_is_installed()) {  torch_full(list(2, 3), 3.141592) } #> torch_tensor #>  3.1416  3.1416  3.1416 #>  3.1416  3.1416  3.1416 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Full_like — torch_full_like","title":"Full_like — torch_full_like","text":"Full_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Full_like — torch_full_like","text":"","code":"torch_full_like(   input,   fill_value,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Full_like — torch_full_like","text":"input (Tensor) size input determine size output tensor. fill_value number fill output tensor . dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_full_like.html","id":"full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-","dir":"Reference","previous_headings":"","what":"full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,","title":"Full_like — torch_full_like","text":"memory_format=torch.preserve_format) -> Tensor Returns tensor size input filled fill_value. torch_full_like(input, fill_value) equivalent torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":null,"dir":"Reference","previous_headings":"","what":"Gather — torch_gather","title":"Gather — torch_gather","text":"Gather","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gather — torch_gather","text":"","code":"torch_gather(self, dim, index, sparse_grad = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gather — torch_gather","text":"self (Tensor) source tensor dim (int) axis along index index (LongTensor) indices elements gather sparse_grad (bool,optional) TRUE, gradient w.r.t. input sparse tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"gather-input-dim-index-sparse-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"gather(input, dim, index, sparse_grad=FALSE) -> Tensor","title":"Gather — torch_gather","text":"Gathers values along axis specified dim. 3-D tensor output specified ::   input n-dimensional tensor size \\((x_0, x_1..., x_{-1}, x_i, x_{+1}, ..., x_{n-1})\\) dim = , index must \\(n\\)-dimensional tensor size \\((x_0, x_1, ..., x_{-1}, y, x_{+1}, ..., x_{n-1})\\) \\(y \\geq 1\\) size index.","code":"out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0 out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1 out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2"},{"path":"https://torch.mlverse.org/docs/reference/torch_gather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gather — torch_gather","text":"","code":"if (torch_is_installed()) {  t = torch_tensor(matrix(c(1,2,3,4), ncol = 2, byrow = TRUE)) torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64())) } #> torch_tensor #>  1  1 #>  4  3 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Gcd — torch_gcd","title":"Gcd — torch_gcd","text":"Gcd","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gcd — torch_gcd","text":"","code":"torch_gcd(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gcd — torch_gcd","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Gcd — torch_gcd","text":"defines \\(gcd(0, 0) = 0\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"gcd-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"gcd(input, other, *, out=None) -> Tensor","title":"Gcd — torch_gcd","text":"Computes element-wise greatest common divisor (GCD) input . input must integer types.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gcd — torch_gcd","text":"","code":"if (torch_is_installed()) {  if (torch::cuda_is_available()) { a <- torch_tensor(c(5, 10, 15), dtype = torch_long(), device = \"cuda\") b <- torch_tensor(c(3, 4, 5), dtype = torch_long(), device = \"cuda\") torch_gcd(a, b) c <- torch_tensor(c(3L), device = \"cuda\") torch_gcd(a, c) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":null,"dir":"Reference","previous_headings":"","what":"Ge — torch_ge","title":"Ge — torch_ge","text":"Ge","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ge — torch_ge","text":"","code":"torch_ge(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ge — torch_ge","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ge-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ge(input, other, out=NULL) -> Tensor","title":"Ge — torch_ge","text":"Computes \\(\\mbox{input} \\geq \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ge.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ge — torch_ge","text":"","code":"if (torch_is_installed()) {  torch_ge(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  1  1 #>  0  1 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Generator object — torch_generator","title":"Create a Generator object — torch_generator","text":"torch_generator  object manages state algorithm produces pseudo random numbers. Used keyword argument many -place random sampling functions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Generator object — torch_generator","text":"","code":"torch_generator()"},{"path":"https://torch.mlverse.org/docs/reference/torch_generator.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Generator object — torch_generator","text":"","code":"if (torch_is_installed()) {  # Via string generator <- torch_generator() generator$current_seed() generator$set_current_seed(1234567L) generator$current_seed()  } #> integer64 #> [1] 1234567"},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":null,"dir":"Reference","previous_headings":"","what":"Geqrf — torch_geqrf","title":"Geqrf — torch_geqrf","text":"Geqrf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geqrf — torch_geqrf","text":"","code":"torch_geqrf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geqrf — torch_geqrf","text":"self (Tensor) input matrix","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_geqrf.html","id":"geqrf-input-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"geqrf(input, out=NULL) -> (Tensor, Tensor)","title":"Geqrf — torch_geqrf","text":"low-level function calling LAPACK directly. function returns namedtuple (, tau) defined LAPACK documentation geqrf_ . generally want use torch_qr instead. Computes QR decomposition input, without constructing \\(Q\\) \\(R\\) explicit separate matrices. Rather, directly calls underlying LAPACK function ?geqrf produces sequence 'elementary reflectors'. See LAPACK documentation geqrf_ details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":null,"dir":"Reference","previous_headings":"","what":"Ger — torch_ger","title":"Ger — torch_ger","text":"Ger","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ger — torch_ger","text":"","code":"torch_ger(self, vec2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ger — torch_ger","text":"self (Tensor) 1-D input vector vec2 (Tensor) 1-D input vector","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Ger — torch_ger","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ger-input-vec-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ger(input, vec2, out=NULL) -> Tensor","title":"Ger — torch_ger","text":"Outer product input vec2. input vector size \\(n\\) vec2 vector size \\(m\\), must matrix size \\((n \\times m)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ger.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ger — torch_ger","text":"","code":"if (torch_is_installed()) {  v1 = torch_arange(1., 5.) v2 = torch_arange(1., 4.) torch_ger(v1, v2) } #> torch_tensor #>   1   2   3   4 #>   2   4   6   8 #>   3   6   9  12 #>   4   8  12  16 #>   5  10  15  20 #> [ CPUFloatType{5,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":null,"dir":"Reference","previous_headings":"","what":"RNG state management — torch_get_rng_state","title":"RNG state management — torch_get_rng_state","text":"Low level functionality set change RNG state. recommended use torch_manual_seed() cases.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RNG state management — torch_get_rng_state","text":"","code":"torch_get_rng_state()  torch_set_rng_state(state)  cuda_get_rng_state(device = NULL)  cuda_set_rng_state(state, device = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RNG state management — torch_get_rng_state","text":"state tensor current state list containing state device - (CUDA). device cuda device index get set state. NULL gets state available devices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_get_rng_state.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"RNG state management — torch_get_rng_state","text":"torch_set_rng_state(): Sets RNG state CPU cuda_get_rng_state(): Gets RNG state CUDA. cuda_set_rng_state(): Sets RNG state CUDA.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":null,"dir":"Reference","previous_headings":"","what":"Greater — torch_greater","title":"Greater — torch_greater","text":"Greater","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greater — torch_greater","text":"","code":"torch_greater(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greater — torch_greater","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater.html","id":"greater-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"greater(input, other, *, out=None) -> Tensor","title":"Greater — torch_greater","text":"Alias torch_gt().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Greater_equal — torch_greater_equal","title":"Greater_equal — torch_greater_equal","text":"Greater_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greater_equal — torch_greater_equal","text":"","code":"torch_greater_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greater_equal — torch_greater_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_greater_equal.html","id":"greater-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"greater_equal(input, other, *, out=None) -> Tensor","title":"Greater_equal — torch_greater_equal","text":"Alias torch_ge().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":null,"dir":"Reference","previous_headings":"","what":"Gt — torch_gt","title":"Gt — torch_gt","text":"Gt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gt — torch_gt","text":"","code":"torch_gt(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gt — torch_gt","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"gt-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"gt(input, other, out=NULL) -> Tensor","title":"Gt — torch_gt","text":"Computes \\(\\mbox{input} > \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_gt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gt — torch_gt","text":"","code":"if (torch_is_installed()) {  torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  1 #>  0  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Hamming_window — torch_hamming_window","title":"Hamming_window — torch_hamming_window","text":"Hamming_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hamming_window — torch_hamming_window","text":"","code":"torch_hamming_window(   window_length,   periodic = TRUE,   alpha = 0.54,   beta = 0.46,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hamming_window — torch_hamming_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. alpha (float, optional) coefficient \\(\\alpha\\) equation beta (float, optional) coefficient \\(\\beta\\) equation dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Hamming_window — torch_hamming_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1. This is a generalized version of `torch_hann_window`."},{"path":"https://torch.mlverse.org/docs/reference/torch_hamming_window.html","id":"hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Hamming_window — torch_hamming_window","text":"Hamming window function. $$     w[n] = \\alpha - \\beta\\ \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right), $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_hamming_window(L, periodic=TRUE) equal torch_hamming_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Hann_window — torch_hann_window","title":"Hann_window — torch_hann_window","text":"Hann_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hann_window — torch_hann_window","text":"","code":"torch_hann_window(   window_length,   periodic = TRUE,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hann_window — torch_hann_window","text":"window_length (int) size returned window periodic (bool, optional) TRUE, returns window used periodic        function. False, return symmetric window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). floating point types supported. layout (torch.layout, optional) desired layout returned window tensor.          torch_strided (dense layout) supported. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Hann_window — torch_hann_window","text":"","code":"If `window_length` \\eqn{=1}, the returned window contains a single value 1."},{"path":"https://torch.mlverse.org/docs/reference/torch_hann_window.html","id":"hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Hann_window — torch_hann_window","text":"Hann window function. $$     w[n] = \\frac{1}{2}\\ \\left[1 - \\cos \\left( \\frac{2 \\pi n}{N - 1} \\right)\\right] =             \\sin^2 \\left( \\frac{\\pi n}{N - 1} \\right), $$ \\(N\\) full window size. input window_length positive integer controlling returned window size. periodic flag determines whether returned window trims last duplicate value symmetric window ready used periodic window functions like torch_stft. Therefore, periodic true, \\(N\\) formula fact \\(\\mbox{window\\_length} + 1\\). Also, always torch_hann_window(L, periodic=TRUE) equal torch_hann_window(L + 1, periodic=False)[:-1]).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":null,"dir":"Reference","previous_headings":"","what":"Heaviside — torch_heaviside","title":"Heaviside — torch_heaviside","text":"Heaviside","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heaviside — torch_heaviside","text":"","code":"torch_heaviside(self, values)"},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heaviside — torch_heaviside","text":"self (Tensor) input tensor. values (Tensor) values use input zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"heaviside-input-values-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"heaviside(input, values, *, out=None) -> Tensor","title":"Heaviside — torch_heaviside","text":"Computes Heaviside step function element input. Heaviside step function defined : $$ \\mbox{{heaviside}}(input, values) = \\begin{array}{ll}  0, & \\mbox{input < 0}\\\\  values, & \\mbox{input == 0}\\\\  1, & \\mbox{input > 0}  \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_heaviside.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heaviside — torch_heaviside","text":"","code":"if (torch_is_installed()) {  input <- torch_tensor(c(-1.5, 0, 2.0)) values <- torch_tensor(c(0.5)) torch_heaviside(input, values) values <- torch_tensor(c(1.2, -2.0, 3.5)) torch_heaviside(input, values) } #> torch_tensor #>  0 #> -2 #>  1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":null,"dir":"Reference","previous_headings":"","what":"Histc — torch_histc","title":"Histc — torch_histc","text":"Histc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histc — torch_histc","text":"","code":"torch_histc(self, bins = 100L, min = 0L, max = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histc — torch_histc","text":"self (Tensor) input tensor. bins (int) number histogram bins min (int) lower end range (inclusive) max (int) upper end range (inclusive)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"histc-input-bins-min-max-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"histc(input, bins=100, min=0, max=0, out=NULL) -> Tensor","title":"Histc — torch_histc","text":"Computes histogram tensor. elements sorted equal width bins min max. min max zero, minimum maximum values data used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_histc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histc — torch_histc","text":"","code":"if (torch_is_installed()) {  torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3) } #> torch_tensor #>  0 #>  2 #>  1 #>  0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Hstack — torch_hstack","title":"Hstack — torch_hstack","text":"Hstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hstack — torch_hstack","text":"","code":"torch_hstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hstack — torch_hstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"hstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"hstack(tensors, *, out=None) -> Tensor","title":"Hstack — torch_hstack","text":"Stack tensors sequence horizontally (column wise). equivalent concatenation along first axis 1-D tensors, along second axis tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hstack — torch_hstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_hstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_hstack(list(a,b)) } #> torch_tensor #>  1  4 #>  2  5 #>  3  6 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":null,"dir":"Reference","previous_headings":"","what":"Hypot — torch_hypot","title":"Hypot — torch_hypot","text":"Hypot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hypot — torch_hypot","text":"","code":"torch_hypot(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hypot — torch_hypot","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"hypot-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"hypot(input, other, *, out=None) -> Tensor","title":"Hypot — torch_hypot","text":"Given legs right triangle, return hypotenuse. $$ \\mbox{}_{} = \\sqrt{\\mbox{input}_{}^{2} + \\mbox{}_{}^{2}} $$ shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_hypot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hypot — torch_hypot","text":"","code":"if (torch_is_installed()) {  torch_hypot(torch_tensor(c(4.0)), torch_tensor(c(3.0, 4.0, 5.0))) } #> torch_tensor #>  5.0000 #>  5.6569 #>  6.4031 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":null,"dir":"Reference","previous_headings":"","what":"I0 — torch_i0","title":"I0 — torch_i0","text":"I0","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"I0 — torch_i0","text":"","code":"torch_i0(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"I0 — torch_i0","text":"self (Tensor) input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"i-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"i0(input, *, out=None) -> Tensor","title":"I0 — torch_i0","text":"Computes zeroth order modified Bessel function first kind element input. $$ \\mbox{}_{} = I_0(\\mbox{input}_{}) = \\sum_{k=0}^{\\infty} \\frac{(\\mbox{input}_{}^2/4)^k}{(k!)^2} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_i0.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"I0 — torch_i0","text":"","code":"if (torch_is_installed()) {  torch_i0(torch_arange(start = 0, end = 5, dtype=torch_float32())) } #> torch_tensor #>   1.0000 #>   1.2661 #>   2.2796 #>   4.8808 #>  11.3019 #>  27.2399 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Integer type info — torch_iinfo","title":"Integer type info — torch_iinfo","text":"list represents numerical properties integer type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Integer type info — torch_iinfo","text":"","code":"torch_iinfo(dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_iinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Integer type info — torch_iinfo","text":"dtype dtype get information .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":null,"dir":"Reference","previous_headings":"","what":"Imag — torch_imag","title":"Imag — torch_imag","text":"Imag","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Imag — torch_imag","text":"","code":"torch_imag(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Imag — torch_imag","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"imag-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"imag(input) -> Tensor","title":"Imag — torch_imag","text":"Returns imaginary part input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Imag — torch_imag","text":"yet implemented. $$     \\mbox{}_{} = imag(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_imag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Imag — torch_imag","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ torch_imag(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Index torch tensors — torch_index","title":"Index torch tensors — torch_index","text":"Helper functions index tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Index torch tensors — torch_index","text":"","code":"torch_index(self, indices)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Index torch tensors — torch_index","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":null,"dir":"Reference","previous_headings":"","what":"Modify values selected by indices. — torch_index_put","title":"Modify values selected by indices. — torch_index_put","text":"Modify values selected indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Modify values selected by indices. — torch_index_put","text":"","code":"torch_index_put(self, indices, values, accumulate = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Modify values selected by indices. — torch_index_put","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype. values (Tensor) values replaced indexed location. Used torch_index_put torch_index_put_. accumulate (bool) Wether instead replacing current values values, want add .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":null,"dir":"Reference","previous_headings":"","what":"In-place version of torch_index_put. — torch_index_put_","title":"In-place version of torch_index_put. — torch_index_put_","text":"-place version torch_index_put.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"In-place version of torch_index_put. — torch_index_put_","text":"","code":"torch_index_put_(self, indices, values, accumulate = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_put_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"In-place version of torch_index_put. — torch_index_put_","text":"self (Tensor) Tensor indexed. indices (List[Tensor]) List indices. Indices torch tensors torch_long() dtype. values (Tensor) values replaced indexed location. Used torch_index_put torch_index_put_. accumulate (bool) Wether instead replacing current values values, want add .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Index_select — torch_index_select","title":"Index_select — torch_index_select","text":"Index_select","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Index_select — torch_index_select","text":"","code":"torch_index_select(self, dim, index)"},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Index_select — torch_index_select","text":"self (Tensor) input tensor. dim (int) dimension index index (LongTensor) 1-D tensor containing indices index","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Index_select — torch_index_select","text":"returned tensor use storage original tensor.  different shape expected, silently change correct shape, reallocating underlying storage necessary.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"index-select-input-dim-index-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"index_select(input, dim, index, out=NULL) -> Tensor","title":"Index_select — torch_index_select","text":"Returns new tensor indexes input tensor along dimension dim using entries index LongTensor. returned tensor number dimensions original tensor (input).  dim\\ th dimension size length index; dimensions size original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_index_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Index_select — torch_index_select","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) x indices = torch_tensor(c(1, 3), dtype = torch_int64()) torch_index_select(x, 1, indices) torch_index_select(x, 2, indices) } #> torch_tensor #> -0.2243  0.0370 #> -0.5962  2.5766 #> -0.5611 -0.4842 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_install_path.html","id":null,"dir":"Reference","previous_headings":"","what":"A simple exported version of install_path Returns the torch installation path. — torch_install_path","title":"A simple exported version of install_path Returns the torch installation path. — torch_install_path","text":"simple exported version install_path Returns torch installation path.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_install_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A simple exported version of install_path Returns the torch installation path. — torch_install_path","text":"","code":"torch_install_path()"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse — torch_inverse","title":"Inverse — torch_inverse","text":"Inverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse — torch_inverse","text":"","code":"torch_inverse(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse — torch_inverse","text":"self (Tensor) input tensor size \\((*, n, n)\\) * zero                    batch dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Inverse — torch_inverse","text":"","code":"Irrespective of the original strides, the returned tensors will be transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`"},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"inverse-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"inverse(input, out=NULL) -> Tensor","title":"Inverse — torch_inverse","text":"Takes inverse square matrix input. input can batches 2D square tensors, case function return tensor composed individual inverses.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse — torch_inverse","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ x = torch_rand(c(4, 4)) y = torch_inverse(x) z = torch_mm(x, y) z torch_max(torch_abs(z - torch_eye(4))) # Max non-zero # Batched inverse example x = torch_randn(c(2, 3, 4, 4)) y = torch_inverse(x) z = torch_matmul(x, y) torch_max(torch_abs(z - torch_eye(4)$expand_as(x))) # Max non-zero } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_complex — torch_is_complex","title":"Is_complex — torch_is_complex","text":"Is_complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_complex — torch_is_complex","text":"","code":"torch_is_complex(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_complex — torch_is_complex","text":"self (Tensor) PyTorch tensor test","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_complex.html","id":"is-complex-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_complex(input) -> (bool)","title":"Is_complex — torch_is_complex","text":"Returns TRUE data type input complex data type .e., one torch_complex64, torch.complex128.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_floating_point — torch_is_floating_point","title":"Is_floating_point — torch_is_floating_point","text":"Is_floating_point","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_floating_point — torch_is_floating_point","text":"","code":"torch_is_floating_point(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_floating_point — torch_is_floating_point","text":"self (Tensor) PyTorch tensor test","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_floating_point.html","id":"is-floating-point-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_floating_point(input) -> (bool)","title":"Is_floating_point — torch_is_floating_point","text":"Returns TRUE data type input floating point data type .e., one torch_float64, torch.float32 torch.float16.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_installed.html","id":null,"dir":"Reference","previous_headings":"","what":"Verifies if torch is installed — torch_is_installed","title":"Verifies if torch is installed — torch_is_installed","text":"Verifies torch installed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_installed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Verifies if torch is installed — torch_is_installed","text":"","code":"torch_is_installed()"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Is_nonzero — torch_is_nonzero","title":"Is_nonzero — torch_is_nonzero","text":"Is_nonzero","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is_nonzero — torch_is_nonzero","text":"","code":"torch_is_nonzero(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is_nonzero — torch_is_nonzero","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"is-nonzero-input-gt-bool-","dir":"Reference","previous_headings":"","what":"is_nonzero(input) -> (bool)","title":"Is_nonzero — torch_is_nonzero","text":"Returns TRUE input single element tensor equal zero type conversions. .e. equal torch_tensor(c(0)) torch_tensor(c(0)) torch_tensor(c(FALSE)). Throws RuntimeError torch_numel() != 1 (even case sparse tensors).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_is_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is_nonzero — torch_is_nonzero","text":"","code":"if (torch_is_installed()) {  torch_is_nonzero(torch_tensor(c(0.))) torch_is_nonzero(torch_tensor(c(1.5))) torch_is_nonzero(torch_tensor(c(FALSE))) torch_is_nonzero(torch_tensor(c(3))) if (FALSE) { torch_is_nonzero(torch_tensor(c(1, 3, 5))) torch_is_nonzero(torch_tensor(c())) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":null,"dir":"Reference","previous_headings":"","what":"Isclose — torch_isclose","title":"Isclose — torch_isclose","text":"Isclose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isclose — torch_isclose","text":"","code":"torch_isclose(self, other, rtol = 1e-05, atol = 1e-08, equal_nan = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isclose — torch_isclose","text":"self (Tensor) first tensor compare (Tensor) second tensor compare rtol (float, optional) relative tolerance. Default: 1e-05 atol (float, optional) absolute tolerance. Default: 1e-08 equal_nan (bool, optional) TRUE, two NaN s considered equal. Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"isclose-input-other-rtol-e-atol-e-equal-nan-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -> Tensor","title":"Isclose — torch_isclose","text":"Returns new tensor boolean elements representing element input \"close\" corresponding element . Closeness defined : $$     \\vert \\mbox{input} - \\mbox{} \\vert \\leq \\mbox{atol} + \\mbox{rtol} \\times \\vert \\mbox{} \\vert $$ input finite. input /nonfinite close equal, NaNs considered equal equal_nan TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isclose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isclose — torch_isclose","text":"","code":"if (torch_is_installed()) {  torch_isclose(torch_tensor(c(1., 2, 3)), torch_tensor(c(1 + 1e-10, 3, 4))) torch_isclose(torch_tensor(c(Inf, 4)), torch_tensor(c(Inf, 6)), rtol=.5) } #> torch_tensor #>  1 #>  1 #> [ CPUBoolType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Isfinite — torch_isfinite","title":"Isfinite — torch_isfinite","text":"Isfinite","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isfinite — torch_isfinite","text":"","code":"torch_isfinite(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isfinite — torch_isfinite","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isfinite — torch_isfinite","text":"Returns new tensor boolean elements representing element Finite .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isfinite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isfinite — torch_isfinite","text":"","code":"if (torch_is_installed()) {  torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN))) } #> torch_tensor #>  1 #>  0 #>  1 #>  0 #>  0 #> [ CPUBoolType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isinf — torch_isinf","title":"Isinf — torch_isinf","text":"Isinf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isinf — torch_isinf","text":"","code":"torch_isinf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isinf — torch_isinf","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isinf — torch_isinf","text":"Returns new tensor boolean elements representing element +/-INF .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isinf — torch_isinf","text":"","code":"if (torch_is_installed()) {  torch_isinf(torch_tensor(c(1, Inf, 2, -Inf, NaN))) } #> torch_tensor #>  0 #>  1 #>  0 #>  1 #>  0 #> [ CPUBoolType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":null,"dir":"Reference","previous_headings":"","what":"Isnan — torch_isnan","title":"Isnan — torch_isnan","text":"Isnan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isnan — torch_isnan","text":"","code":"torch_isnan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isnan — torch_isnan","text":"self (Tensor) tensor check","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Isnan — torch_isnan","text":"Returns new tensor boolean elements representing element NaN .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isnan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isnan — torch_isnan","text":"","code":"if (torch_is_installed()) {  torch_isnan(torch_tensor(c(1, NaN, 2))) } #> torch_tensor #>  0 #>  1 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isneginf — torch_isneginf","title":"Isneginf — torch_isneginf","text":"Isneginf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isneginf — torch_isneginf","text":"","code":"torch_isneginf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isneginf — torch_isneginf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"isneginf-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"isneginf(input, *, out=None) -> Tensor","title":"Isneginf — torch_isneginf","text":"Tests element input negative infinity .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isneginf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isneginf — torch_isneginf","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(-Inf, Inf, 1.2)) torch_isneginf(a) } #> torch_tensor #>  1 #>  0 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Isposinf — torch_isposinf","title":"Isposinf — torch_isposinf","text":"Isposinf","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isposinf — torch_isposinf","text":"","code":"torch_isposinf(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isposinf — torch_isposinf","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"isposinf-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"isposinf(input, *, out=None) -> Tensor","title":"Isposinf — torch_isposinf","text":"Tests element input positive infinity .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isposinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isposinf — torch_isposinf","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(-Inf, Inf, 1.2)) torch_isposinf(a) } #> torch_tensor #>  0 #>  1 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":null,"dir":"Reference","previous_headings":"","what":"Isreal — torch_isreal","title":"Isreal — torch_isreal","text":"Isreal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isreal — torch_isreal","text":"","code":"torch_isreal(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isreal — torch_isreal","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"isreal-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"isreal(input) -> Tensor","title":"Isreal — torch_isreal","text":"Returns new tensor boolean elements representing element input real-valued . real-valued types considered real. Complex values considered real imaginary part 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_isreal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isreal — torch_isreal","text":"","code":"if (torch_is_installed()) { if (FALSE) { torch_isreal(torch_tensor(c(1, 1+1i, 2+0i))) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":null,"dir":"Reference","previous_headings":"","what":"Istft — torch_istft","title":"Istft — torch_istft","text":"Inverse short time Fourier Transform. expected inverse torch_stft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Istft — torch_istft","text":"","code":"torch_istft(   self,   n_fft,   hop_length = NULL,   win_length = NULL,   window = list(),   center = TRUE,   normalized = FALSE,   onesided = NULL,   length = NULL,   return_complex = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Istft — torch_istft","text":"self (Tensor) input tensor. Expected output torch_stft(), can either complex (channel, fft_size, n_frame), real (channel, fft_size, n_frame, 2) channel dimension optional. n_fft (int) Size Fourier transform hop_length (Optional[int]) distance neighboring sliding window frames. (Default: n_fft %% 4) win_length (Optional[int]) size window frame STFT filter. (Default: n_fft) window (Optional(torch.Tensor)) optional window function. (Default: torch_ones(win_length)) center (bool) Whether input padded sides \\(t\\)-th frame centered time \\(t \\times \\mbox{hop\\_length}\\). (Default: TRUE) normalized (bool) Whether STFT normalized. (Default: FALSE) onesided (Optional(bool)) Whether STFT onesided. (Default: TRUE n_fft != fft_size input size) length (Optional(int)]) amount trim signal (.e. original signal length). (Default: whole signal) return_complex (Optional(bool)) Whether output complex, input assumed derive real signal window. Note incompatible onesided=TRUE. (Default: FALSE)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_istft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Istft — torch_istft","text":"parameters (+ additional optional parameter length) return least squares estimation original signal. algorithm check using NOLA condition (nonzero overlap). Important consideration parameters window center envelop created summation windows never zero certain point time. Specifically, \\(\\sum_{t=-\\infty}^{\\infty} |w|^2(n-t\\times hop_length) \\neq 0\\). Since torch_stft() discards elements end signal fit frame, istft may return shorter signal original signal (can occur center FALSE since signal padded). center TRUE, padding e.g. 'constant', 'reflect', etc. Left padding can trimmed exactly can calculated right padding calculated without additional information. Example: Suppose last window : [c(17, 18, 0, 0, 0) vs c(18, 0, 0, 0, 0) n_fft, hop_length, win_length prevents calculation right padding. additional values zeros reflection signal providing length useful. length None padding aggressively removed (loss signal). D. W. Griffin J. S. Lim, \"Signal estimation modified short-time Fourier transform,\" IEEE Trans. ASSP, vol.32, .2, pp.236-243, Apr. 1984.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaiser_window — torch_kaiser_window","title":"Kaiser_window — torch_kaiser_window","text":"Kaiser_window","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaiser_window — torch_kaiser_window","text":"","code":"torch_kaiser_window(   window_length,   periodic,   beta,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaiser_window — torch_kaiser_window","text":"window_length (int) length window. periodic (bool, optional) TRUE, returns periodic window suitable use spectral analysis.        FALSE, returns symmetric window suitable use filter design. beta (float, optional) shape parameter window. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Kaiser_window — torch_kaiser_window","text":"window_length one, returned window single element tensor containing one.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kaiser_window.html","id":"kaiser-window-window-length-periodic-true-beta-dtype-none-layout-torch-strided-device-none-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"kaiser_window(window_length, periodic=TRUE, beta=12.0, *, dtype=None, layout=torch.strided, device=None, requires_grad=FALSE) -> Tensor","title":"Kaiser_window — torch_kaiser_window","text":"Computes Kaiser window window length window_length shape parameter beta. Let I_0 zeroth order modified Bessel function first kind (see torch_i0()) N = L - 1 periodic FALSE L periodic TRUE, L window_length. function computes: $$     out_i = I_0 \\left( \\beta \\sqrt{1 - \\left( {\\frac{- N/2}{N/2}} \\right) ^2 } \\right) / I_0( \\beta ) $$ Calling torch_kaiser_window(L, B, periodic=TRUE) equivalent calling torch_kaiser_window(L + 1, B, periodic=FALSE)[:-1]). periodic argument intended helpful shorthand produce periodic window input functions like torch_stft().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kron.html","id":null,"dir":"Reference","previous_headings":"","what":"Kronecker product — torch_kron","title":"Kronecker product — torch_kron","text":"Computes Kronecker product self .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kron.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kronecker product — torch_kron","text":"","code":"torch_kron(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_kron.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kronecker product — torch_kron","text":"self (Tensor) input Tensor (Tensor) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":null,"dir":"Reference","previous_headings":"","what":"Kthvalue — torch_kthvalue","title":"Kthvalue — torch_kthvalue","text":"Kthvalue","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kthvalue — torch_kthvalue","text":"","code":"torch_kthvalue(self, k, dim = -1L, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kthvalue — torch_kthvalue","text":"self (Tensor) input tensor. k (int) k k-th smallest element dim (int, optional) dimension find kth value along keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Kthvalue — torch_kthvalue","text":"Returns namedtuple (values, indices) values k th smallest element row input tensor given dimension dim. indices index location element found. dim given, last dimension input chosen. keepdim TRUE, values indices tensors size input, except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting values indices tensors 1 fewer dimension input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_kthvalue.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kthvalue — torch_kthvalue","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 6) x torch_kthvalue(x, 4) x <- torch_arange(1,6)$resize_(c(2,3)) x torch_kthvalue(x, 2, 1, TRUE) } #> [[1]] #> torch_tensor #>  4  5  6 #> [ CPUFloatType{1,3} ] #>  #> [[2]] #> torch_tensor #>  1  1  1 #> [ CPULongType{1,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the corresponding layout — torch_layout","title":"Creates the corresponding layout — torch_layout","text":"Creates corresponding layout","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the corresponding layout — torch_layout","text":"","code":"torch_strided()  torch_sparse_coo()"},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":null,"dir":"Reference","previous_headings":"","what":"Lcm — torch_lcm","title":"Lcm — torch_lcm","text":"Lcm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lcm — torch_lcm","text":"","code":"torch_lcm(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lcm — torch_lcm","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Lcm — torch_lcm","text":"defines \\(lcm(0, 0) = 0\\) \\(lcm(0, ) = 0\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"lcm-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"lcm(input, other, *, out=None) -> Tensor","title":"Lcm — torch_lcm","text":"Computes element-wise least common multiple (LCM) input . input must integer types.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lcm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lcm — torch_lcm","text":"","code":"if (torch_is_installed()) {  if (torch::cuda_is_available()) { a <- torch_tensor(c(5, 10, 15), dtype = torch_long(), device = \"cuda\") b <- torch_tensor(c(3, 4, 5), dtype = torch_long(), device = \"cuda\") torch_lcm(a, b) c <- torch_tensor(c(3L), device = \"cuda\") torch_lcm(a, c) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":null,"dir":"Reference","previous_headings":"","what":"Le — torch_le","title":"Le — torch_le","text":"Le","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Le — torch_le","text":"","code":"torch_le(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Le — torch_le","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"le-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"le(input, other, out=NULL) -> Tensor","title":"Le — torch_le","text":"Computes \\(\\mbox{input} \\leq \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_le.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Le — torch_le","text":"","code":"if (torch_is_installed()) {  torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  1  0 #>  1  1 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":null,"dir":"Reference","previous_headings":"","what":"Lerp — torch_lerp","title":"Lerp — torch_lerp","text":"Lerp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lerp — torch_lerp","text":"","code":"torch_lerp(self, end, weight)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lerp — torch_lerp","text":"self (Tensor) tensor starting points end (Tensor) tensor ending points weight (float tensor) weight interpolation formula","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"lerp-input-end-weight-out-null-","dir":"Reference","previous_headings":"","what":"lerp(input, end, weight, out=NULL)","title":"Lerp — torch_lerp","text":"linear interpolation two tensors start (given input) end based scalar tensor weight returns resulting tensor. $$     \\mbox{}_i = \\mbox{start}_i + \\mbox{weight}_i \\times (\\mbox{end}_i - \\mbox{start}_i) $$ shapes start end must broadcastable . weight tensor, shapes weight, start, end must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lerp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lerp — torch_lerp","text":"","code":"if (torch_is_installed()) {  start = torch_arange(1, 4) end = torch_empty(4)$fill_(10) start end torch_lerp(start, end, 0.5) torch_lerp(start, end, torch_full_like(start, 0.5)) } #> torch_tensor #>  5.5000 #>  6.0000 #>  6.5000 #>  7.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":null,"dir":"Reference","previous_headings":"","what":"Less — torch_less","title":"Less — torch_less","text":"Less","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Less — torch_less","text":"","code":"torch_less(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Less — torch_less","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less.html","id":"less-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"less(input, other, *, out=None) -> Tensor","title":"Less — torch_less","text":"Alias torch_lt().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Less_equal — torch_less_equal","title":"Less_equal — torch_less_equal","text":"Less_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Less_equal — torch_less_equal","text":"","code":"torch_less_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Less_equal — torch_less_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_less_equal.html","id":"less-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"less_equal(input, other, *, out=None) -> Tensor","title":"Less_equal — torch_less_equal","text":"Alias torch_le().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Lgamma — torch_lgamma","title":"Lgamma — torch_lgamma","text":"Lgamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lgamma — torch_lgamma","text":"","code":"torch_lgamma(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lgamma — torch_lgamma","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"lgamma-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lgamma(input, out=NULL) -> Tensor","title":"Lgamma — torch_lgamma","text":"Computes logarithm gamma function input. $$     \\mbox{}_{} = \\log \\Gamma(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lgamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lgamma — torch_lgamma","text":"","code":"if (torch_is_installed()) {  a = torch_arange(0.5, 2, 0.5) torch_lgamma(a) } #> torch_tensor #>  0.5724 #>  0.0000 #> -0.1208 #>  0.0000 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Linspace — torch_linspace","title":"Linspace — torch_linspace","text":"Linspace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linspace — torch_linspace","text":"","code":"torch_linspace(   start,   end,   steps = 100,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linspace — torch_linspace","text":"start (float) starting value set points end (float) ending value set points steps (int) number points sample start        end. Default: 100. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Linspace — torch_linspace","text":"Returns one-dimensional tensor steps equally spaced points start end. output tensor 1-D size steps.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_linspace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linspace — torch_linspace","text":"","code":"if (torch_is_installed()) {  torch_linspace(3, 10, steps=5) torch_linspace(-10, 10, steps=5) torch_linspace(start=-10, end=10, steps=5) torch_linspace(start=-10, end=10, steps=1) } #> torch_tensor #> -10 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":null,"dir":"Reference","previous_headings":"","what":"Loads a saved object — torch_load","title":"Loads a saved object — torch_load","text":"Loads saved object","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loads a saved object — torch_load","text":"","code":"torch_load(path, device = \"cpu\")"},{"path":"https://torch.mlverse.org/docs/reference/torch_load.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loads a saved object — torch_load","text":"path path saved object device device load tensors . default load cpu can also load cuda device. NULL device tensor saved reused.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":null,"dir":"Reference","previous_headings":"","what":"Log — torch_log","title":"Log — torch_log","text":"Log","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log — torch_log","text":"","code":"torch_log(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log — torch_log","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log(input, out=NULL) -> Tensor","title":"Log — torch_log","text":"Returns new tensor natural logarithm elements input. $$     y_{} = \\log_{e} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log — torch_log","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_log(a) } #> torch_tensor #> nan #> nan #> nan #> nan #> nan #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":null,"dir":"Reference","previous_headings":"","what":"Log10 — torch_log10","title":"Log10 — torch_log10","text":"Log10","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log10 — torch_log10","text":"","code":"torch_log10(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log10 — torch_log10","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log10(input, out=NULL) -> Tensor","title":"Log10 — torch_log10","text":"Returns new tensor logarithm base 10 elements input. $$     y_{} = \\log_{10} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log10 — torch_log10","text":"","code":"if (torch_is_installed()) {  a = torch_rand(5) a torch_log10(a) } #> torch_tensor #> -0.1356 #> -0.4927 #> -0.0069 #> -0.8804 #> -0.8490 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":null,"dir":"Reference","previous_headings":"","what":"Log1p — torch_log1p","title":"Log1p — torch_log1p","text":"Log1p","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log1p — torch_log1p","text":"","code":"torch_log1p(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log1p — torch_log1p","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log1p — torch_log1p","text":"function accurate torch_log small values input","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"log-p-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log1p(input, out=NULL) -> Tensor","title":"Log1p — torch_log1p","text":"Returns new tensor natural logarithm (1 + input). $$     y_i = \\log_{e} (x_i + 1) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log1p.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log1p — torch_log1p","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_log1p(a) } #> torch_tensor #> -1.4409 #> -1.0291 #>     nan #>  0.7767 #>  0.1844 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":null,"dir":"Reference","previous_headings":"","what":"Log2 — torch_log2","title":"Log2 — torch_log2","text":"Log2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Log2 — torch_log2","text":"","code":"torch_log2(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Log2 — torch_log2","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"log-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"log2(input, out=NULL) -> Tensor","title":"Log2 — torch_log2","text":"Returns new tensor logarithm base 2 elements input. $$     y_{} = \\log_{2} (x_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_log2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Log2 — torch_log2","text":"","code":"if (torch_is_installed()) {  a = torch_rand(5) a torch_log2(a) } #> torch_tensor #> -0.1048 #> -3.1116 #> -0.3869 #> -0.6945 #> -1.8945 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logaddexp — torch_logaddexp","title":"Logaddexp — torch_logaddexp","text":"Logaddexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logaddexp — torch_logaddexp","text":"","code":"torch_logaddexp(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logaddexp — torch_logaddexp","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"logaddexp-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logaddexp(input, other, *, out=None) -> Tensor","title":"Logaddexp — torch_logaddexp","text":"Logarithm sum exponentiations inputs. Calculates pointwise \\(\\log\\left(e^x + e^y\\right)\\). function useful statistics calculated probabilities events may small exceed range normal floating point numbers. cases logarithm calculated probability stored. function allows adding probabilities stored fashion. op disambiguated torch_logsumexp() performs reduction single tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logaddexp — torch_logaddexp","text":"","code":"if (torch_is_installed()) {  torch_logaddexp(torch_tensor(c(-1.0)), torch_tensor(c(-1.0, -2, -3))) torch_logaddexp(torch_tensor(c(-100.0, -200, -300)), torch_tensor(c(-1.0, -2, -3))) torch_logaddexp(torch_tensor(c(1.0, 2000, 30000)), torch_tensor(c(-1.0, -2, -3))) } #> torch_tensor #>      1.1269 #>   2000.0000 #>  30000.0000 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":null,"dir":"Reference","previous_headings":"","what":"Logaddexp2 — torch_logaddexp2","title":"Logaddexp2 — torch_logaddexp2","text":"Logaddexp2","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logaddexp2 — torch_logaddexp2","text":"","code":"torch_logaddexp2(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logaddexp2 — torch_logaddexp2","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logaddexp2.html","id":"logaddexp-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logaddexp2(input, other, *, out=None) -> Tensor","title":"Logaddexp2 — torch_logaddexp2","text":"Logarithm sum exponentiations inputs base-2. Calculates pointwise \\(\\log_2\\left(2^x + 2^y\\right)\\). See torch_logaddexp() details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logcumsumexp — torch_logcumsumexp","title":"Logcumsumexp — torch_logcumsumexp","text":"Logcumsumexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logcumsumexp — torch_logcumsumexp","text":"","code":"torch_logcumsumexp(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logcumsumexp — torch_logcumsumexp","text":"self (Tensor) input tensor. dim (int) dimension operation ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"logcumsumexp-input-dim-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logcumsumexp(input, dim, *, out=None) -> Tensor","title":"Logcumsumexp — torch_logcumsumexp","text":"Returns logarithm cumulative summation exponentiation elements input dimension dim. summation index \\(j\\) given dim indices \\(\\), result $$         \\mbox{logcumsumexp}(x)_{ij} = \\log \\sum\\limits_{j=0}^{} \\exp(x_{ij}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logcumsumexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logcumsumexp — torch_logcumsumexp","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(10)) torch_logcumsumexp(a, dim=1) } #> torch_tensor #>  0.7236 #>  0.9456 #>  1.1725 #>  2.1297 #>  2.2222 #>  2.2610 #>  2.4665 #>  2.5697 #>  2.6306 #>  2.6780 #> [ CPUFloatType{10} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Logdet — torch_logdet","title":"Logdet — torch_logdet","text":"Logdet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logdet — torch_logdet","text":"","code":"torch_logdet(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logdet — torch_logdet","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logdet — torch_logdet","text":"","code":"Result is `-inf` if `input` has zero log determinant, and is `NaN` if `input` has negative determinant. Backward through `logdet` internally uses SVD results when `input` is not invertible. In this case, double backward through `logdet` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"logdet-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"logdet(input) -> Tensor","title":"Logdet — torch_logdet","text":"Calculates log determinant square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logdet — torch_logdet","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) torch_det(A) torch_logdet(A) A A$det() A$det()$log() } #> torch_tensor #> nan #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_and — torch_logical_and","title":"Logical_and — torch_logical_and","text":"Logical_and","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_and — torch_logical_and","text":"","code":"torch_logical_and(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_and — torch_logical_and","text":"self (Tensor) input tensor. (Tensor) tensor compute ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"logical-and-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_and(input, other, out=NULL) -> Tensor","title":"Logical_and — torch_logical_and","text":"Computes element-wise logical given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_and.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_and — torch_logical_and","text":"","code":"if (torch_is_installed()) {  torch_logical_and(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_and(a, b) if (FALSE) { # \\dontrun{ torch_logical_and(a, b, out=torch_empty(4, dtype=torch_bool())) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_not — torch_logical_not","title":"Logical_not — torch_logical_not","text":"Logical_not","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_not — torch_logical_not","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"logical-not-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_not(input, out=NULL) -> Tensor","title":"Logical_not — torch_logical_not","text":"Computes element-wise logical given input tensor. specified, output tensor bool dtype. input tensor bool tensor, zeros treated FALSE non-zeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_not — torch_logical_not","text":"","code":"if (torch_is_installed()) {  torch_logical_not(torch_tensor(c(TRUE, FALSE))) torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8())) torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double())) } #> torch_tensor #>  1 #>  0 #>  0 #> [ CPUBoolType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_or — torch_logical_or","title":"Logical_or — torch_logical_or","text":"Logical_or","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_or — torch_logical_or","text":"","code":"torch_logical_or(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_or — torch_logical_or","text":"self (Tensor) input tensor. (Tensor) tensor compute ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"logical-or-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_or(input, other, out=NULL) -> Tensor","title":"Logical_or — torch_logical_or","text":"Computes element-wise logical given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_or.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_or — torch_logical_or","text":"","code":"if (torch_is_installed()) {  torch_logical_or(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_or(a, b) if (FALSE) { # \\dontrun{ torch_logical_or(a$double(), b$double()) torch_logical_or(a$double(), b) torch_logical_or(a, b, out=torch_empty(4, dtype=torch_bool())) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical_xor — torch_logical_xor","title":"Logical_xor — torch_logical_xor","text":"Logical_xor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical_xor — torch_logical_xor","text":"","code":"torch_logical_xor(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical_xor — torch_logical_xor","text":"self (Tensor) input tensor. (Tensor) tensor compute XOR ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"logical-xor-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"logical_xor(input, other, out=NULL) -> Tensor","title":"Logical_xor — torch_logical_xor","text":"Computes element-wise logical XOR given input tensors. Zeros treated FALSE nonzeros treated TRUE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logical_xor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical_xor — torch_logical_xor","text":"","code":"if (torch_is_installed()) {  torch_logical_xor(torch_tensor(c(TRUE, FALSE, TRUE)), torch_tensor(c(TRUE, FALSE, FALSE))) a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8()) b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8()) torch_logical_xor(a, b) torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double())) torch_logical_xor(a$to(dtype=torch_double()), b) } #> torch_tensor #>  1 #>  1 #>  0 #>  0 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":null,"dir":"Reference","previous_headings":"","what":"Logit — torch_logit","title":"Logit — torch_logit","text":"Logit","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logit — torch_logit","text":"","code":"torch_logit(self, eps = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logit — torch_logit","text":"self (Tensor) input tensor. eps (float, optional) epsilon input clamp bound. Default: None","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"logit-input-eps-none-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"logit(input, eps=None, *, out=None) -> Tensor","title":"Logit — torch_logit","text":"Returns new tensor logit elements input. input clamped [eps, 1 - eps] eps None. eps None input < 0 input > 1, function yields NaN. $$     y_{} = \\ln(\\frac{z_{}}{1 - z_{}}) \\\\     z_{} = \\begin{array}{ll}         x_{} & \\mbox{eps None} \\\\         \\mbox{eps} & \\mbox{} x_{} < \\mbox{eps} \\\\         x_{} & \\mbox{} \\mbox{eps} \\leq x_{} \\leq 1 - \\mbox{eps} \\\\         1 - \\mbox{eps} & \\mbox{} x_{} > 1 - \\mbox{eps}     \\end{array} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logit — torch_logit","text":"","code":"if (torch_is_installed()) {  a <- torch_rand(5) a torch_logit(a, eps=1e-6) } #> torch_tensor #> -0.5341 #> -3.9903 #>  0.4445 #> -1.6060 #> -0.2971 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":null,"dir":"Reference","previous_headings":"","what":"Logspace — torch_logspace","title":"Logspace — torch_logspace","text":"Logspace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logspace — torch_logspace","text":"","code":"torch_logspace(   start,   end,   steps = 100,   base = 10,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logspace — torch_logspace","text":"start (float) starting value set points end (float) ending value set points steps (int) number points sample start        end. Default: 100. base (float) base logarithm function. Default: 10.0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Logspace — torch_logspace","text":"Returns one-dimensional tensor steps points logarithmically spaced base base \\({\\mbox{base}}^{\\mbox{start}}\\) \\({\\mbox{base}}^{\\mbox{end}}\\). output tensor 1-D size steps.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logspace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logspace — torch_logspace","text":"","code":"if (torch_is_installed()) {  torch_logspace(start=-10, end=10, steps=5) torch_logspace(start=0.1, end=1.0, steps=5) torch_logspace(start=0.1, end=1.0, steps=1) torch_logspace(start=2, end=2, steps=1, base=2) } #> torch_tensor #>  4 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":null,"dir":"Reference","previous_headings":"","what":"Logsumexp — torch_logsumexp","title":"Logsumexp — torch_logsumexp","text":"Logsumexp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logsumexp — torch_logsumexp","text":"","code":"torch_logsumexp(self, dim, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logsumexp — torch_logsumexp","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"logsumexp-input-dim-keepdim-false-out-null-","dir":"Reference","previous_headings":"","what":"logsumexp(input, dim, keepdim=False, out=NULL)","title":"Logsumexp — torch_logsumexp","text":"Returns log summed exponentials row input tensor given dimension dim. computation numerically stabilized. summation index \\(j\\) given dim indices \\(\\), result $$         \\mbox{logsumexp}(x)_{} = \\log \\sum_j \\exp(x_{ij}) $$ keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_logsumexp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logsumexp — torch_logsumexp","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) torch_logsumexp(a, 1) } #> torch_tensor #>  1.3163 #>  1.4516 #>  1.3805 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Lstsq — torch_lstsq","title":"Lstsq — torch_lstsq","text":"Lstsq","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lstsq — torch_lstsq","text":"self (Tensor) matrix \\(B\\) (Tensor) \\(m\\) \\(n\\) matrix \\(\\)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Lstsq — torch_lstsq","text":"","code":"The case when \\eqn{m < n} is not supported on the GPU."},{"path":"https://torch.mlverse.org/docs/reference/torch_lstsq.html","id":"lstsq-input-a-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lstsq(input, A, out=NULL) -> Tensor","title":"Lstsq — torch_lstsq","text":"Computes solution least squares least norm problems full rank matrix \\(\\) size \\((m \\times n)\\) matrix \\(B\\) size \\((m \\times k)\\). \\(m \\geq n\\), torch_lstsq() solves least-squares problem: $$    \\begin{array}{ll}    \\min_X & \\|AX-B\\|_2.    \\end{array} $$ \\(m < n\\), torch_lstsq() solves least-norm problem: $$    \\begin{array}{llll}    \\min_X & \\|X\\|_2 & \\mbox{subject } & AX = B.    \\end{array} $$ Returned tensor \\(X\\) shape \\((\\mbox{max}(m, n) \\times k)\\). first \\(n\\) rows \\(X\\) contains solution. \\(m \\geq n\\), residual sum squares solution column given sum squares elements remaining \\(m - n\\) rows column.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":null,"dir":"Reference","previous_headings":"","what":"Lt — torch_lt","title":"Lt — torch_lt","text":"Lt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lt — torch_lt","text":"","code":"torch_lt(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lt — torch_lt","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"lt-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lt(input, other, out=NULL) -> Tensor","title":"Lt — torch_lt","text":"Computes \\(\\mbox{input} < \\mbox{}\\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lt — torch_lt","text":"","code":"if (torch_is_installed()) {  torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  0 #>  1  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":null,"dir":"Reference","previous_headings":"","what":"LU — torch_lu","title":"LU — torch_lu","text":"Computes LU factorization matrix batches matrices . Returns tuple containing LU factorization pivots . Pivoting done pivot set True.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LU — torch_lu","text":"","code":"torch_lu(A, pivot = TRUE, get_infos = FALSE, out = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LU — torch_lu","text":"(Tensor) tensor factor size (, m, n)(,m,n) pivot (bool, optional) – controls whether pivoting done. Default: TRUE get_infos (bool, optional) – set True, returns info IntTensor. Default: FALSE (tuple, optional) – optional output tuple. get_infos True, elements tuple Tensor, IntTensor, IntTensor. get_infos False, elements tuple Tensor, IntTensor. Default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LU — torch_lu","text":"","code":"if (torch_is_installed()) {  A <- torch_randn(c(2, 3, 3)) torch_lu(A) } #> [[1]] #> torch_tensor #> (1,.,.) =  #>  -0.9564 -0.4735  0.4072 #>   0.8957  0.8704 -0.5667 #>   0.9415  0.9478  0.6570 #>  #> (2,.,.) =  #>   1.1940 -0.6063 -0.7687 #>  -0.0325  1.0327 -0.4151 #>  -0.2992  0.2452 -0.1442 #> [ CPUFloatType{2,3,3} ] #>  #> [[2]] #> torch_tensor #>  3  2  3 #>  1  3  3 #> [ CPUIntType{2,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Lu_solve — torch_lu_solve","title":"Lu_solve — torch_lu_solve","text":"Lu_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lu_solve — torch_lu_solve","text":"","code":"torch_lu_solve(self, LU_data, LU_pivots)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lu_solve — torch_lu_solve","text":"self (Tensor) RHS tensor size \\((*, m, k)\\), \\(*\\)                zero batch dimensions. LU_data (Tensor) pivoted LU factorization torch_lu size \\((*, m, m)\\),                       \\(*\\) zero batch dimensions. LU_pivots (IntTensor) pivots LU factorization torch_lu size \\((*, m)\\),                           \\(*\\) zero batch dimensions.                           batch dimensions LU_pivots must equal batch dimensions                           LU_data.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"lu_solve(input, LU_data, LU_pivots, out=NULL) -> Tensor","title":"Lu_solve — torch_lu_solve","text":"Returns LU solve linear system \\(Ax = b\\) using partially pivoted LU factorization torch_lu.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lu_solve — torch_lu_solve","text":"","code":"if (torch_is_installed()) { A = torch_randn(c(2, 3, 3)) b = torch_randn(c(2, 3, 1)) out = torch_lu(A) x = torch_lu_solve(b, out[[1]], out[[2]]) torch_norm(torch_bmm(A, x) - b) } #> torch_tensor #> 4.74035e-07 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":null,"dir":"Reference","previous_headings":"","what":"Lu_unpack — torch_lu_unpack","title":"Lu_unpack — torch_lu_unpack","text":"Lu_unpack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lu_unpack — torch_lu_unpack","text":"","code":"torch_lu_unpack(LU_data, LU_pivots, unpack_data = TRUE, unpack_pivots = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lu_unpack — torch_lu_unpack","text":"LU_data (Tensor) – packed LU factorization data LU_pivots (Tensor) – packed LU factorization pivots unpack_data (logical) – flag indicating data unpacked. FALSE, returned L U NULL Default: TRUE unpack_pivots (logical) – flag indicating pivots unpacked permutation matrix P. FALSE, returned P None. Default: TRUE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_lu_unpack.html","id":"lu-unpack-lu-data-lu-pivots-unpack-data-true-unpack-pivots-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"lu_unpack(LU_data, LU_pivots, unpack_data = TRUE, unpack_pivots=TRUE) -> Tensor","title":"Lu_unpack — torch_lu_unpack","text":"Unpacks data pivots LU factorization tensor tensors L U permutation tensor P LU_data_and_pivots <- torch_lu(P$matmul(L)$matmul(U)). Returns list tensors list(P tensor (permutation matrix), L tensor, U tensor)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Sets the seed for generating random numbers. — torch_manual_seed","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"Sets seed generating random numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"","code":"torch_manual_seed(seed)  local_torch_manual_seed(seed, .env = parent.frame())  with_torch_manual_seed(code, ..., seed)"},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"seed integer seed. .env environment take modifications manual_seed. code expression run context seed ... unused currently.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"local_torch_manual_seed(): Modifies torch seed environment scope. with_torch_manual_seed(): context change seed function execution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_manual_seed.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sets the seed for generating random numbers. — torch_manual_seed","text":"Currently local_torch_manual_seed with_torch_manual_seed work Tensors MPS device. can sample tensors CPU move MPS reproducibility required.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Masked_select — torch_masked_select","title":"Masked_select — torch_masked_select","text":"Masked_select","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Masked_select — torch_masked_select","text":"","code":"torch_masked_select(self, mask)"},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Masked_select — torch_masked_select","text":"self (Tensor) input tensor. mask (BoolTensor) tensor containing binary mask index ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Masked_select — torch_masked_select","text":"returned tensor use storage original tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"masked-select-input-mask-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"masked_select(input, mask, out=NULL) -> Tensor","title":"Masked_select — torch_masked_select","text":"Returns new 1-D tensor indexes input tensor according boolean mask mask BoolTensor. shapes mask tensor input tensor need match, must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_masked_select.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Masked_select — torch_masked_select","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) x mask = x$ge(0.5) mask torch_masked_select(x, mask) } #> torch_tensor #>  1.3810 #>  0.7781 #>  1.2837 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":null,"dir":"Reference","previous_headings":"","what":"Matmul — torch_matmul","title":"Matmul — torch_matmul","text":"Matmul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matmul — torch_matmul","text":"","code":"torch_matmul(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matmul — torch_matmul","text":"self (Tensor) first tensor multiplied (Tensor) second tensor multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Matmul — torch_matmul","text":"","code":"The 1-dimensional dot product version of this function does not support an `out` parameter."},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"matmul-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"matmul(input, other, out=NULL) -> Tensor","title":"Matmul — torch_matmul","text":"Matrix product two tensors. behavior depends dimensionality tensors follows: tensors 1-dimensional, dot product (scalar) returned. arguments 2-dimensional, matrix-matrix product returned. first argument 1-dimensional second argument 2-dimensional, 1 prepended dimension purpose matrix multiply. matrix multiply, prepended dimension removed. first argument 2-dimensional second argument 1-dimensional, matrix-vector product returned. arguments least 1-dimensional least one argument N-dimensional (N > 2), batched matrix multiply returned.  first argument 1-dimensional, 1 prepended dimension purpose batched matrix multiply removed .  second argument 1-dimensional, 1 appended dimension purpose batched matrix multiple removed . non-matrix (.e. batch) dimensions broadcasted  (thus must broadcastable).  example, input \\((j \\times 1 \\times n \\times m)\\) tensor \\((k \\times m \\times p)\\) tensor, \\((j \\times k \\times n \\times p)\\) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matmul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matmul — torch_matmul","text":"","code":"if (torch_is_installed()) {  # vector x vector tensor1 = torch_randn(c(3)) tensor2 = torch_randn(c(3)) torch_matmul(tensor1, tensor2) # matrix x vector tensor1 = torch_randn(c(3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2) # batched matrix x broadcasted vector tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4)) torch_matmul(tensor1, tensor2) # batched matrix x batched matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(10, 4, 5)) torch_matmul(tensor1, tensor2) # batched matrix x broadcasted matrix tensor1 = torch_randn(c(10, 3, 4)) tensor2 = torch_randn(c(4, 5)) torch_matmul(tensor1, tensor2) } #> torch_tensor #> (1,.,.) =  #>   0.3851  0.7968  0.2611  0.2167 -0.2853 #>   0.8506 -1.5824  1.5723  0.1699  0.1608 #>   1.8180 -0.1436 -3.5947  4.8995  1.5083 #>  #> (2,.,.) =  #>  -0.8802 -1.0578 -0.0391 -1.0979 -0.2634 #>  -1.3431 -1.8385  4.6939 -4.6969 -0.5755 #>  -1.2714 -2.5916  1.3334 -2.2095  0.4801 #>  #> (3,.,.) =  #>  -0.9593  5.0288 -3.3491  0.1169 -2.4787 #>  -0.4831 -0.2680 -0.7904 -0.1181 -0.0924 #>   1.4521 -0.2914 -1.1571  2.7915  0.9958 #>  #> (4,.,.) =  #>   0.7540 -2.6506  3.9934 -1.1908  1.1497 #>   1.0666  0.2036 -2.0001  2.8705  1.1918 #>  -0.9074 -1.2377 -0.1101 -0.8432  0.7071 #>  #> (5,.,.) =  #>   1.1063  0.2889  2.1356 -0.1968 -0.9114 #>  -0.5778  2.4138 -3.6613  1.2819 -0.8573 #>   0.4689 -1.2846 -0.6056  1.1021  0.3662 #>  #> (6,.,.) =  #>   0.8615  0.0674  1.1361  0.2918 -0.2679 #>  -0.4541 -0.7477 -2.1147  1.1032  1.2781 #>  -1.2123  3.6691  2.0633 -3.5226 -2.3677 #>  #> ... [the output was truncated (use n=-1 to disable)] #> [ CPUFloatType{10,3,5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_exp — torch_matrix_exp","title":"Matrix_exp — torch_matrix_exp","text":"Matrix_exp","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_exp — torch_matrix_exp","text":"","code":"torch_matrix_exp(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_exp — torch_matrix_exp","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"matrix-power-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_power(input) -> Tensor","title":"Matrix_exp — torch_matrix_exp","text":"Returns matrix exponential. Supports batched input. matrix , matrix exponential defined $$     \\exp^= \\sum_{k=0}^\\infty ^k / k!. $$ implementation based : Bader, P.; Blanes, S.; Casas, F. Computing Matrix Exponential Optimized Taylor Polynomial Approximation. Mathematics 2019, 7, 1174.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_exp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_exp — torch_matrix_exp","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(2, 2, 2)) a[1, , ] <- torch_eye(2, 2) a[2, , ] <- 2 * torch_eye(2, 2) a torch_matrix_exp(a)  x <- torch_tensor(rbind(c(0, pi/3), c(-pi/3, 0))) x$matrix_exp() # should be [[cos(pi/3), sin(pi/3)], [-sin(pi/3), cos(pi/3)]] } #> torch_tensor #>  0.5000  0.8660 #> -0.8660  0.5000 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_power — torch_matrix_power","title":"Matrix_power — torch_matrix_power","text":"Matrix_power","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix_power — torch_matrix_power","text":"","code":"torch_matrix_power(self, n)"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_power — torch_matrix_power","text":"self (Tensor) input tensor. n (int) power raise matrix ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"matrix-power-input-n-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_power(input, n) -> Tensor","title":"Matrix_power — torch_matrix_power","text":"Returns matrix raised power n square matrices. batch matrices, individual matrix raised power n. n negative, inverse matrix (invertible) raised power n.  batch matrices, batched inverse (invertible) raised power n. n 0, identity matrix returned.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_power.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrix_power — torch_matrix_power","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(2, 2, 2)) a torch_matrix_power(a, 3) } #> torch_tensor #> (1,.,.) =  #>    0.2047   0.6763 #>    4.3261  17.6104 #>  #> (2,.,.) =  #>   1.8462 -1.1701 #>   1.4960  2.3349 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix_rank — torch_matrix_rank","title":"Matrix_rank — torch_matrix_rank","text":"Matrix_rank","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix_rank — torch_matrix_rank","text":"self (Tensor) input 2-D tensor tol (float, optional) tolerance value. Default: NULL symmetric (bool, optional) indicates whether input symmetric.                               Default: FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_matrix_rank.html","id":"matrix-rank-input-tol-null-symmetric-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"matrix_rank(input, tol=NULL, symmetric=False) -> Tensor","title":"Matrix_rank — torch_matrix_rank","text":"Returns numerical rank 2-D tensor. method compute matrix rank done using SVD default. symmetric TRUE, input assumed symmetric, computation rank done obtaining eigenvalues. tol threshold singular values (eigenvalues symmetric TRUE) considered 0. tol specified, tol set S.max() * max(S.size()) * eps S singular values (eigenvalues symmetric TRUE), eps epsilon value datatype input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":null,"dir":"Reference","previous_headings":"","what":"Max — torch_max","title":"Max — torch_max","text":"Max","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Max — torch_max","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . Default: FALSE. (tuple, optional) result tuple two output tensors (max, max_indices) (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Max — torch_max","text":"shapes match, shape returned output tensor follows broadcasting rules .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"max(input) -> Tensor","title":"Max — torch_max","text":"Returns maximum value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"max(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Max — torch_max","text":"Returns namedtuple (values, indices) values maximum value row input tensor given dimension dim. indices index location maximum value found (argmax).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Max — torch_max","text":"indices necessarily contain first occurrence maximal value found, unless unique. exact implementation details device-specific. expect result run CPU GPU general. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"max-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"max(input, other, out=NULL) -> Tensor","title":"Max — torch_max","text":"element tensor input compared corresponding element tensor element-wise maximum taken. shapes input need match, must broadcastable . $$     \\mbox{}_i = \\max(\\mbox{tensor}_i, \\mbox{}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_max.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Max — torch_max","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_max(a)   a = torch_randn(c(4, 4)) a torch_max(a, dim = 1)   a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_max(a, other = b) } #> torch_tensor #>  0.3014 #>  1.4046 #>  1.2867 #>  0.0281 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":null,"dir":"Reference","previous_headings":"","what":"Maximum — torch_maximum","title":"Maximum — torch_maximum","text":"Maximum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Maximum — torch_maximum","text":"","code":"torch_maximum(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Maximum — torch_maximum","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Maximum — torch_maximum","text":"one elements compared NaN, element returned. torch_maximum() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"maximum-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"maximum(input, other, *, out=None) -> Tensor","title":"Maximum — torch_maximum","text":"Computes element-wise maximum input .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_maximum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Maximum — torch_maximum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, -1)) b <- torch_tensor(c(3, 0, 4)) torch_maximum(a, b) } #> torch_tensor #>  3 #>  2 #>  4 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean — torch_mean","title":"Mean — torch_mean","text":"Mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean — torch_mean","text":"","code":"torch_mean(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean — torch_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype resulting data type.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"mean-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"mean(input) -> Tensor","title":"Mean — torch_mean","text":"Returns mean value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"mean-input-dim-keepdim-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mean(input, dim, keepdim=False, out=NULL) -> Tensor","title":"Mean — torch_mean","text":"Returns mean value row input tensor given dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean — torch_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_mean(a)   a = torch_randn(c(4, 4)) a torch_mean(a, 1) torch_mean(a, 1, TRUE) } #> torch_tensor #> -0.4723 -0.2494 -0.0274  0.3934 #> [ CPUFloatType{1,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":null,"dir":"Reference","previous_headings":"","what":"Median — torch_median","title":"Median — torch_median","text":"Median","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Median — torch_median","text":"","code":"torch_median(self, dim, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Median — torch_median","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"median-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"median(input) -> Tensor","title":"Median — torch_median","text":"Returns median value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"median(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Median — torch_median","text":"Returns namedtuple (values, indices) values median value row input tensor given dimension dim. indices index location median value found. default, dim last dimension input tensor. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting outputs tensor 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_median.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Median — torch_median","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_median(a)   a = torch_randn(c(4, 5)) a torch_median(a, 1) } #> [[1]] #> torch_tensor #> -0.5959 #> -0.2752 #> -0.2743 #> -0.4593 #> -0.1138 #> [ CPUFloatType{5} ] #>  #> [[2]] #> torch_tensor #>  0 #>  3 #>  2 #>  0 #>  0 #> [ CPULongType{5} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_memory_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Memory format — torch_memory_format","title":"Memory format — torch_memory_format","text":"Returns correspondent memory format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_memory_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Memory format — torch_memory_format","text":"","code":"torch_contiguous_format()  torch_preserve_format()  torch_channels_last_format()"},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":null,"dir":"Reference","previous_headings":"","what":"Meshgrid — torch_meshgrid","title":"Meshgrid — torch_meshgrid","text":"Take \\(N\\) tensors, can either scalar 1-dimensional vector, create \\(N\\) N-dimensional grids, \\(\\) th grid defined expanding \\(\\) th input dimensions defined inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meshgrid — torch_meshgrid","text":"","code":"torch_meshgrid(tensors, indexing)"},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meshgrid — torch_meshgrid","text":"tensors (list Tensor) list scalars 1 dimensional tensors. Scalars treated (1,). indexing (str, optional): indexing mode, either “xy” “ij”, defaults “ij”. See warning future changes. “xy” selected, first dimension corresponds cardinality second input second dimension corresponds cardinality first input. “ij” selected, dimensions order cardinality inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Meshgrid — torch_meshgrid","text":"future torch_meshgrid transition indexing=’xy’ default. issue tracks issue goal migrating NumPy’s behavior.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_meshgrid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Meshgrid — torch_meshgrid","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3)) y = torch_tensor(c(4, 5, 6)) out = torch_meshgrid(list(x, y)) out } #> [[1]] #> torch_tensor #>  1  1  1 #>  2  2  2 #>  3  3  3 #> [ CPUFloatType{3,3} ] #>  #> [[2]] #> torch_tensor #>  4  5  6 #>  4  5  6 #>  4  5  6 #> [ CPUFloatType{3,3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Min — torch_min","title":"Min — torch_min","text":"Min","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Min — torch_min","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . (tuple, optional) tuple two output tensors (min, min_indices) (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Min — torch_min","text":"shapes match, shape returned output tensor follows broadcasting rules .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"min(input) -> Tensor","title":"Min — torch_min","text":"Returns minimum value elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"min(input, dim, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Min — torch_min","text":"Returns namedtuple (values, indices) values minimum value row input tensor given dimension dim. indices index location minimum value found (argmin).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Min — torch_min","text":"indices necessarily contain first occurrence minimal value found, unless unique. exact implementation details device-specific. expect result run CPU GPU general. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"min-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"min(input, other, out=NULL) -> Tensor","title":"Min — torch_min","text":"element tensor input compared corresponding element tensor element-wise minimum taken. resulting tensor returned. shapes input need match, must broadcastable . $$     \\mbox{}_i = \\min(\\mbox{tensor}_i, \\mbox{}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_min.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Min — torch_min","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_min(a)   a = torch_randn(c(4, 4)) a torch_min(a, dim = 1)   a = torch_randn(c(4)) a b = torch_randn(c(4)) b torch_min(a, other = b) } #> torch_tensor #> -0.5141 #> -2.3883 #> -2.1665 #> -0.3134 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum — torch_minimum","title":"Minimum — torch_minimum","text":"Minimum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum — torch_minimum","text":"","code":"torch_minimum(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum — torch_minimum","text":"self (Tensor) input tensor. (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Minimum — torch_minimum","text":"one elements compared NaN, element returned. torch_minimum() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"minimum-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"minimum(input, other, *, out=None) -> Tensor","title":"Minimum — torch_minimum","text":"Computes element-wise minimum input .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_minimum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum — torch_minimum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, -1)) b <- torch_tensor(c(3, 0, 4)) torch_minimum(a, b) } #> torch_tensor #>  1 #>  0 #> -1 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":null,"dir":"Reference","previous_headings":"","what":"Mm — torch_mm","title":"Mm — torch_mm","text":"Mm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mm — torch_mm","text":"","code":"torch_mm(self, mat2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mm — torch_mm","text":"self (Tensor) first matrix multiplied mat2 (Tensor) second matrix multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mm — torch_mm","text":"function broadcast . broadcasting matrix products, see torch_matmul.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"mm-input-mat-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mm(input, mat2, out=NULL) -> Tensor","title":"Mm — torch_mm","text":"Performs matrix multiplication matrices input mat2. input \\((n \\times m)\\) tensor, mat2 \\((m \\times p)\\) tensor, \\((n \\times p)\\) tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mm — torch_mm","text":"","code":"if (torch_is_installed()) {  mat1 = torch_randn(c(2, 3)) mat2 = torch_randn(c(3, 3)) torch_mm(mat1, mat2) } #> torch_tensor #> -0.2545 -0.2052 -1.7691 #>  0.3064  1.2126 -2.8038 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":null,"dir":"Reference","previous_headings":"","what":"Mode — torch_mode","title":"Mode — torch_mode","text":"Mode","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mode — torch_mode","text":"","code":"torch_mode(self, dim = -1L, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mode — torch_mode","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mode — torch_mode","text":"function defined torch_cuda.Tensor yet.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"mode(input, dim=-1, keepdim=False, out=NULL) -> (Tensor, LongTensor)","title":"Mode — torch_mode","text":"Returns namedtuple (values, indices) values mode value row input tensor given dimension dim, .e. value appears often row, indices index location mode value found. default, dim last dimension input tensor. keepdim TRUE, output tensors size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensors 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mode — torch_mode","text":"","code":"if (torch_is_installed()) {  a = torch_randint(0, 50, size = list(5)) a torch_mode(a, 1) } #> [[1]] #> torch_tensor #> 2 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 2 #> [ CPULongType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":null,"dir":"Reference","previous_headings":"","what":"Movedim — torch_movedim","title":"Movedim — torch_movedim","text":"Movedim","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Movedim — torch_movedim","text":"","code":"torch_movedim(self, source, destination)"},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Movedim — torch_movedim","text":"self (Tensor) input tensor. source (int tuple ints) Original positions dims move. must unique. destination (int tuple ints) Destination positions original dims. must also unique.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"movedim-input-source-destination-gt-tensor-","dir":"Reference","previous_headings":"","what":"movedim(input, source, destination) -> Tensor","title":"Movedim — torch_movedim","text":"Moves dimension(s) input position(s) source position(s) destination. dimensions input explicitly moved remain original order appear positions specified destination.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_movedim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Movedim — torch_movedim","text":"","code":"if (torch_is_installed()) {  t <- torch_randn(c(3,2,1)) t torch_movedim(t, 2, 1)$shape torch_movedim(t, 2, 1) torch_movedim(t, c(2, 3), c(1, 2))$shape torch_movedim(t, c(2, 3), c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>  -1.4617  0.8193  0.2749 #>  #> (2,.,.) =  #>   0.2758  1.4123  0.2071 #> [ CPUFloatType{2,1,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":null,"dir":"Reference","previous_headings":"","what":"Mul — torch_mul","title":"Mul — torch_mul","text":"Mul","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mul — torch_mul","text":"","code":"torch_mul(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mul — torch_mul","text":"self (Tensor) first multiplicand tensor (Tensor) second multiplicand tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"mul-input-other-out-null-","dir":"Reference","previous_headings":"","what":"mul(input, other, out=NULL)","title":"Mul — torch_mul","text":"Multiplies element input input scalar returns new resulting tensor. $$     \\mbox{}_i = \\mbox{} \\times \\mbox{input}_i $$ input type FloatTensor DoubleTensor, real number, otherwise integer element tensor input multiplied corresponding element Tensor . resulting tensor returned. shapes input must broadcastable . $$     \\mbox{}_i = \\mbox{input}_i \\times \\mbox{}_i $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mul.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mul — torch_mul","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3)) a torch_mul(a, 100)   a = torch_randn(c(4, 1)) a b = torch_randn(c(1, 4)) b torch_mul(a, b) } #> torch_tensor #>  1.8229e+00  6.1573e-01 -3.7671e-01  3.9122e-03 #>  8.1764e-01  2.7618e-01 -1.6897e-01  1.7548e-03 #>  9.2321e+00  3.1184e+00 -1.9079e+00  1.9814e-02 #> -1.1543e-02 -3.8989e-03  2.3854e-03 -2.4772e-05 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Multinomial — torch_multinomial","title":"Multinomial — torch_multinomial","text":"Multinomial","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multinomial — torch_multinomial","text":"","code":"torch_multinomial(self, num_samples, replacement = FALSE, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multinomial — torch_multinomial","text":"self (Tensor) input tensor containing probabilities num_samples (int) number samples draw replacement (bool, optional) whether draw replacement generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multinomial — torch_multinomial","text":"Indices ordered left right according sampled (first samples placed first column). input vector, vector size num_samples. input matrix m rows, matrix shape \\((m \\times \\mbox{num\\_samples})\\). replacement TRUE, samples drawn replacement. , drawn without replacement, means sample index drawn row, drawn row.","code":"The rows of `input` do not need to sum to one (in which case we use the values as weights), but must be non-negative, finite and have a non-zero sum. When drawn without replacement, `num_samples` must be lower than number of non-zero elements in `input` (or the min number of non-zero elements in each row of `input` if it is a matrix)."},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-","dir":"Reference","previous_headings":"","what":"multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -> LongTensor","title":"Multinomial — torch_multinomial","text":"Returns tensor row contains num_samples indices sampled multinomial probability distribution located corresponding row tensor input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multinomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multinomial — torch_multinomial","text":"","code":"if (torch_is_installed()) {  weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights torch_multinomial(weights, 2) torch_multinomial(weights, 4, replacement=TRUE) } #> torch_tensor #>  2 #>  2 #>  2 #>  2 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply — torch_multiply","title":"Multiply — torch_multiply","text":"Multiply","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply — torch_multiply","text":"","code":"torch_multiply(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply — torch_multiply","text":"self (Tensor) first multiplicand tensor (Tensor) second multiplicand tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_multiply.html","id":"multiply-input-other-out-none-","dir":"Reference","previous_headings":"","what":"multiply(input, other, *, out=None)","title":"Multiply — torch_multiply","text":"Alias torch_mul().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":null,"dir":"Reference","previous_headings":"","what":"Mv — torch_mv","title":"Mv — torch_mv","text":"Mv","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mv — torch_mv","text":"","code":"torch_mv(self, vec)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mv — torch_mv","text":"self (Tensor) matrix multiplied vec (Tensor) vector multiplied","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mv — torch_mv","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"mv-input-vec-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"mv(input, vec, out=NULL) -> Tensor","title":"Mv — torch_mv","text":"Performs matrix-vector product matrix input vector vec. input \\((n \\times m)\\) tensor, vec 1-D tensor size \\(m\\), 1-D size \\(n\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mv — torch_mv","text":"","code":"if (torch_is_installed()) {  mat = torch_randn(c(2, 3)) vec = torch_randn(c(3)) torch_mv(mat, vec) } #> torch_tensor #> -0.1862 #> -0.2170 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Mvlgamma — torch_mvlgamma","title":"Mvlgamma — torch_mvlgamma","text":"Mvlgamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mvlgamma — torch_mvlgamma","text":"","code":"torch_mvlgamma(self, p)"},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mvlgamma — torch_mvlgamma","text":"self (Tensor) tensor compute multivariate log-gamma function p (int) number dimensions","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"mvlgamma-input-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"mvlgamma(input, p) -> Tensor","title":"Mvlgamma — torch_mvlgamma","text":"Computes multivariate log-gamma function <https://en.wikipedia.org/wiki/Multivariate_gamma_function>_) dimension \\(p\\) element-wise, given $$     \\log(\\Gamma_{p}()) = C + \\displaystyle \\sum_{=1}^{p} \\log\\left(\\Gamma\\left(- \\frac{- 1}{2}\\right)\\right) $$ \\(C = \\log(\\pi) \\times \\frac{p (p - 1)}{4}\\) \\(\\Gamma(\\cdot)\\) Gamma function. elements must greater \\(\\frac{p - 1}{2}\\), otherwise error thrown.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_mvlgamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mvlgamma — torch_mvlgamma","text":"","code":"if (torch_is_installed()) {  a = torch_empty(c(2, 3))$uniform_(1, 2) a torch_mvlgamma(a, 2) } #> torch_tensor #>  0.7204  0.4106  0.3954 #>  0.4121  0.6110  0.3928 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Nanquantile — torch_nanquantile","title":"Nanquantile — torch_nanquantile","text":"Nanquantile","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nanquantile — torch_nanquantile","text":"","code":"torch_nanquantile(   self,   q,   dim = NULL,   keepdim = FALSE,   interpolation = \"linear\" )"},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nanquantile — torch_nanquantile","text":"self (Tensor) input tensor. q (float Tensor) scalar 1D tensor quantile values range [0, 1] dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . interpolation interpolation method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"nanquantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nanquantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor","title":"Nanquantile — torch_nanquantile","text":"variant torch_quantile() \"ignores\" NaN values, computing quantiles q NaN values input exist. values reduced row NaN quantiles reduction NaN. See documentation torch_quantile().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nanquantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nanquantile — torch_nanquantile","text":"","code":"if (torch_is_installed()) {  t <- torch_tensor(c(NaN, 1, 2)) t$quantile(0.5) t$nanquantile(0.5) t <- torch_tensor(rbind(c(NaN, NaN), c(1, 2))) t t$nanquantile(0.5, dim=1) t$nanquantile(0.5, dim=2) torch_nanquantile(t, 0.5, dim = 1) torch_nanquantile(t, 0.5, dim = 2) } #> torch_tensor #>     nan  1.5000 #> [ CPUFloatType{1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":null,"dir":"Reference","previous_headings":"","what":"Nansum — torch_nansum","title":"Nansum — torch_nansum","text":"Nansum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nansum — torch_nansum","text":"","code":"torch_nansum(self, dim = NULL, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nansum — torch_nansum","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype desired data type returned tensor. specified, input tensor casted dtype operation performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"nansum-input-dtype-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nansum(input, *, dtype=None) -> Tensor","title":"Nansum — torch_nansum","text":"Returns sum elements, treating Numbers (NaNs) zero.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"nansum-input-dim-keepdim-false-dtype-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nansum(input, dim, keepdim=FALSE, *, dtype=None) -> Tensor","title":"Nansum — torch_nansum","text":"Returns sum row input tensor given dimension dim, treating Numbers (NaNs) zero. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nansum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nansum — torch_nansum","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1., 2., NaN, 4.)) torch_nansum(a)   torch_nansum(torch_tensor(c(1., NaN))) a <- torch_tensor(rbind(c(1, 2), c(3., NaN))) torch_nansum(a) torch_nansum(a, dim=1) torch_nansum(a, dim=2) } #> torch_tensor #>  3 #>  3 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":null,"dir":"Reference","previous_headings":"","what":"Narrow — torch_narrow","title":"Narrow — torch_narrow","text":"Narrow","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Narrow — torch_narrow","text":"","code":"torch_narrow(self, dim, start, length)"},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Narrow — torch_narrow","text":"self (Tensor) tensor narrow dim (int) dimension along narrow start (int) starting dimension length (int) distance ending dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"narrow-input-dim-start-length-gt-tensor-","dir":"Reference","previous_headings":"","what":"narrow(input, dim, start, length) -> Tensor","title":"Narrow — torch_narrow","text":"Returns new tensor narrowed version input tensor. dimension dim input start start + length. returned tensor input tensor share underlying storage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_narrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Narrow — torch_narrow","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE)) torch_narrow(x, 1, 1, 2) torch_narrow(x, 2, 2, 2) } #> torch_tensor #>  2  3 #>  5  6 #>  8  9 #> [ CPULongType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":null,"dir":"Reference","previous_headings":"","what":"Ne — torch_ne","title":"Ne — torch_ne","text":"Ne","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ne — torch_ne","text":"","code":"torch_ne(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ne — torch_ne","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ne-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"ne(input, other, out=NULL) -> Tensor","title":"Ne — torch_ne","text":"Computes \\(input \\neq \\) element-wise. second argument can number tensor whose shape broadcastable  first argument.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ne.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ne — torch_ne","text":"","code":"if (torch_is_installed()) {  torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),           torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE))) } #> torch_tensor #>  0  1 #>  1  0 #> [ CPUBoolType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":null,"dir":"Reference","previous_headings":"","what":"Neg — torch_neg","title":"Neg — torch_neg","text":"Neg","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Neg — torch_neg","text":"","code":"torch_neg(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Neg — torch_neg","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"neg-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"neg(input, out=NULL) -> Tensor","title":"Neg — torch_neg","text":"Returns new tensor negative elements input. $$     \\mbox{} = -1 \\times \\mbox{input} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_neg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Neg — torch_neg","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5)) a torch_neg(a) } #> torch_tensor #>  0.9773 #>  1.1485 #>  0.3309 #>  0.7101 #>  1.6614 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Negative — torch_negative","title":"Negative — torch_negative","text":"Negative","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Negative — torch_negative","text":"","code":"torch_negative(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Negative — torch_negative","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_negative.html","id":"negative-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"negative(input, *, out=None) -> Tensor","title":"Negative — torch_negative","text":"Alias torch_neg()","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":null,"dir":"Reference","previous_headings":"","what":"Nextafter — torch_nextafter","title":"Nextafter — torch_nextafter","text":"Nextafter","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nextafter — torch_nextafter","text":"","code":"torch_nextafter(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nextafter — torch_nextafter","text":"self (Tensor) first input tensor (Tensor) second input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"nextafter-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"nextafter(input, other, *, out=None) -> Tensor","title":"Nextafter — torch_nextafter","text":"Return next floating-point value input towards , elementwise. shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nextafter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nextafter — torch_nextafter","text":"","code":"if (torch_is_installed()) {  eps <- torch_finfo(torch_float32())$eps torch_nextafter(torch_tensor(c(1, 2)), torch_tensor(c(2, 1))) == torch_tensor(c(eps + 1, 2 - eps)) } #> torch_tensor #>  1 #>  1 #> [ CPUBoolType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":null,"dir":"Reference","previous_headings":"","what":"Nonzero — torch_nonzero","title":"Nonzero — torch_nonzero","text":"Nonzero elements tensors.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nonzero — torch_nonzero","text":"","code":"torch_nonzero(self, as_list = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nonzero — torch_nonzero","text":"self (Tensor) input tensor. as_list FALSE, output tensor containing indices. TRUE, one 1-D tensor dimension, containing indices nonzero element along dimension. as_list FALSE (default): Returns tensor containing indices non-zero elements input.  row result contains indices non-zero element input. result sorted lexicographically, last index changing fastest (C-style). input \\(n\\) dimensions, resulting indices tensor size \\((z \\times n)\\), \\(z\\) total number non-zero elements input tensor. as_list TRUE: Returns tuple 1-D tensors, one dimension input, containing indices (dimension) non-zero elements input . input \\(n\\) dimensions, resulting tuple contains \\(n\\) tensors size \\(z\\), \\(z\\) total number non-zero elements input tensor. special case, input zero dimensions nonzero scalar value, treated one-dimensional tensor one element.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_nonzero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nonzero — torch_nonzero","text":"","code":"if (torch_is_installed()) {  torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1))) } #> torch_tensor #>  1 #>  2 #>  3 #>  5 #> [ CPULongType{4,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Norm — torch_norm","title":"Norm — torch_norm","text":"Norm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Norm — torch_norm","text":"","code":"torch_norm(self, p = 2L, dim, keepdim = FALSE, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Norm — torch_norm","text":"self (Tensor) input tensor p (int, float, inf, -inf, 'fro', 'nuc', optional) order norm. Default: 'fro'        following norms can calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                –        'nuc'  nuclear norm                  –         vec norm dim NULL  sum(abs(x)ord)(1./ord)        =====  ============================  ========================== dim (int, 2-tuple ints, 2-list ints, optional) int,        vector norm calculated, 2-tuple ints, matrix norm        calculated. value NULL, matrix norm calculated        input tensor two dimensions, vector norm        calculated input tensor one dimension. input        tensor two dimensions, vector norm applied        last dimension. keepdim (bool, optional) whether output tensors dim        retained . Ignored dim = NULL        = NULL. Default: FALSE Ignored        dim = NULL = NULL. dtype (torch.dtype, optional) desired data type        returned tensor. specified, input tensor casted        'dtype' performing operation. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Norm — torch_norm","text":"Returns matrix norm vector norm given tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Norm — torch_norm","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(1, 9, dtype = torch_float()) b <- a$reshape(list(3, 3)) torch_norm(a) torch_norm(b) torch_norm(a, Inf) torch_norm(b, Inf)  } #> torch_tensor #> 9 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Normal — torch_normal","title":"Normal — torch_normal","text":"Normal Normal distributed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normal — torch_normal","text":"","code":"torch_normal(mean, std, size = NULL, generator = NULL, ...)"},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normal — torch_normal","text":"mean (tensor scalar double) Mean normal distribution. torch_tensor() output dim mean represents per-element mean. scalar value, reused elements. std (tensor scalar double) standard deviation normal distribution. torch_tensor() output size std represents per-element standard deviation. scalar value, reused elements. size (integers, optional) used mean std scalars. generator random number generator created torch_generator(). NULL default generator used. ... Tensor option parameters like dtype, layout, device. Can used mean std scalar numerics.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Normal — torch_normal","text":"shapes match, shape mean used shape returned output tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor-","dir":"Reference","previous_headings":"","what":"normal(mean, std, *) -> Tensor","title":"Normal — torch_normal","text":"Returns tensor random numbers drawn separate normal distributions whose mean standard deviation given. mean tensor mean output element's normal distribution std tensor standard deviation output element's normal distribution shapes mean std need match, total number elements tensor need .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor--1","dir":"Reference","previous_headings":"","what":"normal(mean=0.0, std) -> Tensor","title":"Normal — torch_normal","text":"Similar function , means shared among drawn elements.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-gt-tensor--2","dir":"Reference","previous_headings":"","what":"normal(mean, std=1.0) -> Tensor","title":"Normal — torch_normal","text":"Similar function , standard-deviations shared among drawn elements.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"normal-mean-std-size-gt-tensor-","dir":"Reference","previous_headings":"","what":"normal(mean, std, size, *) -> Tensor","title":"Normal — torch_normal","text":"Similar function , means standard deviations shared among drawn elements. resulting tensor size given size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_normal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normal — torch_normal","text":"","code":"if (torch_is_installed()) {  torch_normal(mean=0, std=torch_arange(1, 0, -0.1) + 1e-6) torch_normal(mean=0.5, std=torch_arange(1., 6.)) torch_normal(mean=torch_arange(1., 6.)) torch_normal(2, 3, size=c(1, 4))  } #> torch_tensor #>  2.2658 -0.3548  1.2976  1.0859 #> [ CPUFloatType{1,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":null,"dir":"Reference","previous_headings":"","what":"Not_equal — torch_not_equal","title":"Not_equal — torch_not_equal","text":"Not_equal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Not_equal — torch_not_equal","text":"","code":"torch_not_equal(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Not_equal — torch_not_equal","text":"self (Tensor) tensor compare (Tensor float) tensor value compare","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_not_equal.html","id":"not-equal-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"not_equal(input, other, *, out=None) -> Tensor","title":"Not_equal — torch_not_equal","text":"Alias torch_ne().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones — torch_ones","title":"Ones — torch_ones","text":"Ones","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones — torch_ones","text":"","code":"torch_ones(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones — torch_ones","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Ones — torch_ones","text":"Returns tensor filled scalar value 1, shape defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones — torch_ones","text":"","code":"if (torch_is_installed()) {  torch_ones(c(2, 3)) torch_ones(c(5)) } #> torch_tensor #>  1 #>  1 #>  1 #>  1 #>  1 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Ones_like — torch_ones_like","title":"Ones_like — torch_ones_like","text":"Ones_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ones_like — torch_ones_like","text":"","code":"torch_ones_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ones_like — torch_ones_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Ones_like — torch_ones_like","text":"Returns tensor filled scalar value 1, size input. torch_ones_like(input) equivalent torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Ones_like — torch_ones_like","text":"0.4, function support keyword. alternative, old torch_ones_like(input, =output) equivalent torch_ones(input.size(), =output).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ones_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ones_like — torch_ones_like","text":"","code":"if (torch_is_installed()) {  input = torch_empty(c(2, 3)) torch_ones_like(input) } #> torch_tensor #>  1  1  1 #>  1  1  1 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":null,"dir":"Reference","previous_headings":"","what":"Orgqr — torch_orgqr","title":"Orgqr — torch_orgqr","text":"Orgqr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orgqr — torch_orgqr","text":"","code":"torch_orgqr(self, input2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orgqr — torch_orgqr","text":"self (Tensor) torch_geqrf. input2 (Tensor) tau torch_geqrf.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_orgqr.html","id":"orgqr-input-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"orgqr(input, input2) -> Tensor","title":"Orgqr — torch_orgqr","text":"Computes orthogonal matrix Q QR factorization, (input, input2) tuple returned torch_geqrf. directly calls underlying LAPACK function ?orgqr. See LAPACK documentation orgqr_ details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":null,"dir":"Reference","previous_headings":"","what":"Ormqr — torch_ormqr","title":"Ormqr — torch_ormqr","text":"Ormqr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ormqr — torch_ormqr","text":"","code":"torch_ormqr(self, input2, input3, left = TRUE, transpose = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ormqr — torch_ormqr","text":"self (Tensor) torch_geqrf. input2 (Tensor) tau torch_geqrf. input3 (Tensor) matrix multiplied. left see LAPACK documentation transpose see LAPACK documentation","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_ormqr.html","id":"ormqr-input-input-input-left-true-transpose-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"ormqr(input, input2, input3, left=TRUE, transpose=False) -> Tensor","title":"Ormqr — torch_ormqr","text":"Multiplies mat (given input3) orthogonal Q matrix QR factorization formed torch_geqrf() represented (, tau) (given (input, input2)). directly calls underlying LAPACK function ?ormqr.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":null,"dir":"Reference","previous_headings":"","what":"Outer — torch_outer","title":"Outer — torch_outer","text":"Outer","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outer — torch_outer","text":"","code":"torch_outer(self, vec2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outer — torch_outer","text":"self (Tensor) 1-D input vector vec2 (Tensor) 1-D input vector","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outer — torch_outer","text":"function broadcast.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"outer-input-vec-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"outer(input, vec2, *, out=None) -> Tensor","title":"Outer — torch_outer","text":"Outer product input vec2. input vector size \\(n\\) vec2 vector size \\(m\\), must matrix size \\((n \\times m)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_outer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outer — torch_outer","text":"","code":"if (torch_is_installed()) {  v1 <- torch_arange(1., 5.) v2 <- torch_arange(1., 4.) torch_outer(v1, v2) } #> torch_tensor #>   1   2   3   4 #>   2   4   6   8 #>   3   6   9  12 #>   4   8  12  16 #>   5  10  15  20 #> [ CPUFloatType{5,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":null,"dir":"Reference","previous_headings":"","what":"Pdist — torch_pdist","title":"Pdist — torch_pdist","text":"Pdist","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pdist — torch_pdist","text":"","code":"torch_pdist(self, p = 2L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pdist — torch_pdist","text":"self NA input tensor shape \\(N \\times M\\). p NA p value p-norm distance calculate vector pair        \\(\\[0, \\infty]\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pdist.html","id":"pdist-input-p-gt-tensor-","dir":"Reference","previous_headings":"","what":"pdist(input, p=2) -> Tensor","title":"Pdist — torch_pdist","text":"Computes p-norm distance every pair row vectors input. identical upper triangular portion, excluding diagonal, torch_norm(input[:, NULL] - input, dim=2, p=p). function faster rows contiguous. input shape \\(N \\times M\\) output shape \\(\\frac{1}{2} N (N - 1)\\). function equivalent scipy.spatial.distance.pdist(input, 'minkowski', p=p) \\(p \\(0, \\infty)\\). \\(p = 0\\) equivalent scipy.spatial.distance.pdist(input, 'hamming') * M. \\(p = \\infty\\), closest scipy function scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max()).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Pinverse — torch_pinverse","title":"Pinverse — torch_pinverse","text":"Pinverse","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pinverse — torch_pinverse","text":"","code":"torch_pinverse(self, rcond = 1e-15)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pinverse — torch_pinverse","text":"self (Tensor) input tensor size \\((*, m, n)\\) \\(*\\) zero batch dimensions rcond (float) floating point value determine cutoff small singular values.                   Default: 1e-15","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Pinverse — torch_pinverse","text":"","code":"This method is implemented using the Singular Value Decomposition. The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_. Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_. However, this method is backprop-able due to the implementation by using SVD results, and could be unstable. Double-backward will also be unstable due to the usage of SVD internally. See `~torch.svd` for more details."},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"pinverse-input-rcond-e-gt-tensor-","dir":"Reference","previous_headings":"","what":"pinverse(input, rcond=1e-15) -> Tensor","title":"Pinverse — torch_pinverse","text":"Calculates pseudo-inverse (also known Moore-Penrose inverse) 2D tensor. Please look Moore-Penrose inverse_ details","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pinverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pinverse — torch_pinverse","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(3, 5)) input torch_pinverse(input) # Batched pinverse example a = torch_randn(c(2,6,3)) b = torch_pinverse(a) torch_matmul(b, a) } #> torch_tensor #> (1,.,.) =  #>   1.0000e+00 -8.9407e-08 -2.9802e-08 #>  -4.4703e-08  1.0000e+00 -1.3039e-07 #>  -7.4506e-09 -2.2352e-08  1.0000e+00 #>  #> (2,.,.) =  #>   1.0000e+00  4.8429e-08 -5.9605e-08 #>  -5.9605e-08  1.0000e+00  2.3097e-07 #>   3.8743e-07  5.7742e-08  1.0000e+00 #> [ CPUFloatType{2,3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":null,"dir":"Reference","previous_headings":"","what":"Pixel_shuffle — torch_pixel_shuffle","title":"Pixel_shuffle — torch_pixel_shuffle","text":"Pixel_shuffle","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pixel_shuffle — torch_pixel_shuffle","text":"","code":"torch_pixel_shuffle(self, upscale_factor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pixel_shuffle — torch_pixel_shuffle","text":"self (Tensor) input tensor upscale_factor (int) factor increase spatial resolution ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"rearranges-elements-in-a-tensor-of-shape-","dir":"Reference","previous_headings":"","what":"Rearranges elements in a tensor of shape","title":"Pixel_shuffle — torch_pixel_shuffle","text":"math:(*, C \\times r^2, H, W) : Rearranges elements tensor shape \\((*, C \\times r^2, H, W)\\) tensor shape \\((*, C, H \\times r, W \\times r)\\). See ~torch.nn.PixelShuffle details.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pixel_shuffle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pixel_shuffle — torch_pixel_shuffle","text":"","code":"if (torch_is_installed()) {  input = torch_randn(c(1, 9, 4, 4)) output = nnf_pixel_shuffle(input, 3) print(output$size()) } #> [1]  1  1 12 12"},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":null,"dir":"Reference","previous_headings":"","what":"Poisson — torch_poisson","title":"Poisson — torch_poisson","text":"Poisson","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Poisson — torch_poisson","text":"","code":"torch_poisson(self, generator = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Poisson — torch_poisson","text":"self (Tensor) input tensor containing rates Poisson distribution generator (torch.Generator, optional) pseudorandom number generator sampling","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"poisson-input-generator-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"poisson(input *, generator=NULL) -> Tensor","title":"Poisson — torch_poisson","text":"Returns tensor size input element sampled Poisson distribution rate parameter given corresponding element input .e., $$     \\mbox{}_i \\sim \\mbox{Poisson}(\\mbox{input}_i) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_poisson.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Poisson — torch_poisson","text":"","code":"if (torch_is_installed()) {  rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5 torch_poisson(rates) } #> torch_tensor #>  3  0  3  3 #>  0  1  1  0 #>  5  6  0  0 #>  0  0  1  6 #> [ CPUFloatType{4,4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":null,"dir":"Reference","previous_headings":"","what":"Polar — torch_polar","title":"Polar — torch_polar","text":"Polar","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polar — torch_polar","text":"","code":"torch_polar(abs, angle)"},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polar — torch_polar","text":"abs (Tensor) absolute value complex tensor. Must float double. angle (Tensor) angle complex tensor. Must dtype abs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"polar-abs-angle-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"polar(abs, angle, *, out=None) -> Tensor","title":"Polar — torch_polar","text":"Constructs complex tensor whose elements Cartesian coordinates corresponding polar coordinates absolute value abs angle angle. $$     \\mbox{} = \\mbox{abs} \\cdot \\cos(\\mbox{angle}) + \\mbox{abs} \\cdot \\sin(\\mbox{angle}) \\cdot j $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polar — torch_polar","text":"","code":"if (torch_is_installed()) {  abs <- torch_tensor(c(1, 2), dtype=torch_float64()) angle <- torch_tensor(c(pi / 2, 5 * pi / 4), dtype=torch_float64()) z <- torch_polar(abs, angle) z } #> torch_tensor #> ℹ Use `$real` or `$imag` to print the contents of this tensor. #> [ CPUComplexDoubleType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygamma — torch_polygamma","title":"Polygamma — torch_polygamma","text":"Polygamma","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygamma — torch_polygamma","text":"","code":"torch_polygamma(n, input)"},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polygamma — torch_polygamma","text":"n (int) order polygamma function input (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Polygamma — torch_polygamma","text":"","code":"This function is not implemented for \\eqn{n \\geq 2}."},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"polygamma-n-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"polygamma(n, input, out=NULL) -> Tensor","title":"Polygamma — torch_polygamma","text":"Computes \\(n^{th}\\) derivative digamma function input. \\(n \\geq 0\\) called order polygamma function. $$     \\psi^{(n)}(x) = \\frac{d^{(n)}}{dx^{(n)}} \\psi(x) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_polygamma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polygamma — torch_polygamma","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ a = torch_tensor(c(1, 0.5)) torch_polygamma(1, a) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":null,"dir":"Reference","previous_headings":"","what":"Pow — torch_pow","title":"Pow — torch_pow","text":"Pow","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pow — torch_pow","text":"","code":"torch_pow(self, exponent)"},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pow — torch_pow","text":"self (float) scalar base value power operation exponent (float tensor) exponent value","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"pow-input-exponent-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"pow(input, exponent, out=NULL) -> Tensor","title":"Pow — torch_pow","text":"Takes power element input exponent returns tensor result. exponent can either single float number Tensor number elements input. exponent scalar value, operation applied : $$     \\mbox{}_i = x_i^{\\mbox{exponent}} $$ exponent tensor, operation applied : $$     \\mbox{}_i = x_i^{\\mbox{exponent}_i} $$ exponent tensor, shapes input exponent must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"pow-self-exponent-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"pow(self, exponent, out=NULL) -> Tensor","title":"Pow — torch_pow","text":"self scalar float value, exponent tensor. returned tensor shape exponent operation applied : $$     \\mbox{}_i = \\mbox{self} ^ {\\mbox{exponent}_i} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_pow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pow — torch_pow","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_pow(a, 2) exp <- torch_arange(1, 5) a <- torch_arange(1, 5) a exp torch_pow(a, exp)   exp <- torch_arange(1, 5) base <- 2 torch_pow(base, exp) } #> torch_tensor #>   2 #>   4 #>   8 #>  16 #>  32 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":null,"dir":"Reference","previous_headings":"","what":"Prod — torch_prod","title":"Prod — torch_prod","text":"Prod","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prod — torch_prod","text":"","code":"torch_prod(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prod — torch_prod","text":"self (Tensor) input tensor. dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"prod-input-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"prod(input, dtype=NULL) -> Tensor","title":"Prod — torch_prod","text":"Returns product elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"prod-input-dim-keepdim-false-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"prod(input, dim, keepdim=False, dtype=NULL) -> Tensor","title":"Prod — torch_prod","text":"Returns product row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 fewer dimension input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_prod.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prod — torch_prod","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_prod(a)   a = torch_randn(c(4, 2)) a torch_prod(a, 1) } #> torch_tensor #> 0.001 * #> -1.7749 #> -91.8498 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":null,"dir":"Reference","previous_headings":"","what":"Promote_types — torch_promote_types","title":"Promote_types — torch_promote_types","text":"Promote_types","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Promote_types — torch_promote_types","text":"","code":"torch_promote_types(type1, type2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Promote_types — torch_promote_types","text":"type1 (torch.dtype) type2 (torch.dtype)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"promote-types-type-type-gt-dtype-","dir":"Reference","previous_headings":"","what":"promote_types(type1, type2) -> dtype","title":"Promote_types — torch_promote_types","text":"Returns torch_dtype smallest size scalar kind smaller lower kind either type1 type2. See type promotion documentation  information type promotion logic.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_promote_types.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Promote_types — torch_promote_types","text":"","code":"if (torch_is_installed()) {  torch_promote_types(torch_int32(), torch_float32()) torch_promote_types(torch_uint8(), torch_long()) } #> torch_Long"},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":null,"dir":"Reference","previous_headings":"","what":"Qr — torch_qr","title":"Qr — torch_qr","text":"Qr","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Qr — torch_qr","text":"","code":"torch_qr(self, some = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Qr — torch_qr","text":"self (Tensor) input tensor size \\((*, m, n)\\) * zero                batch dimensions consisting matrices dimension \\(m \\times n\\). (bool, optional) Set TRUE reduced QR decomposition FALSE                complete QR decomposition.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Qr — torch_qr","text":"precision may lost magnitudes elements input large always give valid decomposition, may give one across platforms - depend LAPACK implementation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"qr-input-some-true-out-null-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"qr(input, some=TRUE, out=NULL) -> (Tensor, Tensor)","title":"Qr — torch_qr","text":"Computes QR decomposition matrix batch matrices input, returns namedtuple (Q, R) tensors \\(\\mbox{input} = Q R\\) \\(Q\\) orthogonal matrix batch orthogonal matrices \\(R\\) upper triangular matrix batch upper triangular matrices. TRUE, function returns thin (reduced) QR factorization. Otherwise, FALSE, function returns complete QR factorization.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Qr — torch_qr","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE)) out = torch_qr(a) q = out[[1]] r = out[[2]] torch_mm(q, r)$round() torch_mm(q$t(), q)$round() } #> torch_tensor #>  1 -0  0 #> -0  1  0 #>  0  0  1 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_qscheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the corresponding Scheme object — torch_qscheme","title":"Creates the corresponding Scheme object — torch_qscheme","text":"Creates corresponding Scheme object","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_qscheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the corresponding Scheme object — torch_qscheme","text":"","code":"torch_per_channel_affine()  torch_per_tensor_affine()  torch_per_channel_symmetric()  torch_per_tensor_symmetric()"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantile — torch_quantile","title":"Quantile — torch_quantile","text":"Quantile","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantile — torch_quantile","text":"","code":"torch_quantile(self, q, dim = NULL, keepdim = FALSE, interpolation = \"linear\")"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantile — torch_quantile","text":"self (Tensor) input tensor. q (float Tensor) scalar 1D tensor quantile values range [0, 1] dim (int) dimension reduce. keepdim (bool) whether output tensor dim retained . interpolation interpolation method.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"quantile-input-q-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantile(input, q) -> Tensor","title":"Quantile — torch_quantile","text":"Returns q-th quantiles elements input tensor, linear interpolation q-th quantile lies two data points.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"quantile-input-q-dim-none-keepdim-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantile(input, q, dim=None, keepdim=FALSE, *, out=None) -> Tensor","title":"Quantile — torch_quantile","text":"Returns q-th quantiles row input tensor along dimension dim, linear interpolation q-th quantile lies two data points. default, dim None resulting input tensor flattened computation. keepdim TRUE, output dimensions size input except dimensions reduced (dim dim NULL) size 1. Otherwise, dimensions reduced squeezed (see torch_squeeze). q 1D tensor, extra dimension prepended output tensor size q represents quantiles.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantile — torch_quantile","text":"","code":"if (torch_is_installed()) {  a <- torch_randn(c(1, 3)) a q <- torch_tensor(c(0, 0.5, 1)) torch_quantile(a, q)   a <- torch_randn(c(2, 3)) a q <- torch_tensor(c(0.25, 0.5, 0.75)) torch_quantile(a, q, dim=1, keepdim=TRUE) torch_quantile(a, q, dim=1, keepdim=TRUE)$shape } #> [1] 3 1 3"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize_per_channel — torch_quantize_per_channel","title":"Quantize_per_channel — torch_quantize_per_channel","text":"Quantize_per_channel","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize_per_channel — torch_quantize_per_channel","text":"","code":"torch_quantize_per_channel(self, scales, zero_points, axis, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize_per_channel — torch_quantize_per_channel","text":"self (Tensor) float tensor quantize scales (Tensor) float 1D tensor scales use, size match input.size(axis) zero_points (int) integer 1D tensor offset use, size match input.size(axis) axis (int) dimension apply per-channel quantization dtype (torch.dtype) desired data type returned tensor.        one quantized dtypes: torch_quint8, torch.qint8, torch.qint32","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor","title":"Quantize_per_channel — torch_quantize_per_channel","text":"Converts float tensor per-channel quantized tensor given scales zero points.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_channel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantize_per_channel — torch_quantize_per_channel","text":"","code":"if (torch_is_installed()) { x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE)) torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),                             torch_tensor(c(10L, 0L)), 0, torch_quint8()) torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),                             torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr() } #> torch_tensor #>    0   10 #>  100  200 #> [ CPUByteType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantize_per_tensor — torch_quantize_per_tensor","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"Quantize_per_tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"","code":"torch_quantize_per_tensor(self, scale, zero_point, dtype)"},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"self (Tensor) float tensor quantize scale (float) scale apply quantization formula zero_point (int) offset integer value maps float zero dtype (torch.dtype) desired data type returned tensor.        one quantized dtypes: torch_quint8, torch.qint8, torch.qint32","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-","dir":"Reference","previous_headings":"","what":"quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"Converts float tensor quantized tensor given scale zero point.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_quantize_per_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantize_per_tensor — torch_quantize_per_tensor","text":"","code":"if (torch_is_installed()) { torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8()) torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr() } #> torch_tensor #>   0 #>  10 #>  20 #>  30 #> [ CPUByteType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":null,"dir":"Reference","previous_headings":"","what":"Rad2deg — torch_rad2deg","title":"Rad2deg — torch_rad2deg","text":"Rad2deg","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rad2deg — torch_rad2deg","text":"","code":"torch_rad2deg(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rad2deg — torch_rad2deg","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"rad-deg-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"rad2deg(input, *, out=None) -> Tensor","title":"Rad2deg — torch_rad2deg","text":"Returns new tensor elements input converted angles radians degrees.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rad2deg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rad2deg — torch_rad2deg","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(rbind(c(3.142, -3.142), c(6.283, -6.283), c(1.570, -1.570))) torch_rad2deg(a) } #> torch_tensor #>  180.0233 -180.0233 #>  359.9894 -359.9894 #>   89.9544  -89.9544 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":null,"dir":"Reference","previous_headings":"","what":"Rand — torch_rand","title":"Rand — torch_rand","text":"Rand","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rand — torch_rand","text":"","code":"torch_rand(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rand — torch_rand","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional dimension names dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Rand — torch_rand","text":"Returns tensor filled random numbers uniform distribution interval \\([0, 1)\\) shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rand — torch_rand","text":"","code":"if (torch_is_installed()) {  torch_rand(4) torch_rand(c(2, 3)) } #> torch_tensor #>  0.8258  0.3044  0.1683 #>  0.7110  0.5204  0.0257 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Rand_like — torch_rand_like","title":"Rand_like — torch_rand_like","text":"Rand_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rand_like — torch_rand_like","text":"","code":"torch_rand_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rand_like — torch_rand_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rand_like.html","id":"rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Rand_like — torch_rand_like","text":"Returns tensor size input filled random numbers uniform distribution interval \\([0, 1)\\). torch_rand_like(input) equivalent torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":null,"dir":"Reference","previous_headings":"","what":"Randint — torch_randint","title":"Randint — torch_randint","text":"Randint","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randint — torch_randint","text":"","code":"torch_randint(   low,   high,   size,   generator = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randint — torch_randint","text":"low (int, optional) Lowest integer drawn distribution. Default: 0. high (int) One highest integer drawn distribution. size (tuple) tuple defining shape output tensor. generator (torch.Generator, optional) pseudorandom number generator sampling dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format memory format resulting tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"randint-low-high-size-generator-null-out-null-","dir":"Reference","previous_headings":"","what":"randint(low=0, high, size, *, generator=NULL, out=NULL, \\","title":"Randint — torch_randint","text":"dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor Returns tensor filled random integers generated uniformly low (inclusive) high (exclusive). shape tensor defined variable argument size. .. note: global dtype default (torch_float32), function returns tensor dtype torch_int64.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randint — torch_randint","text":"","code":"if (torch_is_installed()) {  torch_randint(3, 5, list(3)) torch_randint(0, 10, size = list(2, 2)) torch_randint(3, 10, list(2, 2)) } #> torch_tensor #>  6  9 #>  9  9 #> [ CPUFloatType{2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Randint_like — torch_randint_like","title":"Randint_like — torch_randint_like","text":"Randint_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randint_like — torch_randint_like","text":"","code":"torch_randint_like(   input,   low,   high,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randint_like — torch_randint_like","text":"input (Tensor) size input determine size output tensor. low (int, optional) Lowest integer drawn distribution. Default: 0. high (int) One highest integer drawn distribution. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randint_like.html","id":"randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-","dir":"Reference","previous_headings":"","what":"randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False,","title":"Randint_like — torch_randint_like","text":"memory_format=torch.preserve_format) -> Tensor Returns tensor shape Tensor input filled random integers generated uniformly low (inclusive) high (exclusive). .. note: global dtype default (torch_float32), function returns tensor dtype torch_int64.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":null,"dir":"Reference","previous_headings":"","what":"Randn — torch_randn","title":"Randn — torch_randn","text":"Randn","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randn — torch_randn","text":"","code":"torch_randn(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randn — torch_randn","text":"... (int...) sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional names dimensions dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Randn — torch_randn","text":"Returns tensor filled random numbers normal distribution mean 0 variance 1 (also called standard normal distribution). $$     \\mbox{}_{} \\sim \\mathcal{N}(0, 1) $$ shape tensor defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randn — torch_randn","text":"","code":"if (torch_is_installed()) {  torch_randn(c(4)) torch_randn(c(2, 3)) } #> torch_tensor #> -0.3322 -0.4046 -0.0668 #> -0.2351  1.0560  0.8915 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Randn_like — torch_randn_like","title":"Randn_like — torch_randn_like","text":"Randn_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randn_like — torch_randn_like","text":"","code":"torch_randn_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randn_like — torch_randn_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randn_like.html","id":"randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Randn_like — torch_randn_like","text":"Returns tensor size input filled random numbers normal distribution mean 0 variance 1. torch_randn_like(input) equivalent torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":null,"dir":"Reference","previous_headings":"","what":"Randperm — torch_randperm","title":"Randperm — torch_randperm","text":"Randperm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randperm — torch_randperm","text":"","code":"torch_randperm(   n,   dtype = torch_int64(),   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randperm — torch_randperm","text":"n (int) upper bound (exclusive) dtype (torch.dtype, optional) desired data type returned tensor.        Default: torch_int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-","dir":"Reference","previous_headings":"","what":"randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -> LongTensor","title":"Randperm — torch_randperm","text":"Returns random permutation integers 0 n - 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_randperm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Randperm — torch_randperm","text":"","code":"if (torch_is_installed()) {  torch_randperm(4) } #> torch_tensor #>  1 #>  0 #>  2 #>  3 #> [ CPULongType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Range — torch_range","title":"Range — torch_range","text":"Range","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Range — torch_range","text":"","code":"torch_range(   start,   end,   step = 1,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Range — torch_range","text":"start (float) starting value set points. Default: 0. end (float) ending value set points step (float) gap pair adjacent points. Default: 1. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). dtype given, infer data type input        arguments. start, end, stop floating-point,        dtype inferred default dtype, see        ~torch.get_default_dtype. Otherwise, dtype inferred        torch.int64. layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Range — torch_range","text":"Returns 1-D tensor size \\(\\left\\lfloor \\frac{\\mbox{end} - \\mbox{start}}{\\mbox{step}} \\right\\rfloor + 1\\) values start end step step. Step gap two values tensor. $$     \\mbox{}_{+1} = \\mbox{}_i + \\mbox{step}. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Range — torch_range","text":"function deprecated favor torch_arange.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Range — torch_range","text":"","code":"if (torch_is_installed()) {  torch_range(1, 4) torch_range(1, 4, 0.5) } #> Warning: This function is deprecated in favor of torch_arange. #> Warning: This function is deprecated in favor of torch_arange. #> torch_tensor #>  1.0000 #>  1.5000 #>  2.0000 #>  2.5000 #>  3.0000 #>  3.5000 #>  4.0000 #> [ CPUFloatType{7} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":null,"dir":"Reference","previous_headings":"","what":"Real — torch_real","title":"Real — torch_real","text":"Real","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Real — torch_real","text":"","code":"torch_real(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Real — torch_real","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"real-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"real(input) -> Tensor","title":"Real — torch_real","text":"Returns real part input tensor. input real (non-complex) tensor, function just returns .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Real — torch_real","text":"yet implemented complex tensors. $$     \\mbox{}_{} = real(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_real.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Real — torch_real","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ torch_real(torch_tensor(c(-1 + 1i, -2 + 2i, 3 - 3i))) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal — torch_reciprocal","title":"Reciprocal — torch_reciprocal","text":"Reciprocal","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal — torch_reciprocal","text":"","code":"torch_reciprocal(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal — torch_reciprocal","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"reciprocal-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"reciprocal(input, out=NULL) -> Tensor","title":"Reciprocal — torch_reciprocal","text":"Returns new tensor reciprocal elements input $$     \\mbox{}_{} = \\frac{1}{\\mbox{input}_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reciprocal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal — torch_reciprocal","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_reciprocal(a) } #> torch_tensor #> -2.5504 #> -2.8364 #> -30.0044 #>  8.3571 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_reduction.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates the reduction objet — torch_reduction","title":"Creates the reduction objet — torch_reduction","text":"Creates reduction objet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reduction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates the reduction objet — torch_reduction","text":"","code":"torch_reduction_sum()  torch_reduction_mean()  torch_reduction_none()"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu — torch_relu","title":"Relu — torch_relu","text":"Relu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu — torch_relu","text":"","code":"torch_relu(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu — torch_relu","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu.html","id":"relu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"relu(input) -> Tensor","title":"Relu — torch_relu","text":"Computes relu tranformation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Relu_ — torch_relu_","title":"Relu_ — torch_relu_","text":"Relu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Relu_ — torch_relu_","text":"","code":"torch_relu_(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Relu_ — torch_relu_","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_relu_.html","id":"relu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"relu_(input) -> Tensor","title":"Relu_ — torch_relu_","text":"-place version torch_relu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":null,"dir":"Reference","previous_headings":"","what":"Remainder — torch_remainder","title":"Remainder — torch_remainder","text":"Remainder","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remainder — torch_remainder","text":"","code":"torch_remainder(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remainder — torch_remainder","text":"self (Tensor) dividend (Tensor float) divisor may either number                               Tensor shape dividend","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"remainder-input-other-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"remainder(input, other, out=NULL) -> Tensor","title":"Remainder — torch_remainder","text":"Computes element-wise remainder division. divisor dividend may contain integer floating point numbers. remainder sign divisor. tensor, shapes input must broadcastable .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_remainder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remainder — torch_remainder","text":"","code":"if (torch_is_installed()) {  torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2) torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5) } #> torch_tensor #>  1.0000 #>  0.5000 #>  0.0000 #>  1.0000 #>  0.5000 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Renorm — torch_renorm","title":"Renorm — torch_renorm","text":"Renorm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renorm — torch_renorm","text":"","code":"torch_renorm(self, p, dim, maxnorm)"},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renorm — torch_renorm","text":"self (Tensor) input tensor. p (float) power norm computation dim (int) dimension slice get sub-tensors maxnorm (float) maximum norm keep sub-tensor ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Renorm — torch_renorm","text":"norm row lower maxnorm, row unchanged","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"renorm-input-p-dim-maxnorm-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"renorm(input, p, dim, maxnorm, out=NULL) -> Tensor","title":"Renorm — torch_renorm","text":"Returns tensor sub-tensor input along dimension dim normalized p-norm sub-tensor lower value maxnorm","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_renorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Renorm — torch_renorm","text":"","code":"if (torch_is_installed()) { x = torch_ones(c(3, 3)) x[2,]$fill_(2) x[3,]$fill_(3) x torch_renorm(x, 1, 1, 5) } #> torch_tensor #>  1.0000  1.0000  1.0000 #>  1.6667  1.6667  1.6667 #>  1.6667  1.6667  1.6667 #> [ CPUFloatType{3,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":null,"dir":"Reference","previous_headings":"","what":"Repeat_interleave — torch_repeat_interleave","title":"Repeat_interleave — torch_repeat_interleave","text":"Repeat_interleave","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"torch_repeat_interleave(self, repeats, dim = NULL, output_size = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Repeat_interleave — torch_repeat_interleave","text":"self (Tensor) input tensor. repeats (Tensor int) number repetitions element.        repeats broadcasted fit shape given axis. dim (int, optional) dimension along repeat values.        default, use flattened input array, return flat output        array. output_size (int, optional) – Total output size given axis ( e.g. sum repeats). given, avoid stream syncronization needed calculate output shape tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"repeat-interleave-input-repeats-dim-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"repeat_interleave(input, repeats, dim=NULL) -> Tensor","title":"Repeat_interleave — torch_repeat_interleave","text":"Repeat elements tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`."},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"repeat-interleave-repeats-gt-tensor-","dir":"Reference","previous_headings":"","what":"repeat_interleave(repeats) -> Tensor","title":"Repeat_interleave — torch_repeat_interleave","text":"repeats tensor([n1, n2, n3, ...]), output tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...]) 0 appears n1 times, 1 appears n2 times, 2 appears n3 times, etc.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_repeat_interleave.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Repeat_interleave — torch_repeat_interleave","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ x = torch_tensor(c(1, 2, 3)) x$repeat_interleave(2) y = torch_tensor(matrix(c(1, 2, 3, 4), ncol = 2, byrow=TRUE)) torch_repeat_interleave(y, 2) torch_repeat_interleave(y, 3, dim=1) torch_repeat_interleave(y, torch_tensor(c(1, 2)), dim=1) } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Reshape — torch_reshape","title":"Reshape — torch_reshape","text":"Reshape","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reshape — torch_reshape","text":"","code":"torch_reshape(self, shape)"},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reshape — torch_reshape","text":"self (Tensor) tensor reshaped shape (tuple ints) new shape","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"reshape-input-shape-gt-tensor-","dir":"Reference","previous_headings":"","what":"reshape(input, shape) -> Tensor","title":"Reshape — torch_reshape","text":"Returns tensor data number elements input, specified shape. possible, returned tensor view input. Otherwise, copy. Contiguous inputs inputs compatible strides can reshaped without copying, depend copying vs. viewing behavior. See torch_Tensor.view possible return view. single dimension may -1, case inferred remaining dimensions number elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_reshape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reshape — torch_reshape","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(0, 3) torch_reshape(a, list(2, 2)) b <- torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE)) torch_reshape(b, list(-1)) } #> torch_tensor #>  0 #>  1 #>  2 #>  3 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Result_type — torch_result_type","title":"Result_type — torch_result_type","text":"Result_type","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result_type — torch_result_type","text":"","code":"torch_result_type(tensor1, tensor2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result_type — torch_result_type","text":"tensor1 (Tensor Number) input tensor number tensor2 (Tensor Number) input tensor number","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"result-type-tensor-tensor-gt-dtype-","dir":"Reference","previous_headings":"","what":"result_type(tensor1, tensor2) -> dtype","title":"Result_type — torch_result_type","text":"Returns torch_dtype result performing arithmetic operation provided input tensors. See type promotion documentation information type promotion logic.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_result_type.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result_type — torch_result_type","text":"","code":"if (torch_is_installed()) {  torch_result_type(tensor1 = torch_tensor(c(1, 2), dtype=torch_int()), tensor2 = 1) } #> torch_Float"},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":null,"dir":"Reference","previous_headings":"","what":"Roll — torch_roll","title":"Roll — torch_roll","text":"Roll","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Roll — torch_roll","text":"","code":"torch_roll(self, shifts, dims = list())"},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Roll — torch_roll","text":"self (Tensor) input tensor. shifts (int tuple ints) number places elements        tensor shifted. shifts tuple, dims must tuple        size, dimension rolled corresponding        value dims (int tuple ints) Axis along roll","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"roll-input-shifts-dims-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"roll(input, shifts, dims=NULL) -> Tensor","title":"Roll — torch_roll","text":"Roll tensor along given dimension(s). Elements shifted beyond last position re-introduced first position. dimension specified, tensor flattened rolling restored original shape.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_roll.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Roll — torch_roll","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2)) x torch_roll(x, 1, 1) torch_roll(x, -1, 1) torch_roll(x, shifts=list(2, 1), dims=list(1, 2)) } #> torch_tensor #>  6  5 #>  8  7 #>  2  1 #>  4  3 #> [ CPUFloatType{4,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":null,"dir":"Reference","previous_headings":"","what":"Rot90 — torch_rot90","title":"Rot90 — torch_rot90","text":"Rot90","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rot90 — torch_rot90","text":"","code":"torch_rot90(self, k = 1L, dims = c(0, 1))"},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rot90 — torch_rot90","text":"self (Tensor) input tensor. k (int) number times rotate dims (list tuple) axis rotate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"rot-input-k-dims-gt-tensor-","dir":"Reference","previous_headings":"","what":"rot90(input, k, dims) -> Tensor","title":"Rot90 — torch_rot90","text":"Rotate n-D tensor 90 degrees plane specified dims axis. Rotation direction first towards second axis k > 0, second towards first k < 0.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rot90.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rot90 — torch_rot90","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 4)$view(c(2, 2)) x torch_rot90(x, 1, c(1, 2)) x <- torch_arange(1, 8)$view(c(2, 2, 2)) x torch_rot90(x, 1, c(1, 2)) } #> torch_tensor #> (1,.,.) =  #>   3  4 #>   7  8 #>  #> (2,.,.) =  #>   1  2 #>   5  6 #> [ CPUFloatType{2,2,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":null,"dir":"Reference","previous_headings":"","what":"Round — torch_round","title":"Round — torch_round","text":"Round","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round — torch_round","text":"","code":"torch_round(self, decimals)"},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round — torch_round","text":"self (Tensor) input tensor. decimals Number decimal places round (default: 0). decimals negative, specifies number positions left decimal point.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"round-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"round(input, out=NULL) -> Tensor","title":"Round — torch_round","text":"Returns new tensor elements input rounded closest integer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_round.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round — torch_round","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_round(a) } #> torch_tensor #>  1 #> -1 #>  1 #> -1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Rrelu_ — torch_rrelu_","title":"Rrelu_ — torch_rrelu_","text":"Rrelu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rrelu_ — torch_rrelu_","text":"","code":"torch_rrelu_(   self,   lower = 0.125,   upper = 0.333333333333333,   training = FALSE,   generator = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rrelu_ — torch_rrelu_","text":"self input tensor lower lower bound uniform distribution. Default: 1/8 upper upper bound uniform distribution. Default: 1/3 training bool wether training pass. DEfault: FALSE generator random number generator","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rrelu_.html","id":"rrelu-input-lower-upper-training-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor","title":"Rrelu_ — torch_rrelu_","text":"-place version torch_rrelu.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Rsqrt — torch_rsqrt","title":"Rsqrt — torch_rsqrt","text":"Rsqrt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rsqrt — torch_rsqrt","text":"","code":"torch_rsqrt(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rsqrt — torch_rsqrt","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"rsqrt-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"rsqrt(input, out=NULL) -> Tensor","title":"Rsqrt — torch_rsqrt","text":"Returns new tensor reciprocal square-root elements input. $$     \\mbox{}_{} = \\frac{1}{\\sqrt{\\mbox{input}_{}}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_rsqrt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rsqrt — torch_rsqrt","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_rsqrt(a) } #> torch_tensor #>  1.0125 #>     nan #>     nan #>  0.9953 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":null,"dir":"Reference","previous_headings":"","what":"Saves an object to a disk file. — torch_save","title":"Saves an object to a disk file. — torch_save","text":"function experimental, use long term storage.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Saves an object to a disk file. — torch_save","text":"","code":"torch_save(obj, path, ..., compress = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_save.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Saves an object to a disk file. — torch_save","text":"obj saved object path connection name file save. ... currently used. compress logical specifying whether saving named file use \"gzip\" compression, one \"gzip\", \"bzip2\" \"xz\" indicate type compression used. Ignored file connection.","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar tensor — torch_scalar_tensor","title":"Scalar tensor — torch_scalar_tensor","text":"Creates singleton dimension tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar tensor — torch_scalar_tensor","text":"","code":"torch_scalar_tensor(value, dtype = NULL, device = NULL, requires_grad = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_scalar_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar tensor — torch_scalar_tensor","text":"value value want use dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":null,"dir":"Reference","previous_headings":"","what":"Searchsorted — torch_searchsorted","title":"Searchsorted — torch_searchsorted","text":"Searchsorted","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Searchsorted — torch_searchsorted","text":"","code":"torch_searchsorted(   sorted_sequence,   self,   out_int32 = FALSE,   right = FALSE,   side = NULL,   sorter = list() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Searchsorted — torch_searchsorted","text":"sorted_sequence (Tensor) N-D 1-D tensor, containing monotonically increasing sequence innermost dimension. self (Tensor Scalar) N-D tensor Scalar containing search value(s). out_int32 (bool, optional) – indicate output data type. torch_int32() True, torch_int64() otherwise. Default value FALSE, .e. default output data type torch_int64(). right (bool, optional) – False, return first suitable location found. True, return last index. suitable index found, return 0 non-numerical value (eg. nan, inf) size boundaries (one pass last index). words, False, gets lower bound index value input boundaries. True, gets upper bound index instead. Default value False. side right preferred. “left” corresponds FALSE right “right” corresponds TRUE right. error set “left” right TRUE. sorter provided, tensor matching shape unsorted sorted_sequence containing sequence indices sort ascending order innermost dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"searchsorted-sorted-sequence-values-out-int-false-right-false-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor","title":"Searchsorted — torch_searchsorted","text":"Find indices innermost dimension sorted_sequence , corresponding values values inserted indices, order corresponding innermost dimension within sorted_sequence preserved. Return new tensor size values. right FALSE (default), left boundary sorted_sequence closed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_searchsorted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Searchsorted — torch_searchsorted","text":"","code":"if (torch_is_installed()) {  sorted_sequence <- torch_tensor(rbind(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10))) sorted_sequence values <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9))) values torch_searchsorted(sorted_sequence, values) torch_searchsorted(sorted_sequence, values, right=TRUE) sorted_sequence_1d <- torch_tensor(c(1, 3, 5, 7, 9)) sorted_sequence_1d torch_searchsorted(sorted_sequence_1d, values) } #> torch_tensor #>  1  3  4 #>  1  3  4 #> [ CPULongType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu — torch_selu","title":"Selu — torch_selu","text":"Selu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu — torch_selu","text":"","code":"torch_selu(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu — torch_selu","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu.html","id":"selu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"selu(input) -> Tensor","title":"Selu — torch_selu","text":"Computes selu transformation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":null,"dir":"Reference","previous_headings":"","what":"Selu_ — torch_selu_","title":"Selu_ — torch_selu_","text":"Selu_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selu_ — torch_selu_","text":"","code":"torch_selu_(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selu_ — torch_selu_","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_selu_.html","id":"selu-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"selu_(input) -> Tensor","title":"Selu_ — torch_selu_","text":"-place version torch_selu().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":null,"dir":"Reference","previous_headings":"","what":"Serialize a torch object returning a raw object — torch_serialize","title":"Serialize a torch object returning a raw object — torch_serialize","text":"just wraper around torch_save().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Serialize a torch object returning a raw object — torch_serialize","text":"","code":"torch_serialize(obj, ...)"},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Serialize a torch object returning a raw object — torch_serialize","text":"obj saved object ... Additional arguments passed torch_save(). obj path accepted set torch_serialize().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_serialize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Serialize a torch object returning a raw object — torch_serialize","text":"raw vector containing serialized object. Can reloaded using torch_load().","code":""},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":null,"dir":"Reference","previous_headings":"","what":"Sgn — torch_sgn","title":"Sgn — torch_sgn","text":"Sgn","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sgn — torch_sgn","text":"","code":"torch_sgn(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sgn — torch_sgn","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"sgn-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"sgn(input, *, out=None) -> Tensor","title":"Sgn — torch_sgn","text":"complex tensors, function returns new tensor whose elemants angle elements input absolute value 1. non-complex tensor, function returns signs elements input (see torch_sign). \\(\\mbox{}_{} = 0\\), \\(|{\\mbox{{input}}_i}| == 0\\) \\(\\mbox{}_{} = \\frac{{\\mbox{{input}}_i}}{|{\\mbox{{input}}_i}|}\\), otherwise","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sgn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sgn — torch_sgn","text":"","code":"if (torch_is_installed()) { if (FALSE) { x <- torch_tensor(c(3+4i, 7-24i, 0, 1+2i)) x$sgn() torch_sgn(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Sigmoid — torch_sigmoid","title":"Sigmoid — torch_sigmoid","text":"Sigmoid","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sigmoid — torch_sigmoid","text":"","code":"torch_sigmoid(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sigmoid — torch_sigmoid","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"sigmoid-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sigmoid(input, out=NULL) -> Tensor","title":"Sigmoid — torch_sigmoid","text":"Returns new tensor sigmoid elements input. $$     \\mbox{}_{} = \\frac{1}{1 + e^{-\\mbox{input}_{}}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sigmoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sigmoid — torch_sigmoid","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sigmoid(a) } #> torch_tensor #>  0.2432 #>  0.6924 #>  0.5103 #>  0.5446 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":null,"dir":"Reference","previous_headings":"","what":"Sign — torch_sign","title":"Sign — torch_sign","text":"Sign","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sign — torch_sign","text":"","code":"torch_sign(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sign — torch_sign","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"sign-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sign(input, out=NULL) -> Tensor","title":"Sign — torch_sign","text":"Returns new tensor signs elements input. $$     \\mbox{}_{} = \\mbox{sgn}(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sign — torch_sign","text":"","code":"if (torch_is_installed()) {  a = torch_tensor(c(0.7, -1.2, 0., 2.3)) a torch_sign(a) } #> torch_tensor #>  1 #> -1 #>  0 #>  1 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":null,"dir":"Reference","previous_headings":"","what":"Signbit — torch_signbit","title":"Signbit — torch_signbit","text":"Signbit","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signbit — torch_signbit","text":"","code":"torch_signbit(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signbit — torch_signbit","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"signbit-input-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"signbit(input, *, out=None) -> Tensor","title":"Signbit — torch_signbit","text":"Tests element input sign bit set (less zero) .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_signbit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signbit — torch_signbit","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(0.7, -1.2, 0., 2.3)) torch_signbit(a) } #> torch_tensor #>  0 #>  1 #>  0 #>  0 #> [ CPUBoolType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":null,"dir":"Reference","previous_headings":"","what":"Sin — torch_sin","title":"Sin — torch_sin","text":"Sin","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sin — torch_sin","text":"","code":"torch_sin(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sin — torch_sin","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"sin-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sin(input, out=NULL) -> Tensor","title":"Sin — torch_sin","text":"Returns new tensor sine elements input. $$     \\mbox{}_{} = \\sin(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sin — torch_sin","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sin(a) } #> torch_tensor #>  0.3945 #> -0.7831 #>  0.1282 #>  0.9433 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Sinh — torch_sinh","title":"Sinh — torch_sinh","text":"Sinh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sinh — torch_sinh","text":"","code":"torch_sinh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sinh — torch_sinh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"sinh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sinh(input, out=NULL) -> Tensor","title":"Sinh — torch_sinh","text":"Returns new tensor hyperbolic sine elements input. $$     \\mbox{}_{} = \\sinh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sinh — torch_sinh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sinh(a) } #> torch_tensor #>  0.3335 #>  1.5504 #> -1.9916 #> -0.3577 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":null,"dir":"Reference","previous_headings":"","what":"Slogdet — torch_slogdet","title":"Slogdet — torch_slogdet","text":"Slogdet","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Slogdet — torch_slogdet","text":"","code":"torch_slogdet(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Slogdet — torch_slogdet","text":"self (Tensor) input tensor size (*, n, n) * zero                batch dimensions.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Slogdet — torch_slogdet","text":"","code":"If `input` has zero determinant, this returns `(0, -inf)`. Backward through `slogdet` internally uses SVD results when `input` is not invertible. In this case, double backward through `slogdet` will be unstable in when `input` doesn't have distinct singular values. See `~torch.svd` for details."},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"slogdet-input-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"slogdet(input) -> (Tensor, Tensor)","title":"Slogdet — torch_slogdet","text":"Calculates sign log absolute value determinant(s) square matrix batches square matrices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_slogdet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slogdet — torch_slogdet","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(3, 3)) A torch_det(A) torch_logdet(A) torch_slogdet(A) } #> [[1]] #> torch_tensor #> 1 #> [ CPUFloatType{} ] #>  #> [[2]] #> torch_tensor #> 1.12348 #> [ CPUFloatType{} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort — torch_sort","title":"Sort — torch_sort","text":"Sort","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort — torch_sort","text":"self (Tensor) input tensor. dim (int, optional) dimension sort along descending (bool, optional) controls sorting order (ascending descending) stable (bool, optional) – makes sorting routine stable, guarantees order equivalent elements preserved.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"sort-input-dim-descending-false-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"sort(input, dim=-1, descending=FALSE) -> (Tensor, LongTensor)","title":"Sort — torch_sort","text":"Sorts elements input tensor along given dimension ascending order value. dim given, last dimension input chosen. descending TRUE elements sorted descending order value. namedtuple (values, indices) returned, values sorted values indices indices elements original input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort — torch_sort","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(3, 4)) out = torch_sort(x) out out = torch_sort(x, 1) out } #> [[1]] #> torch_tensor #> -0.9502  0.2731 -2.9674 -1.5218 #>  0.7463  0.4347 -0.7894 -0.6639 #>  1.4535  0.7934  1.3197  1.2911 #> [ CPUFloatType{3,4} ] #>  #> [[2]] #> torch_tensor #>  1  3  3  2 #>  2  1  2  3 #>  3  2  1  1 #> [ CPULongType{3,4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Sparse_coo_tensor — torch_sparse_coo_tensor","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"Sparse_coo_tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"","code":"torch_sparse_coo_tensor(   indices,   values,   size = NULL,   dtype = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"indices (array_like) Initial data tensor. Can list, tuple,        NumPy ndarray, scalar, types. cast torch_LongTensor        internally. indices coordinates non-zero values matrix, thus        two-dimensional first dimension number tensor dimensions        second dimension number non-zero values. values (array_like) Initial values tensor. Can list, tuple,        NumPy ndarray, scalar, types. size (list, tuple, torch.Size, optional) Size sparse tensor.        provided size inferred minimum size big enough hold non-zero        elements. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, infers data type values. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"Constructs sparse tensors COO(rdinate) format non-zero elements given indices given values. sparse tensor can uncoalesced, case, duplicate coordinates indices, value index sum duplicate value entries: torch_sparse_.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sparse_coo_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sparse_coo_tensor — torch_sparse_coo_tensor","text":"","code":"if (torch_is_installed()) {  i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64()) v = torch_tensor(c(3, 4, 5), dtype=torch_float32()) torch_sparse_coo_tensor(i, v) torch_sparse_coo_tensor(i, v, c(2, 4))  # create empty sparse tensors S = torch_sparse_coo_tensor(   torch_empty(c(1, 0), dtype = torch_int64()),    torch_tensor(numeric(), dtype = torch_float32()),    c(1) ) S = torch_sparse_coo_tensor(   torch_empty(c(1, 0), dtype = torch_int64()),    torch_empty(c(0, 2)),    c(1, 2) ) }"},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Split — torch_split","title":"Split — torch_split","text":"Splits tensor chunks. chunk view original tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split — torch_split","text":"","code":"torch_split(self, split_size, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split — torch_split","text":"self (Tensor) tensor split. split_size (int) size single chunk list sizes chunk dim (int) dimension along split tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split — torch_split","text":"split_size integer type, tensor split equally sized chunks (possible). Last chunk smaller tensor size along given dimension dim divisible split_size. split_size list, tensor split length(split_size) chunks sizes dim according split_size_or_sections.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":null,"dir":"Reference","previous_headings":"","what":"Sqrt — torch_sqrt","title":"Sqrt — torch_sqrt","text":"Sqrt","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sqrt — torch_sqrt","text":"","code":"torch_sqrt(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sqrt — torch_sqrt","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"sqrt-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sqrt(input, out=NULL) -> Tensor","title":"Sqrt — torch_sqrt","text":"Returns new tensor square-root elements input. $$     \\mbox{}_{} = \\sqrt{\\mbox{input}_{}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sqrt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sqrt — torch_sqrt","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_sqrt(a) } #> torch_tensor #>     nan #>     nan #>  0.5573 #>  0.5131 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":null,"dir":"Reference","previous_headings":"","what":"Square — torch_square","title":"Square — torch_square","text":"Square","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Square — torch_square","text":"","code":"torch_square(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Square — torch_square","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"square-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"square(input, out=NULL) -> Tensor","title":"Square — torch_square","text":"Returns new tensor square elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_square.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Square — torch_square","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_square(a) } #> torch_tensor #>  0.0045 #>  0.3852 #>  0.3863 #>  2.2824 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Squeeze — torch_squeeze","title":"Squeeze — torch_squeeze","text":"Squeeze","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Squeeze — torch_squeeze","text":"","code":"torch_squeeze(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Squeeze — torch_squeeze","text":"self (Tensor) input tensor. dim (int, optional) given, input squeezed           dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Squeeze — torch_squeeze","text":"returned tensor shares storage input tensor, changing contents one change contents .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"squeeze-input-dim-null-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"squeeze(input, dim=NULL, out=NULL) -> Tensor","title":"Squeeze — torch_squeeze","text":"Returns tensor dimensions input size 1 removed. example, input shape: \\((\\times 1 \\times B \\times C \\times 1 \\times D)\\) tensor shape: \\((\\times B \\times C \\times D)\\). dim given, squeeze operation done given dimension. input shape: \\((\\times 1 \\times B)\\), squeeze(input, 0) leaves tensor unchanged, squeeze(input, 1) squeeze tensor shape \\((\\times B)\\).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_squeeze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Squeeze — torch_squeeze","text":"","code":"if (torch_is_installed()) {  x = torch_zeros(c(2, 1, 2, 1, 2)) x y = torch_squeeze(x) y y = torch_squeeze(x, 1) y y = torch_squeeze(x, 2) y } #> torch_tensor #> (1,1,.,.) =  #>   0  0 #>  #> (2,1,.,.) =  #>   0  0 #>  #> (1,2,.,.) =  #>   0  0 #>  #> (2,2,.,.) =  #>   0  0 #> [ CPUFloatType{2,2,1,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack — torch_stack","title":"Stack — torch_stack","text":"Stack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack — torch_stack","text":"","code":"torch_stack(tensors, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack — torch_stack","text":"tensors (sequence Tensors) sequence tensors concatenate dim (int) dimension insert. 0 number        dimensions concatenated tensors (inclusive)","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stack.html","id":"stack-tensors-dim-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"stack(tensors, dim=0, out=NULL) -> Tensor","title":"Stack — torch_stack","text":"Concatenates sequence tensors along new dimension. tensors need size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":null,"dir":"Reference","previous_headings":"","what":"Std — torch_std","title":"Std — torch_std","text":"Std","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Std — torch_std","text":"","code":"torch_std(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Std — torch_std","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"std-input-unbiased-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"std(input, unbiased=TRUE) -> Tensor","title":"Std — torch_std","text":"Returns standard-deviation elements input tensor. unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -> Tensor","title":"Std — torch_std","text":"Returns standard-deviation row input tensor dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Std — torch_std","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_std(a)   a = torch_randn(c(4, 4)) a torch_std(a, dim=1) } #> torch_tensor #>  2.0246 #>  0.5911 #>  1.1543 #>  0.5795 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Std_mean — torch_std_mean","title":"Std_mean — torch_std_mean","text":"Std_mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Std_mean — torch_std_mean","text":"","code":"torch_std_mean(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Std_mean — torch_std_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"std-mean-input-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"std_mean(input, unbiased=TRUE) -> (Tensor, Tensor)","title":"Std_mean — torch_std_mean","text":"Returns standard-deviation mean elements input tensor. unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"std_mean(input, dim, unbiased=TRUE, keepdim=False) -> (Tensor, Tensor)","title":"Std_mean — torch_std_mean","text":"Returns standard-deviation mean row input tensor dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, standard-deviation calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_std_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Std_mean — torch_std_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_std_mean(a)   a = torch_randn(c(4, 4)) a torch_std_mean(a, 1) } #> [[1]] #> torch_tensor #>  0.8341 #>  0.6614 #>  0.7547 #>  0.7540 #> [ CPUFloatType{4} ] #>  #> [[2]] #> torch_tensor #>  0.4981 #>  0.7320 #>  0.5279 #>  0.5551 #> [ CPUFloatType{4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":null,"dir":"Reference","previous_headings":"","what":"Stft — torch_stft","title":"Stft — torch_stft","text":"Stft","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stft — torch_stft","text":"","code":"torch_stft(   input,   n_fft,   hop_length = NULL,   win_length = NULL,   window = NULL,   center = TRUE,   pad_mode = \"reflect\",   normalized = FALSE,   onesided = NULL,   return_complex = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stft — torch_stft","text":"input (Tensor) input tensor n_fft (int) size Fourier transform hop_length (int, optional) distance neighboring sliding window frames. Default: NULL (treated equal floor(n_fft / 4)) win_length (int, optional) size window frame STFT filter. Default: NULL  (treated equal n_fft) window (Tensor, optional) optional window function. Default: NULL (treated window \\(1\\) s) center (bool, optional) whether pad input sides \\(t\\)-th frame centered time \\(t \\times \\mbox{hop\\_length}\\). Default: TRUE pad_mode (string, optional) controls padding method used center TRUE. Default: \"reflect\" normalized (bool, optional) controls whether return normalized STFT results Default: FALSE onesided (bool, optional) controls whether return half results avoid redundancy Default: TRUE return_complex (bool, optional) controls whether return complex tensors .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"short-time-fourier-transform-stft-","dir":"Reference","previous_headings":"","what":"Short-time Fourier transform (STFT).","title":"Stft — torch_stft","text":"Short-time Fourier transform (STFT).   $$         X[m, \\omega] = \\sum_{k = 0}^{\\mbox{win\\_length-1}}%                             \\mbox{window}[k]\\ \\mbox{input}[m \\times \\mbox{hop\\_length} + k]\\ %                             \\exp\\left(- j \\frac{2 \\pi \\cdot \\omega k}{\\mbox{win\\_length}}\\right), $$ \\(m\\) index sliding window, \\(\\omega\\) frequency \\(0 \\leq \\omega < \\mbox{n\\_fft}\\). onesided default value TRUE,","code":"Ignoring the optional batch dimension, this method computes the following expression: * `input` must be either a 1-D time sequence or a 2-D batch of time   sequences.  * If `hop_length` is `NULL` (default), it is treated as equal to   `floor(n_fft / 4)`.  * If `win_length` is `NULL` (default), it is treated as equal to   `n_fft`.  * `window` can be a 1-D tensor of size `win_length`, e.g., from   `torch_hann_window`. If `window` is `NULL` (default), it is   treated as if having \\eqn{1} everywhere in the window. If   \\eqn{\\mbox{win\\_length} < \\mbox{n\\_fft}}, `window` will be padded on   both sides to length `n_fft` before being applied.  * If `center` is `TRUE` (default), `input` will be padded on   both sides so that the \\eqn{t}-th frame is centered at time   \\eqn{t \\times \\mbox{hop\\_length}}. Otherwise, the \\eqn{t}-th frame   begins at time  \\eqn{t \\times \\mbox{hop\\_length}}.  * `pad_mode` determines the padding method used on `input` when   `center` is `TRUE`. See `torch_nn.functional.pad` for   all available options. Default is `\"reflect\"`.  * If `onesided` is `TRUE` (default), only values for \\eqn{\\omega}   in \\eqn{\\left[0, 1, 2, \\dots, \\left\\lfloor \\frac{\\mbox{n\\_fft}}{2} \\right\\rfloor + 1\\right]}   are returned because the real-to-complex Fourier transform satisfies the   conjugate symmetry, i.e., \\eqn{X[m, \\omega] = X[m, \\mbox{n\\_fft} - \\omega]^*}.  * If `normalized` is `TRUE` (default is `FALSE`), the function   returns the normalized STFT results, i.e., multiplied by \\eqn{(\\mbox{frame\\_length})^{-0.5}}.  Returns the real and the imaginary parts together as one tensor of size \\eqn{(* \\times N \\times T \\times 2)}, where \\eqn{*} is the optional batch size of `input`, \\eqn{N} is the number of frequencies where STFT is applied, \\eqn{T} is the total number of frames used, and each pair in the last dimension represents a complex number as the real part and the imaginary part."},{"path":"https://torch.mlverse.org/docs/reference/torch_stft.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Stft — torch_stft","text":"function changed signature version 0.4.1. Calling previous signature may cause error return incorrect result.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":null,"dir":"Reference","previous_headings":"","what":"Sub — torch_sub","title":"Sub — torch_sub","text":"Sub","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sub — torch_sub","text":"","code":"torch_sub(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sub — torch_sub","text":"self (Tensor) input tensor. (Tensor Scalar) tensor scalar subtract input alpha scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"sub-input-other-alpha-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"sub(input, other, *, alpha=1, out=None) -> Tensor","title":"Sub — torch_sub","text":"Subtracts , scaled alpha, input. $$     \\mbox{{}}_i = \\mbox{{input}}_i - \\mbox{{alpha}} \\times \\mbox{{}}_i $$ Supports broadcasting common shape , type promotion , integer, float, complex inputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sub.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sub — torch_sub","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2)) b <- torch_tensor(c(0, 1)) torch_sub(a, b, alpha=2) } #> torch_tensor #>  1 #>  0 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":null,"dir":"Reference","previous_headings":"","what":"Subtract — torch_subtract","title":"Subtract — torch_subtract","text":"Subtract","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subtract — torch_subtract","text":"","code":"torch_subtract(self, other, alpha = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subtract — torch_subtract","text":"self (Tensor) input tensor. (Tensor Scalar) tensor scalar subtract input alpha scalar multiplier ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_subtract.html","id":"subtract-input-other-alpha-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"subtract(input, other, *, alpha=1, out=None) -> Tensor","title":"Subtract — torch_subtract","text":"Alias torch_sub().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Sum — torch_sum","title":"Sum — torch_sum","text":"Sum","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sum — torch_sum","text":"","code":"torch_sum(self, dim, keepdim = FALSE, dtype = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sum — torch_sum","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. keepdim (bool) whether output tensor dim retained . dtype (torch.dtype, optional) desired data type returned tensor.        specified, input tensor casted dtype operation        performed. useful preventing data type overflows. Default: NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"sum-input-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sum(input, dtype=NULL) -> Tensor","title":"Sum — torch_sum","text":"Returns sum elements input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"sum-input-dim-keepdim-false-dtype-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"sum(input, dim, keepdim=False, dtype=NULL) -> Tensor","title":"Sum — torch_sum","text":"Returns sum row input tensor given dimension dim. dim list dimensions, reduce . keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sum — torch_sum","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_sum(a)   a <- torch_randn(c(4, 4)) a torch_sum(a, 1) b <- torch_arange(1, 4 * 5 * 6)$view(c(4, 5, 6)) torch_sum(b, list(2, 1)) } #> torch_tensor #>  1160 #>  1180 #>  1200 #>  1220 #>  1240 #>  1260 #> [ CPUFloatType{6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":null,"dir":"Reference","previous_headings":"","what":"Svd — torch_svd","title":"Svd — torch_svd","text":"Svd","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Svd — torch_svd","text":"","code":"torch_svd(self, some = TRUE, compute_uv = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Svd — torch_svd","text":"self (Tensor) input tensor size \\((*, m, n)\\) * zero                    batch dimensions consisting \\(m \\times n\\) matrices. (bool, optional) controls shape returned U V compute_uv (bool, optional) option whether compute U V ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Svd — torch_svd","text":"singular values returned descending order. input batch matrices, singular values matrix batch returned descending order. implementation SVD CPU uses LAPACK routine ?gesdd (divide--conquer algorithm) instead ?gesvd speed. Analogously, SVD GPU uses MAGMA routine gesdd well. Irrespective original strides, returned matrix U transposed, .e. strides U.contiguous().transpose(-2, -1).stride() Extra care needs taken backward U V outputs. operation really stable input full rank distinct singular values. Otherwise, NaN can appear gradients properly defined. Also, notice double backward usually additional backward U V even original backward S. = FALSE, gradients U[..., :, min(m, n):] V[..., :, min(m, n):] ignored backward vectors can arbitrary bases subspaces. compute_uv = FALSE, backward performed since U V forward pass required backward operation.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-","dir":"Reference","previous_headings":"","what":"svd(input, some=TRUE, compute_uv=TRUE) -> (Tensor, Tensor, Tensor)","title":"Svd — torch_svd","text":"function returns namedtuple (U, S, V) singular value decomposition input real matrix batches real matrices input \\(input = U \\times diag(S) \\times V^T\\). TRUE (default), method returns reduced singular value decomposition .e., last two dimensions input m n, returned U V matrices contain \\(min(n, m)\\) orthonormal columns. compute_uv FALSE, returned U V matrices zero matrices shape \\((m \\times m)\\) \\((n \\times n)\\) respectively. ignored .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_svd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Svd — torch_svd","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(5, 3)) a out = torch_svd(a) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t())) a_big = torch_randn(c(7, 5, 3)) out = torch_svd(a_big) u = out[[1]] s = out[[2]] v = out[[3]] torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1))) } #> torch_tensor #> 2.56462e-06 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":null,"dir":"Reference","previous_headings":"","what":"T — torch_t","title":"T — torch_t","text":"T","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"T — torch_t","text":"","code":"torch_t(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"T — torch_t","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"t-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"t(input) -> Tensor","title":"T — torch_t","text":"Expects input <= 2-D tensor transposes dimensions 0 1. 0-D 1-D tensors returned . input 2-D tensor equivalent transpose(input, 0, 1).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"T — torch_t","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2,3)) x torch_t(x) x = torch_randn(c(3)) x torch_t(x) x = torch_randn(c(2, 3)) x torch_t(x) } #> torch_tensor #>  0.2638 -0.3001 #>  0.4795 -0.0578 #>  1.2955  0.0028 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":null,"dir":"Reference","previous_headings":"","what":"Take — torch_take","title":"Take — torch_take","text":"Take","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Take — torch_take","text":"","code":"torch_take(self, index)"},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Take — torch_take","text":"self (Tensor) input tensor. index (LongTensor) indices tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"take-input-index-gt-tensor-","dir":"Reference","previous_headings":"","what":"take(input, index) -> Tensor","title":"Take — torch_take","text":"Returns new tensor elements input given indices. input tensor treated viewed 1-D tensor. result takes shape indices.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Take — torch_take","text":"","code":"if (torch_is_installed()) {  src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE)) torch_take(src, torch_tensor(c(1, 2, 5), dtype = torch_int64())) } #> torch_tensor #>  4 #>  3 #>  7 #> [ CPUFloatType{3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_take_along_dim.html","id":null,"dir":"Reference","previous_headings":"","what":"Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim","title":"Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim","text":"Selects values input 1-dimensional indices indices along given dim.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take_along_dim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim","text":"","code":"torch_take_along_dim(self, indices, dim = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_take_along_dim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim","text":"self input tensor. indices indices input. Must long dtype. dim dimension select along. Default NULL.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take_along_dim.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim","text":"dim NULL, input array treated flattened 1d. Functions return indices along dimension, like torch_argmax() torch_argsort(), designed work function. See examples .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_take_along_dim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Selects values from input at the 1-dimensional indices from indices along the given dim. — torch_take_along_dim","text":"","code":"if (torch_is_installed()) { t <- torch_tensor(matrix(c(10, 30, 20, 60, 40, 50), nrow = 2)) max_idx <- torch_argmax(t) torch_take_along_dim(t, max_idx)  sorted_idx <- torch_argsort(t, dim=2) torch_take_along_dim(t, sorted_idx, dim=2)  } #> torch_tensor #>  10  20  40 #>  30  50  60 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":null,"dir":"Reference","previous_headings":"","what":"Tan — torch_tan","title":"Tan — torch_tan","text":"Tan","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tan — torch_tan","text":"","code":"torch_tan(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tan — torch_tan","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"tan-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tan(input, out=NULL) -> Tensor","title":"Tan — torch_tan","text":"Returns new tensor tangent elements input. $$     \\mbox{}_{} = \\tan(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tan.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tan — torch_tan","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_tan(a) } #> torch_tensor #>  1.0479 #>  1.6779 #> -0.9440 #>  0.2676 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":null,"dir":"Reference","previous_headings":"","what":"Tanh — torch_tanh","title":"Tanh — torch_tanh","text":"Tanh","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tanh — torch_tanh","text":"","code":"torch_tanh(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tanh — torch_tanh","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"tanh-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tanh(input, out=NULL) -> Tensor","title":"Tanh — torch_tanh","text":"Returns new tensor hyperbolic tangent elements input. $$     \\mbox{}_{} = \\tanh(\\mbox{input}_{}) $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tanh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tanh — torch_tanh","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_tanh(a) } #> torch_tensor #>  0.5940 #>  0.9192 #> -0.1055 #> -0.6897 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts R objects to a torch tensor — torch_tensor","title":"Converts R objects to a torch tensor — torch_tensor","text":"Converts R objects torch tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts R objects to a torch tensor — torch_tensor","text":"","code":"torch_tensor(   data,   dtype = NULL,   device = NULL,   requires_grad = FALSE,   pin_memory = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts R objects to a torch tensor — torch_tensor","text":"data R atomic vector, matrix array dtype torch_dtype instance device device creted torch_device() requires_grad autograd record operations returned tensor. pin_memory set, returned tensor allocated pinned memory.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts R objects to a torch tensor — torch_tensor","text":"","code":"if (torch_is_installed()) { torch_tensor(c(1, 2, 3, 4)) torch_tensor(c(1, 2, 3, 4), dtype = torch_int()) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPUIntType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"creates tensor without taking ownership memory points . must call clone want copy memory new tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"","code":"torch_tensor_from_buffer(buffer, shape, dtype = \"float\")  buffer_from_torch_tensor(tensor)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"buffer R atomic object containing data contiguous array. shape shape resulting tensor. dtype torch data type tresulting tensor. tensor Tensor object converted buffer.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensor_from_buffer.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Creates a tensor from a buffer of memory — torch_tensor_from_buffer","text":"buffer_from_torch_tensor(): Creates raw vector containing tensor data. Causes data copy.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":null,"dir":"Reference","previous_headings":"","what":"Tensordot — torch_tensordot","title":"Tensordot — torch_tensordot","text":"Returns contraction b multiple dimensions. tensordot implements generalized matrix product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tensordot — torch_tensordot","text":"","code":"torch_tensordot(a, b, dims = 2)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tensordot — torch_tensordot","text":"(Tensor) Left tensor contract b (Tensor) Right tensor contract dims (int tuple two lists integers) number dimensions     contract explicit lists dimensions     b respectively","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tensordot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tensordot — torch_tensordot","text":"","code":"if (torch_is_installed()) {  a <- torch_arange(start = 1, end = 60)$reshape(c(3, 4, 5)) b <- torch_arange(start = 1, end = 24)$reshape(c(4, 3, 2)) torch_tensordot(a, b, dims = list(c(2, 1), c(1, 2))) if (FALSE) { # \\dontrun{ a = torch_randn(3, 4, 5, device='cuda') b = torch_randn(4, 5, 6, device='cuda') c = torch_tensordot(a, b, dims=2)$cpu() } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":null,"dir":"Reference","previous_headings":"","what":"Threshold_ — torch_threshold_","title":"Threshold_ — torch_threshold_","text":"Threshold_","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Threshold_ — torch_threshold_","text":"","code":"torch_threshold_(self, threshold, value)"},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Threshold_ — torch_threshold_","text":"self input tensor threshold value threshold value value replace ","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_threshold_.html","id":"threshold-input-threshold-value-gt-tensor-","dir":"Reference","previous_headings":"","what":"threshold_(input, threshold, value) -> Tensor","title":"Threshold_ — torch_threshold_","text":"-place version torch_threshold.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":null,"dir":"Reference","previous_headings":"","what":"Topk — torch_topk","title":"Topk — torch_topk","text":"Topk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Topk — torch_topk","text":"","code":"torch_topk(self, k, dim = -1L, largest = TRUE, sorted = TRUE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Topk — torch_topk","text":"self (Tensor) input tensor. k (int) k \"top-k\" dim (int, optional) dimension sort along largest (bool, optional) controls whether return largest           smallest elements sorted (bool, optional) controls whether return elements           sorted order","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-","dir":"Reference","previous_headings":"","what":"topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -> (Tensor, LongTensor)","title":"Topk — torch_topk","text":"Returns k largest elements given input tensor along given dimension. dim given, last dimension input chosen. largest FALSE k smallest elements returned. namedtuple (values, indices) returned, indices indices elements original input tensor. boolean option sorted TRUE, make sure returned k elements sorted","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_topk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Topk — torch_topk","text":"","code":"if (torch_is_installed()) {  x = torch_arange(1., 6.) x torch_topk(x, 3) } #> [[1]] #> torch_tensor #>  6 #>  5 #>  4 #> [ CPUFloatType{3} ] #>  #> [[2]] #> torch_tensor #>  6 #>  5 #>  4 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":null,"dir":"Reference","previous_headings":"","what":"Trace — torch_trace","title":"Trace — torch_trace","text":"Trace","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trace — torch_trace","text":"","code":"torch_trace(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trace — torch_trace","text":"self input tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"trace-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"trace(input) -> Tensor","title":"Trace — torch_trace","text":"Returns sum elements diagonal input 2-D matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trace.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trace — torch_trace","text":"","code":"if (torch_is_installed()) {  x <- torch_arange(1, 9)$view(c(3, 3)) x torch_trace(x) } #> torch_tensor #> 15 #> [ CPUFloatType{} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose — torch_transpose","title":"Transpose — torch_transpose","text":"Transpose","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose — torch_transpose","text":"","code":"torch_transpose(self, dim0, dim1)"},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose — torch_transpose","text":"self (Tensor) input tensor. dim0 (int) first dimension transposed dim1 (int) second dimension transposed","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"transpose-input-dim-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"transpose(input, dim0, dim1) -> Tensor","title":"Transpose — torch_transpose","text":"Returns tensor transposed version input. given dimensions dim0 dim1 swapped. resulting tensor shares underlying storage input tensor, changing content one change content .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_transpose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transpose — torch_transpose","text":"","code":"if (torch_is_installed()) {  x = torch_randn(c(2, 3)) x torch_transpose(x, 1, 2) } #> torch_tensor #> -0.1662 -0.6476 #> -0.5733  1.8846 #>  1.3872  0.7389 #> [ CPUFloatType{3,2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":null,"dir":"Reference","previous_headings":"","what":"Trapz — torch_trapz","title":"Trapz — torch_trapz","text":"Trapz","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trapz — torch_trapz","text":"","code":"torch_trapz(y, dx = 1L, x, dim = -1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trapz — torch_trapz","text":"y (Tensor) values function integrate dx (float) distance points y sampled. x (Tensor) points function y sampled.        x ascending order, intervals decreasing        contribute negatively estimated integral (.e., convention        \\(\\int_a^b f = -\\int_b^f\\) followed). dim (int) dimension along integrate.        default, use last dimension.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"trapz-y-x-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"trapz(y, x, *, dim=-1) -> Tensor","title":"Trapz — torch_trapz","text":"Estimate \\(\\int y\\,dx\\) along dim, using trapezoid rule.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"trapz-y-dx-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"trapz(y, *, dx=1, dim=-1) -> Tensor","title":"Trapz — torch_trapz","text":", sample points spaced uniformly distance dx.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trapz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trapz — torch_trapz","text":"","code":"if (torch_is_installed()) {  y = torch_randn(list(2, 3)) y x = torch_tensor(matrix(c(1, 3, 4, 1, 2, 3), ncol = 3, byrow=TRUE)) torch_trapz(y, x = x)  } #> torch_tensor #>  0.3347 #>  1.7395 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular_solve — torch_triangular_solve","title":"Triangular_solve — torch_triangular_solve","text":"Triangular_solve","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular_solve — torch_triangular_solve","text":"","code":"torch_triangular_solve(   self,   A,   upper = TRUE,   transpose = FALSE,   unitriangular = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular_solve — torch_triangular_solve","text":"self (Tensor) multiple right-hand sides size \\((*, m, k)\\)                \\(*\\) zero batch dimensions (\\(b\\)) (Tensor) input triangular coefficient matrix size \\((*, m, m)\\)                \\(*\\) zero batch dimensions upper (bool, optional) whether solve upper-triangular system        equations (default) lower-triangular system equations. Default: TRUE. transpose (bool, optional) whether \\(\\) transposed        sent solver. Default: FALSE. unitriangular (bool, optional) whether \\(\\) unit triangular.        TRUE, diagonal elements \\(\\) assumed        1 referenced \\(\\). Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -> (Tensor, Tensor)","title":"Triangular_solve — torch_triangular_solve","text":"Solves system equations triangular coefficient matrix \\(\\) multiple right-hand sides \\(b\\). particular, solves \\(AX = b\\) assumes \\(\\) upper-triangular default keyword arguments. torch_triangular_solve(b, ) can take 2D inputs b, inputs batches 2D matrices. inputs batches, returns batched outputs X","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triangular_solve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangular_solve — torch_triangular_solve","text":"","code":"if (torch_is_installed()) {  A = torch_randn(c(2, 2))$triu() A b = torch_randn(c(2, 3)) b torch_triangular_solve(b, A) } #> [[1]] #> torch_tensor #> -0.4159 -19.8541 -28.8686 #> -9.9554 -26.7462 -40.0722 #> [ CPUFloatType{2,3} ] #>  #> [[2]] #> torch_tensor #>  0.2344 -0.1714 #>  0.0000 -0.0268 #> [ CPUFloatType{2,2} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":null,"dir":"Reference","previous_headings":"","what":"Tril — torch_tril","title":"Tril — torch_tril","text":"Tril","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tril — torch_tril","text":"","code":"torch_tril(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tril — torch_tril","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"tril-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"tril(input, diagonal=0, out=NULL) -> Tensor","title":"Tril — torch_tril","text":"Returns lower triangular part matrix (2-D tensor) batch matrices input, elements result tensor set 0. lower triangular part matrix defined elements diagonal. argument diagonal controls diagonal consider. diagonal = 0, elements main diagonal retained. positive value includes just many diagonals main diagonal, similarly negative value excludes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tril — torch_tril","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_tril(a) b = torch_randn(c(4, 6)) b torch_tril(b, diagonal=1) torch_tril(b, diagonal=-1) } #> torch_tensor #>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000 #>  0.7947  0.0000  0.0000  0.0000  0.0000  0.0000 #>  0.5983 -0.5067  0.0000  0.0000  0.0000  0.0000 #> -0.8538  0.6799 -0.4265  0.0000  0.0000  0.0000 #> [ CPUFloatType{4,6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Tril_indices — torch_tril_indices","title":"Tril_indices — torch_tril_indices","text":"Tril_indices","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tril_indices — torch_tril_indices","text":"","code":"torch_tril_indices(   row,   col,   offset = 0,   dtype = NULL,   device = NULL,   layout = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tril_indices — torch_tril_indices","text":"row (int) number rows 2-D matrix. col (int) number columns 2-D matrix. offset (int) diagonal offset main diagonal.        Default: provided, 0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, torch_long. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. layout (torch.layout, optional) currently support torch_strided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tril_indices — torch_tril_indices","text":"","code":"When running on CUDA, `row * col` must be less than \\eqn{2^{59}} to prevent overflow during calculation."},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-","dir":"Reference","previous_headings":"","what":"tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor","title":"Tril_indices — torch_tril_indices","text":"Returns indices lower triangular part row-- col matrix 2--N Tensor, first row contains row coordinates indices second row contains column coordinates. Indices ordered based rows columns. lower triangular part matrix defined elements diagonal. argument offset controls diagonal consider. offset = 0, elements main diagonal retained. positive value includes just many diagonals main diagonal, similarly negative value excludes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_tril_indices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tril_indices — torch_tril_indices","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ a = torch_tril_indices(3, 3) a a = torch_tril_indices(4, 3, -1) a a = torch_tril_indices(4, 3, 1) a } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":null,"dir":"Reference","previous_headings":"","what":"Triu — torch_triu","title":"Triu — torch_triu","text":"Triu","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triu — torch_triu","text":"","code":"torch_triu(self, diagonal = 0L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triu — torch_triu","text":"self (Tensor) input tensor. diagonal (int, optional) diagonal consider","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"triu-input-diagonal-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"triu(input, diagonal=0, out=NULL) -> Tensor","title":"Triu — torch_triu","text":"Returns upper triangular part matrix (2-D tensor) batch matrices input, elements result tensor set 0. upper triangular part matrix defined elements diagonal. argument diagonal controls diagonal consider. diagonal = 0, elements main diagonal retained. positive value excludes just many diagonals main diagonal, similarly negative value includes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triu — torch_triu","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(3, 3)) a torch_triu(a) torch_triu(a, diagonal=1) torch_triu(a, diagonal=-1) b = torch_randn(c(4, 6)) b torch_triu(b, diagonal=1) torch_triu(b, diagonal=-1) } #> torch_tensor #>  1.1087 -0.8975 -1.0364 -2.0779 -2.4391  1.0559 #>  0.8972 -0.5021 -0.7000 -1.7786  0.3989 -0.1569 #>  0.0000 -1.9678  1.9087  1.0738  1.5483 -0.7463 #>  0.0000  0.0000 -1.3872  0.3273  1.6337 -0.7628 #> [ CPUFloatType{4,6} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Triu_indices — torch_triu_indices","title":"Triu_indices — torch_triu_indices","text":"Triu_indices","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triu_indices — torch_triu_indices","text":"","code":"torch_triu_indices(   row,   col,   offset = 0,   dtype = NULL,   device = NULL,   layout = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triu_indices — torch_triu_indices","text":"row (int) number rows 2-D matrix. col (int) number columns 2-D matrix. offset (int) diagonal offset main diagonal.        Default: provided, 0. dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, torch_long. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. layout (torch.layout, optional) currently support torch_strided.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Triu_indices — torch_triu_indices","text":"","code":"When running on CUDA, `row * col` must be less than \\eqn{2^{59}} to prevent overflow during calculation."},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-","dir":"Reference","previous_headings":"","what":"triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor","title":"Triu_indices — torch_triu_indices","text":"Returns indices upper triangular part row col matrix 2--N Tensor, first row contains row coordinates indices second row contains column coordinates. Indices ordered based rows columns. upper triangular part matrix defined elements diagonal. argument offset controls diagonal consider. offset = 0, elements main diagonal retained. positive value excludes just many diagonals main diagonal, similarly negative value includes just many diagonals main diagonal. main diagonal set indices \\(\\lbrace (, ) \\rbrace\\) \\(\\[0, \\min\\{d_{1}, d_{2}\\} - 1]\\) \\(d_{1}, d_{2}\\) dimensions matrix.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_triu_indices.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triu_indices — torch_triu_indices","text":"","code":"if (torch_is_installed()) { if (FALSE) { # \\dontrun{ a = torch_triu_indices(3, 3) a a = torch_triu_indices(4, 3, -1) a a = torch_triu_indices(4, 3, 1) a } # } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":null,"dir":"Reference","previous_headings":"","what":"TRUE_divide — torch_true_divide","title":"TRUE_divide — torch_true_divide","text":"TRUE_divide","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRUE_divide — torch_true_divide","text":"","code":"torch_true_divide(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRUE_divide — torch_true_divide","text":"self (Tensor) dividend (Tensor Scalar) divisor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"true-divide-dividend-divisor-gt-tensor-","dir":"Reference","previous_headings":"","what":"true_divide(dividend, divisor) -> Tensor","title":"TRUE_divide — torch_true_divide","text":"Performs \"true division\" always computes division floating point. Analogous division Python 3 equivalent torch_div except inputs bool integer scalar types, case cast default (floating) scalar type division. $$     \\mbox{}_i = \\frac{\\mbox{dividend}_i}{\\mbox{divisor}} $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_true_divide.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRUE_divide — torch_true_divide","text":"","code":"if (torch_is_installed()) {  dividend = torch_tensor(c(5, 3), dtype=torch_int()) divisor = torch_tensor(c(3, 2), dtype=torch_int()) torch_true_divide(dividend, divisor) torch_true_divide(dividend, 2) } #> torch_tensor #>  2.5000 #>  1.5000 #> [ CPUFloatType{2} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":null,"dir":"Reference","previous_headings":"","what":"Trunc — torch_trunc","title":"Trunc — torch_trunc","text":"Trunc","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trunc — torch_trunc","text":"","code":"torch_trunc(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trunc — torch_trunc","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"trunc-input-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"trunc(input, out=NULL) -> Tensor","title":"Trunc — torch_trunc","text":"Returns new tensor truncated integer values elements input.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_trunc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trunc — torch_trunc","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(4)) a torch_trunc(a) } #> torch_tensor #> -0 #> -1 #>  0 #> -0 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":null,"dir":"Reference","previous_headings":"","what":"Unbind — torch_unbind","title":"Unbind — torch_unbind","text":"Unbind","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unbind — torch_unbind","text":"","code":"torch_unbind(self, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unbind — torch_unbind","text":"self (Tensor) tensor unbind dim (int) dimension remove","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"unbind-input-dim-gt-seq-","dir":"Reference","previous_headings":"","what":"unbind(input, dim=0) -> seq","title":"Unbind — torch_unbind","text":"Removes tensor dimension. Returns tuple slices along given dimension, already without .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unbind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unbind — torch_unbind","text":"","code":"if (torch_is_installed()) {  torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE))) } #> [[1]] #> torch_tensor #>  1 #>  2 #>  3 #> [ CPULongType{3} ] #>  #> [[2]] #> torch_tensor #>  4 #>  5 #>  6 #> [ CPULongType{3} ] #>  #> [[3]] #> torch_tensor #>  7 #>  8 #>  9 #> [ CPULongType{3} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":null,"dir":"Reference","previous_headings":"","what":"Unique_consecutive — torch_unique_consecutive","title":"Unique_consecutive — torch_unique_consecutive","text":"Unique_consecutive","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unique_consecutive — torch_unique_consecutive","text":"","code":"torch_unique_consecutive(   self,   return_inverse = FALSE,   return_counts = FALSE,   dim = NULL )"},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unique_consecutive — torch_unique_consecutive","text":"self (Tensor) input tensor return_inverse (bool) Whether also return indices        elements original input ended returned unique list. return_counts (bool) Whether also return counts unique        element. dim (int) dimension apply unique. NULL, unique        flattened input returned. default: NULL","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"test-","dir":"Reference","previous_headings":"","what":"TEST","title":"Unique_consecutive — torch_unique_consecutive","text":"Eliminates first element every consecutive group equivalent elements.","code":".. note:: This function is different from [`torch_unique`] in the sense that this function     only eliminates consecutive duplicate values. This semantics is similar to `std::unique`     in C++."},{"path":"https://torch.mlverse.org/docs/reference/torch_unique_consecutive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unique_consecutive — torch_unique_consecutive","text":"","code":"if (torch_is_installed()) { x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2)) output = torch_unique_consecutive(x) output torch_unique_consecutive(x, return_inverse=TRUE) torch_unique_consecutive(x, return_counts=TRUE) } #> [[1]] #> torch_tensor #>  1 #>  2 #>  3 #>  1 #>  2 #> [ CPUFloatType{5} ] #>  #> [[2]] #> torch_tensor #> [ CPULongType{0} ] #>  #> [[3]] #> torch_tensor #>  2 #>  2 #>  1 #>  2 #>  1 #> [ CPULongType{5} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsafe_chunk — torch_unsafe_chunk","title":"Unsafe_chunk — torch_unsafe_chunk","text":"Unsafe_chunk","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsafe_chunk — torch_unsafe_chunk","text":"","code":"torch_unsafe_chunk(self, chunks, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsafe_chunk — torch_unsafe_chunk","text":"self (Tensor) tensor split chunks (int) number chunks return dim (int) dimension along split tensor","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"unsafe-chunk-input-chunks-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"unsafe_chunk(input, chunks, dim=0) -> List of Tensors","title":"Unsafe_chunk — torch_unsafe_chunk","text":"Works like torch_chunk() without enforcing autograd restrictions inplace modification outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_chunk.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unsafe_chunk — torch_unsafe_chunk","text":"function safe use long input, outputs modified inplace calling function. user's responsibility ensure case. input one outputs modified inplace, gradients computed autograd silently incorrect.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsafe_split — torch_unsafe_split","title":"Unsafe_split — torch_unsafe_split","text":"Unsafe_split","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsafe_split — torch_unsafe_split","text":"","code":"torch_unsafe_split(self, split_size, dim = 1L)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsafe_split — torch_unsafe_split","text":"self (Tensor) tensor split. split_size (int) size single chunk list sizes chunk dim (int) dimension along split tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"unsafe-split-tensor-split-size-or-sections-dim-gt-list-of-tensors-","dir":"Reference","previous_headings":"","what":"unsafe_split(tensor, split_size_or_sections, dim=0) -> List of Tensors","title":"Unsafe_split — torch_unsafe_split","text":"Works like torch_split() without enforcing autograd restrictions inplace modification outputs.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsafe_split.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Unsafe_split — torch_unsafe_split","text":"function safe use long input, outputs modified inplace calling function. user's responsibility ensure case. input one outputs modified inplace, gradients computed autograd silently incorrect.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":null,"dir":"Reference","previous_headings":"","what":"Unsqueeze — torch_unsqueeze","title":"Unsqueeze — torch_unsqueeze","text":"Unsqueeze","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unsqueeze — torch_unsqueeze","text":"","code":"torch_unsqueeze(self, dim)"},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unsqueeze — torch_unsqueeze","text":"self (Tensor) input tensor. dim (int) index insert singleton dimension","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"unsqueeze-input-dim-gt-tensor-","dir":"Reference","previous_headings":"","what":"unsqueeze(input, dim) -> Tensor","title":"Unsqueeze — torch_unsqueeze","text":"Returns new tensor dimension size one inserted specified position. returned tensor shares underlying data tensor. dim value within range [-input.dim() - 1, input.dim() + 1) can used. Negative dim correspond unsqueeze applied dim = dim + input.dim() + 1.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_unsqueeze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unsqueeze — torch_unsqueeze","text":"","code":"if (torch_is_installed()) {  x = torch_tensor(c(1, 2, 3, 4)) torch_unsqueeze(x, 1) torch_unsqueeze(x, 2) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #> [ CPUFloatType{4,1} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":null,"dir":"Reference","previous_headings":"","what":"Vander — torch_vander","title":"Vander — torch_vander","text":"Vander","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vander — torch_vander","text":"","code":"torch_vander(x, N = NULL, increasing = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vander — torch_vander","text":"x (Tensor) 1-D input tensor. N (int, optional) Number columns output. N specified, square array returned \\((N = len(x))\\). increasing (bool, optional) Order powers columns. TRUE, powers increase left right, FALSE (default) reversed.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"vander-x-n-none-increasing-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"vander(x, N=None, increasing=FALSE) -> Tensor","title":"Vander — torch_vander","text":"Generates Vandermonde matrix. columns output matrix elementwise powers input vector \\(x^{(N-1)}, x^{(N-2)}, ..., x^0\\). increasing TRUE, order columns reversed \\(x^0, x^1, ..., x^{(N-1)}\\). matrix geometric progression row named Alexandre-Theophile Vandermonde.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vander.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vander — torch_vander","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(c(1, 2, 3, 5)) torch_vander(x) torch_vander(x, N=3) torch_vander(x, N=3, increasing=TRUE) } #> torch_tensor #>   1   1   1 #>   1   2   4 #>   1   3   9 #>   1   5  25 #> [ CPUFloatType{4,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Var — torch_var","title":"Var — torch_var","text":"Var","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Var — torch_var","text":"","code":"torch_var(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Var — torch_var","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"var-input-unbiased-true-gt-tensor-","dir":"Reference","previous_headings":"","what":"var(input, unbiased=TRUE) -> Tensor","title":"Var — torch_var","text":"Returns variance elements input tensor. unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-","dir":"Reference","previous_headings":"","what":"var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -> Tensor","title":"Var — torch_var","text":"Returns variance row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Var — torch_var","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_var(a)   a = torch_randn(c(4, 4)) a torch_var(a, 1) } #> torch_tensor #>  0.7107 #>  0.7116 #>  1.3322 #>  1.6740 #> [ CPUFloatType{4} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Var_mean — torch_var_mean","title":"Var_mean — torch_var_mean","text":"Var_mean","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Var_mean — torch_var_mean","text":"","code":"torch_var_mean(self, dim, unbiased = TRUE, keepdim = FALSE)"},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Var_mean — torch_var_mean","text":"self (Tensor) input tensor. dim (int tuple ints) dimension dimensions reduce. unbiased (bool) whether use unbiased estimation keepdim (bool) whether output tensor dim retained .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"var-mean-input-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"var_mean(input, unbiased=TRUE) -> (Tensor, Tensor)","title":"Var_mean — torch_var_mean","text":"Returns variance mean elements input tensor. unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-","dir":"Reference","previous_headings":"","what":"var_mean(input, dim, keepdim=False, unbiased=TRUE) -> (Tensor, Tensor)","title":"Var_mean — torch_var_mean","text":"Returns variance mean row input tensor given dimension dim. keepdim TRUE, output tensor size input except dimension(s) dim size 1. Otherwise, dim squeezed (see torch_squeeze), resulting output tensor 1 (len(dim)) fewer dimension(s). unbiased FALSE, variance calculated via biased estimator. Otherwise, Bessel's correction used.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_var_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Var_mean — torch_var_mean","text":"","code":"if (torch_is_installed()) {  a = torch_randn(c(1, 3)) a torch_var_mean(a)   a = torch_randn(c(4, 4)) a torch_var_mean(a, 1) } #> [[1]] #> torch_tensor #>  1.1897 #>  1.4186 #>  1.0193 #>  1.1092 #> [ CPUFloatType{4} ] #>  #> [[2]] #> torch_tensor #>  0.0664 #> -0.1636 #>  0.6071 #>  1.1277 #> [ CPUFloatType{4} ] #>"},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":null,"dir":"Reference","previous_headings":"","what":"Vdot — torch_vdot","title":"Vdot — torch_vdot","text":"Vdot","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vdot — torch_vdot","text":"","code":"torch_vdot(self, other)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vdot — torch_vdot","text":"self (Tensor) first tensor dot product. conjugate used complex. (Tensor) second tensor dot product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Vdot — torch_vdot","text":"function broadcast .","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"vdot-input-other-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"vdot(input, other, *, out=None) -> Tensor","title":"Vdot — torch_vdot","text":"Computes dot product (inner product) two tensors. vdot(, b) function handles complex numbers differently dot(, b). first argument complex, complex conjugate first argument used calculation dot product.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vdot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vdot — torch_vdot","text":"","code":"if (torch_is_installed()) {  torch_vdot(torch_tensor(c(2, 3)), torch_tensor(c(2, 1))) if (FALSE) { a <- torch_tensor(list(1 +2i, 3 - 1i)) b <- torch_tensor(list(2 +1i, 4 - 0i)) torch_vdot(a, b) torch_vdot(b, a) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":null,"dir":"Reference","previous_headings":"","what":"View_as_complex — torch_view_as_complex","title":"View_as_complex — torch_view_as_complex","text":"View_as_complex","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View_as_complex — torch_view_as_complex","text":"","code":"torch_view_as_complex(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View_as_complex — torch_view_as_complex","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"view-as-complex-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"view_as_complex(input) -> Tensor","title":"View_as_complex — torch_view_as_complex","text":"Returns view input complex tensor. input complex tensor size \\(m1, m2, \\dots, mi, 2\\), function returns new complex tensor size \\(m1, m2, \\dots, mi\\) last dimension input tensor expected represent real imaginary components complex numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"View_as_complex — torch_view_as_complex","text":"torch_view_as_complex supported tensors torch_dtype torch_float64() torch_float32().  input expected last dimension size 2. addition, tensor must stride 1 last dimension. strides dimensions must even numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_complex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View_as_complex — torch_view_as_complex","text":"","code":"if (torch_is_installed()) { if (FALSE) { x=torch_randn(c(4, 2)) x torch_view_as_complex(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":null,"dir":"Reference","previous_headings":"","what":"View_as_real — torch_view_as_real","title":"View_as_real — torch_view_as_real","text":"View_as_real","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"View_as_real — torch_view_as_real","text":"","code":"torch_view_as_real(self)"},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"View_as_real — torch_view_as_real","text":"self (Tensor) input tensor.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"view-as-real-input-gt-tensor-","dir":"Reference","previous_headings":"","what":"view_as_real(input) -> Tensor","title":"View_as_real — torch_view_as_real","text":"Returns view input real tensor. input complex tensor size \\(m1, m2, \\dots, mi\\), function returns new real tensor size \\(m1, m2, \\dots, mi, 2\\), last dimension size 2 represents real imaginary components complex numbers.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"View_as_real — torch_view_as_real","text":"torch_view_as_real() supported tensors complex dtypes.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_view_as_real.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"View_as_real — torch_view_as_real","text":"","code":"if (torch_is_installed()) {  if (FALSE) { x <- torch_randn(4, dtype=torch_cfloat()) x torch_view_as_real(x) } }"},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":null,"dir":"Reference","previous_headings":"","what":"Vstack — torch_vstack","title":"Vstack — torch_vstack","text":"Vstack","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vstack — torch_vstack","text":"","code":"torch_vstack(tensors)"},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vstack — torch_vstack","text":"tensors (sequence Tensors) sequence tensors concatenate","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"vstack-tensors-out-none-gt-tensor-","dir":"Reference","previous_headings":"","what":"vstack(tensors, *, out=None) -> Tensor","title":"Vstack — torch_vstack","text":"Stack tensors sequence vertically (row wise). equivalent concatenation along first axis 1-D tensors reshaped torch_atleast_2d().","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_vstack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vstack — torch_vstack","text":"","code":"if (torch_is_installed()) {  a <- torch_tensor(c(1, 2, 3)) b <- torch_tensor(c(4, 5, 6)) torch_vstack(list(a,b)) a <- torch_tensor(rbind(1,2,3)) b <- torch_tensor(rbind(4,5,6)) torch_vstack(list(a,b)) } #> torch_tensor #>  1 #>  2 #>  3 #>  4 #>  5 #>  6 #> [ CPUFloatType{6,1} ]"},{"path":[]},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Where — torch_where","text":"","code":"torch_where(condition, self = NULL, other = NULL)"},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Where — torch_where","text":"condition (BoolTensor) TRUE (nonzero), yield x, otherwise yield y self (Tensor) values selected indices condition TRUE (Tensor) values selected indices condition FALSE","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Where — torch_where","text":"See also torch_nonzero().","code":"The tensors `condition`, `x`, `y` must be broadcastable ."},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"where-condition-x-y-gt-tensor-","dir":"Reference","previous_headings":"","what":"where(condition, x, y) -> Tensor","title":"Where — torch_where","text":"Return tensor elements selected either x y, depending condition. operation defined : $$     \\mbox{}_i = \\left\\{ \\begin{array}{ll}         \\mbox{x}_i & \\mbox{} \\mbox{condition}_i \\\\         \\mbox{y}_i & \\mbox{otherwise} \\\\     \\end{array}     \\right. $$","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"where-condition-gt-tuple-of-longtensor-","dir":"Reference","previous_headings":"","what":"where(condition) -> tuple of LongTensor","title":"Where — torch_where","text":"torch_where(condition) identical torch_nonzero(condition, as_tuple=TRUE).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_where.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Where — torch_where","text":"","code":"if (torch_is_installed()) {  if (FALSE) { # \\dontrun{ x = torch_randn(c(3, 2)) y = torch_ones(c(3, 2)) x torch_where(x > 0, x, y) } # }    }"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros — torch_zeros","title":"Zeros — torch_zeros","text":"Zeros","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros — torch_zeros","text":"","code":"torch_zeros(   ...,   names = NULL,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE )"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros — torch_zeros","text":"... sequence integers defining shape output tensor.        Can variable number arguments collection like list tuple. names optional dimension names dtype (torch.dtype, optional) desired data type returned tensor.        Default: NULL, uses global default (see torch_set_default_tensor_type). layout (torch.layout, optional) desired layout returned Tensor.        Default: torch_strided. device (torch.device, optional) desired device returned tensor.        Default: NULL, uses current device default tensor type        (see torch_set_default_tensor_type). device CPU        CPU tensor types current CUDA device CUDA tensor types. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-","dir":"Reference","previous_headings":"","what":"zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor","title":"Zeros — torch_zeros","text":"Returns tensor filled scalar value 0, shape defined variable argument size.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros — torch_zeros","text":"","code":"if (torch_is_installed()) {  torch_zeros(c(2, 3)) torch_zeros(c(5)) } #> torch_tensor #>  0 #>  0 #>  0 #>  0 #>  0 #> [ CPUFloatType{5} ]"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":null,"dir":"Reference","previous_headings":"","what":"Zeros_like — torch_zeros_like","title":"Zeros_like — torch_zeros_like","text":"Zeros_like","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zeros_like — torch_zeros_like","text":"","code":"torch_zeros_like(   input,   dtype = NULL,   layout = NULL,   device = NULL,   requires_grad = FALSE,   memory_format = torch_preserve_format() )"},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Zeros_like — torch_zeros_like","text":"input (Tensor) size input determine size output tensor. dtype (torch.dtype, optional) desired data type returned Tensor.        Default: NULL, defaults dtype input. layout (torch.layout, optional) desired layout returned tensor.        Default: NULL, defaults layout input. device (torch.device, optional) desired device returned tensor.        Default: NULL, defaults device input. requires_grad (bool, optional) autograd record operations        returned tensor. Default: FALSE. memory_format (torch.memory_format, optional) desired memory format        returned Tensor. Default: torch_preserve_format.","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-","dir":"Reference","previous_headings":"","what":"zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -> Tensor","title":"Zeros_like — torch_zeros_like","text":"Returns tensor filled scalar value 0, size input. torch_zeros_like(input) equivalent torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Zeros_like — torch_zeros_like","text":"0.4, function support keyword. alternative, old torch_zeros_like(input, =output) equivalent torch_zeros(input.size(), =output).","code":""},{"path":"https://torch.mlverse.org/docs/reference/torch_zeros_like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Zeros_like — torch_zeros_like","text":"","code":"if (torch_is_installed()) {  input = torch_empty(c(2, 3)) torch_zeros_like(input) } #> torch_tensor #>  0  0  0 #>  0  0  0 #> [ CPUFloatType{2,3} ]"},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":null,"dir":"Reference","previous_headings":"","what":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"two things:","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"","code":"with_detect_anomaly(code)"},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"code Code executed detect anomaly context.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"Running forward pass detection enabled allow backward pass print traceback forward operation created failing backward function. backward computation generate \"nan\" value raise error.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"warning","dir":"Reference","previous_headings":"","what":"Warning","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"mode enabled debugging different tests slow program execution.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_detect_anomaly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Context-manager that enable anomaly detection for the autograd engine. — with_detect_anomaly","text":"","code":"if (torch_is_installed()) { x <- torch_randn(2, requires_grad = TRUE) y <- torch_randn(1) b <- (x^y)$sum() y$add_(1)  try({   b$backward()    with_detect_anomaly({     b$backward()   }) }) } #> Error in (function (self, inputs, gradient, retain_graph, create_graph)  :  #>   one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). #> Exception raised from unpack at /Users/runner/work/libtorch-mac-m1/libtorch-mac-m1/pytorch/torch/csrc/autograd/saved_variable.cpp:187 (most recent call first): #> frame #0: std::__1::shared_ptr<c10::(anonymous namespace)::PyTorchStyleBacktrace> std::__1::make_shared[abi:ue170006]<c10::(anonymous namespace)::PyTorchStyleBacktrace, c10::SourceLocation&, void>(c10::SourceLocation&) + 121 (0x10df17639 in libc10.dylib) #> frame #1: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 54 (0x10df17776 in libc10.dylib) #> frame #2: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 149 (0x10df14035 in libc10.dylib) #> frame #3: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 1987 (0x124d1e6f3 in libtorch_cpu.dylib) #> frame #4: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 84 (0x123b951a4 in libtorch_cpu.dylib) #> frame #5: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 104 (0x124ce5108 in libtorch_cpu.dylib) #> frame #6: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&) + 3039 (0x124cddbdf in libtorch_cpu.dylib) #> frame #7: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 1140 (0x124cdc9d4 in libtorch_cpu.dylib) #> frame #8: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr<torch::autograd::GraphTask> const&, std::__1::shared_ptr<torch::autograd::Node>, torch::autograd::InputBuffer&&) + 415 (0x124ce455f in libtorch_cpu.dylib) #> frame #9: torch::autograd::Engine::execute(std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool, bool, std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge>> const&) + 1786 (0x124ce2dda in libtorch_cpu.dylib) #> frame #10: torch::autograd::run_backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool) + 982 (0x124cca186 in libtorch_cpu.dylib) #> frame #11: torch::autograd::backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::optional<bool>, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&) + 107 (0x124cc968b in libtorch_cpu.dylib) #> frame #12: torch::autograd::VariableHooks::_backward(at::Tensor const&, c10::ArrayRef<at::Tensor>, std::__1::optional<at::Tensor> const&, std::__1::optional<bool>, bool) const + 296 (0x124d23918 in libtorch_cpu.dylib) #> frame #13: at::Tensor::_backward(c10::ArrayRef<at::Tensor>, std::__1::optional<at::Tensor> const&, std::__1::optional<bool>, bool) const + 73 (0x120ce9419 in libtorch_cpu.dylib) #> frame #14: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 211 (0x111cea4c3 in liblantern.dylib) #> frame #15: std::__1::__function::__func<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_2, std::__1::allocator<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_2>, void ()>::operator()() + 54 (0x110573836 in torchpkg.so) #> frame #16: std::__1::packaged_task<void ()>::operator()() + 72 (0x110571908 in torchpkg.so) #> frame #17: EventLoop<void>::run() + 413 (0x11057175d in torchpkg.so) #> frame #18: void* std::__1::__thread_proxy[abi:v160006]<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, ThreadPool<void>::ThreadPool(int)::'lambda'()>>(void*) + 50 (0x1105714b2 in torchpkg.so) #> frame #19: _pthread_start + 125 (0x7ff80f7941d3 in libsystem_pthread.dylib) #> frame #20: thread_start + 15 (0x7ff80f78fbd3 in libsystem_pthread.dylib) #>"},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Enable grad — with_enable_grad","title":"Enable grad — with_enable_grad","text":"Context-manager enables gradient calculation. Enables gradient calculation, disabled via with_no_grad.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enable grad — with_enable_grad","text":"","code":"with_enable_grad(code)  local_enable_grad(.env = parent.frame())"},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enable grad — with_enable_grad","text":"code code executed gradient recording. .env environment use scoping.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enable grad — with_enable_grad","text":"context manager thread local; affect computation threads.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Enable grad — with_enable_grad","text":"local_enable_grad(): Locally enable gradient computations.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_enable_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Enable grad — with_enable_grad","text":"","code":"if (torch_is_installed()) {  x <- torch_tensor(1, requires_grad = TRUE) with_no_grad({   with_enable_grad({     y <- x * 2   }) }) y$backward() x$grad } #> torch_tensor #>  2 #> [ CPUFloatType{1} ]"},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":null,"dir":"Reference","previous_headings":"","what":"Temporarily modify gradient recording. — with_no_grad","title":"Temporarily modify gradient recording. — with_no_grad","text":"Temporarily modify gradient recording.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Temporarily modify gradient recording. — with_no_grad","text":"","code":"with_no_grad(code)  local_no_grad(.env = parent.frame())"},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Temporarily modify gradient recording. — with_no_grad","text":"code code executed gradient recording. .env environment use scoping.","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Temporarily modify gradient recording. — with_no_grad","text":"local_no_grad(): Disable autograd goes scope","code":""},{"path":"https://torch.mlverse.org/docs/reference/with_no_grad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Temporarily modify gradient recording. — with_no_grad","text":"","code":"if (torch_is_installed()) { x <- torch_tensor(runif(5), requires_grad = TRUE) with_no_grad({   x$sub_(torch_tensor(as.numeric(1:5))) }) x x$grad } #> torch_tensor #> [ Tensor (undefined) ]"},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0140","dir":"Changelog","previous_headings":"","what":"torch 0.14.0","title":"torch 0.14.0","text":"CRAN release: 2025-01-30","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-14-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.14.0","text":"Updated LibTorch v2.5.1 (#1204) – potentially breaking change!","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-14-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.14.0","text":"Feature: Faster optimizers (optim_ignite_<name>()) available: Adam, AdamW, Adagrad, RMSprop,SGD. can used drop-replacements optim_<name> considerably faster wrap LibTorch implementation optimizer. biggest speed differences can observed complex optimizers AdamW.","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-14-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.14.0","text":"torch_iinfo() now support integer dtypes (#1190 @cregouby) Fixed float key_padding_mask nnf_multi_head_attention_forward() (#1205) Fix french translation (#1176 @cregouby) Trace jitted modules now respect ‘train’ ‘eval’ mode (#1211) Fix: Avoid name clashes multiple calls jit_trace (#1246)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0130","dir":"Changelog","previous_headings":"","what":"torch 0.13.0","title":"torch 0.13.0","text":"CRAN release: 2024-05-21","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-13-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.13.0","text":"lantern now distributed different URL (https://torch-cdn.mlverse.org). users shouldn’t effect, unless need special authorization access URL’s. (#1162)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-13-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.13.0","text":"Added support private $finalize_deep_clone() method nn_module allows run code cloning module. compare_proxy method torch_tensor type added allows compare torch tensors using testthat::expect_equal(). Converting torch tensor R array works tensor ‘cuda’ device (#1130)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-13-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.13.0","text":"Fix bug using input projection initialization bias nnf_multi_head_attention_forward (#1154 @cregouby) Bugfix: calling $detach() tensor now preserves attributes (#1136) Make sure deep cloning tensor nn_module preserves class attributes requires_grad field. (#1129) Fixed parameters buffers children nn_modules cloned Cloned objects longer reference object cloned Fixed bug nn_module’s patched clone method invalid call internal create_nn_module_callable() Printing grad_fn now appends new line end. Make sure deep cloning preserve state dict attributes. (#1129) Added separate setter unsetter autocast context instead allowing local_autocast(). (#1142) Fixed bug torch_arange() causing return 1:(n-1) values specific request dtype = torch_int64() (#1160)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0120","dir":"Changelog","previous_headings":"","what":"torch 0.12.0","title":"torch 0.12.0","text":"CRAN release: 2023-12-15","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-12-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.12.0","text":"New torch_save serialization format. ’s ~10x faster since ’s based safetensors, files can read safetensors implementation. (#1071) Updated LibTorch 2.0.1. (#1085) torch_load longer supports device=NULL load weights device saved. (#1085) Lantern binaries torch pre-built binaries now built Ubuntu 20.04. (#1124)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-12-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.12.0","text":"Added support CUDA 11.8. (#1089) Added support iterable datasets. (#1095)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-12-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.12.0","text":"fix printer torch device (add new line end) .array now moves tensors cpu copying data R. (#1080) Fixed segfault caused comparing dtype NULL. (#1090) Fixed incorrect naming complex data type names, torch_cfloat64. (#1091) Fixed name out_features attribute nn_linear module. (#1097) Fixed issues loading state dict optimizers learning rate schedulers. (#1100) Fixed bug cloning nn_modules empty state dicts. (#1108) distr_multivariate_normal now correctly handles precision matrix’s. (#1110) Moved length.torch_tensor implementation R7 avoid problems torch dataset torch_tensor class. (#1111) Fixed problem deep cloning nn_module. (#1123)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0110","dir":"Changelog","previous_headings":"","what":"torch 0.11.0","title":"torch 0.11.0","text":"CRAN release: 2023-06-06","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-11-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.11.0","text":"load_state_dict() optimizers now default cloning tensors state dict, don’t keep references objects dict. (#1041)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-11-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.11.0","text":"Added nn_utils_weight_norm (#1025) Added support reading ordered state dicts serialized PyTorch. (#1031) Added jit_ops allowing access JIT operators. (#1023) Added with_device local_device allow temporarily modify default device tensors get initialized. (#1034) nnf_gelu() nn_gelu() gained approximate argument. (#1043) Implemented != torch devices. (#1042) Allows setting dtype string. (#1045) can now create named list modules using nn_module_dict(). (#1046) Faster load_state_dict(), also using less memory. ’ possible use legacy implementation required, see PR. (#1051) Export helpers handling RNG state, temprarily modifying . (#1057) Added support converting half tensors R .numeric(). (#1056) Added new torch_tensor_from_buffer() buffer_from_torch_tensor() allow low level creation torch tensors. (#1061, #1062)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-11-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.11.0","text":"Improved documentation LBFGS optimizer. (#1035) Added message asking user restart session manual installation install_torch(). (#1055)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-11-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.11.0","text":"Fixed bug related handling non-persistent buffers. get added state_dict() even . (#1036) Fixed typo optim_adamw class name. Fixed nn_cross_entropy_loss class name. (#1043) Fixed bug LBFGS w/ line search. (#1048) Correctly handle installation RemoteSha package version. (#1058)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-11-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.11.0","text":"Started building LibLantern macOS 11 instead macOS12 maximum compatibility. (#1026) Added CXXSTD Makevars enable C+11 compilation options. Refactored codepath TensorOptions now tensors initialization handled codepath. (#1033) Added internal argument .refer_to_state_dict load_state_dict() nn_module() method. Allows loading state dict model keeping parmaters references state dict. (#1036)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-0100","dir":"Changelog","previous_headings":"","what":"torch 0.10.0","title":"torch 0.10.0","text":"CRAN release: 2023-04-13","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-10-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.10.0","text":"Updated LibTorch v1.13.1 (#977)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-10-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.10.0","text":"Provide pre-built binaries torch using GH Action workflow. (#975) Added nn_silu() nnf_silu(). (#985) Added support deep cloning nn_modules. (#986) Added local_no_grad() local_enable_grad() alternatives with_ functions. (#990) Added optim_adamw optimizer. (#991) Added support automatic mixed precision (#996) Added functionality temporarily modify torch seed. (#999) Support creating torch tensors raw vectors back. (#1003)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-10-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.10.0","text":"Dataloaders now preserve batch dimension batch_size=1 used. (#994)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-10-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.10.0","text":"Large refactoring build system. (#964) Use native symbol registration instead dynamic lookup. (#976) Returning lists tensors R now much faster. 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(#950, @skeydan)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-9-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.9.1","text":"Fixed bug caused distr_normal$sample() able generate reproducible results setting seeds. (#938) torch_cat error message now correctly reflects 1-based indexing. (#952, @skeydan)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-9-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.9.1","text":"Fixed warnings R CMD Check generated unsafe use sprintf. (#959, @shikokuchuo) Import, suggest glue (#960)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-090","dir":"Changelog","previous_headings":"","what":"torch 0.9.0","title":"torch 0.9.0","text":"CRAN release: 2022-10-24","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-9-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.9.0","text":"Updated LibTorch v1.12.1. (#889, #893, #899) torch_bincount now 1-based indexed. (#896) torch_movedim() $movedim() now 1-based indexed. (#905)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-9-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.9.0","text":"Added cuda_synchronize() allow synchronization CUDA operations. (#887) Added support M1 Macs, including creating Tensors MPS device. (#890) Added support CUDA 11.6 Linux. (#902) Added cuda_empty_cache() allow freeing memory caching allocator system. (#903) Added $is_sparse() method check wether Tensor sparse . (#903) dataset_subset now adds class modified dataset original dataset classes postfixed _subset. (#904) Added torch_serialize() allow creating raw vector torch objects. (#908)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-9-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.9.0","text":"Fixed bug torch_arange causing end value getting included result. (#885, @skeydan) Fixed bug window functions setting default dtype. (#886, @skeydan) Fixed bug using install_torch(reinstall = TRUE). (#883) dims argument torch_tile() longer modified, ’s meant 1-based dimension. (#905) nn_module$state_diict() now detaches output tensors default. (#916)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-9-0","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.9.0","text":"Re-implemented $ method R7 classes C/C++ improve speed calling methods. (#873) Re-implemented garbage collection logic calling inside backward() call. improves speed longer need call GC everytime backward called. (#873) now use thread pool instead launching new thread backward calls. (#883) Implemented options allow configuring activation garbage collection allocating CUDA memory. (#883) nnf_ functions updated use single torch_ kernel instead custom implementation. (#896) Improved performance dataloaders. (#900) now let LibTorch query default generator, allows one use torch_bernoulli() device=\"gpu\". (#906)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-081","dir":"Changelog","previous_headings":"","what":"torch 0.8.1","title":"torch 0.8.1","text":"CRAN release: 2022-08-19","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-8-1","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.8.1","text":"now prompt user installing torch additional dependencies interactive environments. requested CRAN maintainers. (#864)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-8-1","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.8.1","text":"Dataloaders can now handle logical values. (#858, @ryan-heslin) now provide builds Pre CXX11 ABI version LibTorch. can used setting environment variable PRECXX11ABI=1. can useful environments older versions GLIBC. (#870)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-8-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.8.1","text":"Fixed way errors passed dataloaders workers main process. Now using new rlang error chaining. (#864)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-0-8-1","dir":"Changelog","previous_headings":"","what":"Internal","title":"torch 0.8.1","text":"can now call GC even backward call (ie, different thread) allows better memory management. (#853) Fix HTML5 Manual information resquested CRAN (#869)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-080","dir":"Changelog","previous_headings":"","what":"torch 0.8.0","title":"torch 0.8.0","text":"CRAN release: 2022-06-09","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.8.0","text":"Serialization now much faster avoid base64 encoding serialized tensors. result, files serialized newer versions torch can’t opened older versions torch. Set options(torch.serialization_version = 1) want file readable older versions. (#803) Deprecated support CUDA 10.2 Windows. (#835) linalg_matrix_rank linalg_pinv gained atol rtol arguments deprecating tol rcond. 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(#832) sampler() now exported allowing create custom samplers can passed dataloader(). (#833) Creating nn_modules without initialize method now supported. (#834) Added lr_reduce_on_plateau learning rate scheduler. (#836, @egillax) torch_tensor(NULL) longer fails. now returns tensor dimensions data. (#839) Improved complex numbers handling, including better printing support casting R. (#844)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.8.0","text":"Fixed bug weight decay handling Adam optimizer. (#824, @egillax) Fixed bug nn_l1_loss. (#825, @sebffischer)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"documentation-0-8-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"torch 0.8.0","text":"Nice error message embed_dim divisible num_heads nn_multihead_attention. 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(#770)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-7-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.7.0","text":"jit_save_for_mobile allows save traced model bytecode form, loaded LiteModuleLoader. (#713) Exported is_torch_tensor check wether object tensor . (#730, @rdinnager) Adds cuda_get_device_properties(device) allows one query device capability properties. (#734, @rdinnager) Implemented call_torch_function() allow calling potentially unexported torch core functions. (#743, @rdinnager) Now installing torch LibTorch Lantern headers installed within inst directory. allow packages extending torch bind directly C++ library. (#718) dataset_subset use .getbatch method wrapped dataset one available. (#742, @egillax) Added nn_flatten nn_unflatten modules. (#773) Added cuda_memory_stats() cuda_memory_summary() verify amount memory torch using GPU. 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(#723).","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-060","dir":"Changelog","previous_headings":"","what":"torch 0.6.0","title":"torch 0.6.0","text":"CRAN release: 2021-10-07","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.6.0","text":"nn_sequential now bare nn_module, allowing easily inherit . breaking change used name argument. name behavior can achieved subclassing; see tests PR. (#699)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-6-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.6.0","text":"Additional info showed printing tensors like requires grad grad fn. (#668, #669, #673, @mohamed-180) can now subset nn_sequential modules using [. (#678, @mohamed-180) now allow padding='' padding='valid' using convolutions. 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(#698)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-050","dir":"Changelog","previous_headings":"","what":"torch 0.5.0","title":"torch 0.5.0","text":"CRAN release: 2021-08-17","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.5.0","text":"Droped support CUDA 10.1 (#610) torch_manual_seed() now matches PyTorch’s behavior can easily compare implementations. Since breaking change added torch.old_seed_behavior=TRUE option users can stick old behavior. (#639) Indexing vectors now behavior R indexing, making easier understand. Users can still use old behavior using torch_index torch_index_put. (#649)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-5-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.5.0","text":"Added support ScriptModule. Loaded JIT modules now operate nn_modules. (#593) Added jit_compile function allows compiling arbitrary TorchScript code script function can serialized executed. (#601) Added jit_trace support nn_module created R. (#604) Updated LibTorch version 1.9.0 (#610) Added Linear Algebra functions (#612) Added contrib_sort_vertices efficiently sort vertices CUDA. (#619) Allows querying graph traced modules. (#623) Added with_detect_anomaly debug autograd errors. (#628) Implemented traced_module$graph_for() allow inspecting optimized jit graph. (#643) Added slc allow dynamically creating slices indexing tensors. (#648)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.5.0","text":"Fixed bug using .getbatch method didn’t return torch_tensor. (#615) Fixed warning using %/% caused call deprecated torch_floor_divide (#616) Improved CUDA version auto-detection (#644)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-5-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.5.0","text":"Improved R <-> JIT types conversion. (#593) Added Dockerfiles CUDA 11.1 (#597) warning raised incompatible dataset passed parallel dataloader. (#626) Additionally calling gc CUDA memory exhausted now call R_RunPendingFinalizers. improve memory usage, now delete tensors earlier. (#654) Fix rchk issues (#667)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-040","dir":"Changelog","previous_headings":"","what":"torch 0.4.0","title":"torch 0.4.0","text":"CRAN release: 2021-06-10","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.4.0","text":"torch_multinomial now returns 1-based indexes comply 1-based indexing across torch. (#588)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-4-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.4.0","text":"Added parameter multihead attention module allow output unaveraged attention weights. (@jonathanbratt #542) now allow jit_trace functions 1 argument. (#544) Added Multivariate normal distribution (#552) Export torch_diff function added docs . (#565) Added device argument torch_load() allowing one select device parameters loaded. (#578) Added distr_categorical() (#576) Added distr_mixture_same_family() (#576) Improve handling optimizers state implement load_state_dict() state_dict() optimizers. (#585) Added ability save R lists containing torch_tensors using torch_save. allows us save state optimizers modules using torch_save(). (#586)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-4-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.4.0","text":"Fixed bug nn_multihead_attention q,k,v inputs . (@jonathanbratt #540) Fixed $copy_ correctly respects src requires_grad() reloading saved models torch_load(). (#545) Fixed nn_init_xavier_normal_() nn_init_xavier_uniform_() standard deviation calculation. (#557) Fixed bug torch_tensordot() called infering dimensions. (#563) Dataset’s .getbatch now takes integer vector input instead list(). (#572) Fixed bug tensor$size() indexing negative numbers. (#570) Fixed bug log_prob distr_bernoulli() (#581)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"internal-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"torch 0.4.0","text":"Better handling optional Tensor arguments using explicit XPtrTorchOptionalTensor class. (#565) Tensors R side point C++ Tensor now guaranteed object. allows easily determine unique model parameters. (#582)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"torch-030","dir":"Changelog","previous_headings":"","what":"torch 0.3.0","title":"torch 0.3.0","text":"CRAN release: 2021-04-28","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"torch 0.3.0","text":"torch_nonzero tensor$nonzero() now return 1-based indexes. (#432) Breaking change: torch_arange returns closed interval [start, end] instead half open [start, end). makes behave similar R’s seq. (#506)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"torch 0.3.0","text":"torch_split now accepts list sizes well fixed size. (#429) Added nn_layer_norm. (#435) Allow timeout=360 install_torch() parameter large file download (@cregouby #438) Added install_torch_from_file() get_install_libs_url()setup cases direct download possible (@cregouby #439) Added mean.torch_tensor (#448) New arguments worker_globals worker_packages allowing easily pass objects workers parallel dataloaders (#449). now call R garbage collector ’s memory available GPU, can help cases laziness garbage collector allows many tensors memory even though longer referenced R. (#456) Implemented nn_group_norm fixed bug nnf_group_norm (#474) Added backend functions allowing us query optimizations LibTorch compiled (#476) Added normal distribution (#462) Added bernoulli distribution (#484) .list nn_modules (#492) Enumerate support Bernoulli distribution (#490) Added Poisson Distriibution (#495) Allow optional .getbatch datasets/dataloaders (#498) nn_lstm, nn_gru nn_gru can now use cudnn accelerations available (#503). Added Gamma distribution (#489) now respect TORCH_HOME env var automatically install torch. (#522) Implement comparison operator != torch dtypes. (#524) Added Chi-square distribution. (#518) Added optimizer function allowing easily implement custom optimizers. (#527)","code":""},{"path":"https://torch.mlverse.org/docs/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"torch 0.3.0","text":"Fixed bug optim_lbfgs make model objects exponentially big. (#431) Correctly handle NaNs L-BFGS optimizer (#433) default collate function now respects data type converting tensor (dataset returns R object) (#434) Fixed torch_normal. (#450) Fixed backward compatibility issue loading models saved older versions torch. bug introduced #452 now fixed also added regression test. (#458) Fixed bug using RNN’s GPU (#460) Found fixed memory leaks, specially creating datatypes strings saving models torch_save. (#454) Fixed bug nnf_pad using mode='circular'. (#471) Bugfixes nn_multihead_attention (#496) Fixed bug using packed sequences nn_lstm (#500) Fixed bug method nn_module reset requires_grad attribute parameters. (#501) Added strong_wolfe option optim_lbfgs. (#517) Fixed default argument nn_init_trunc_normal_ initializer function. 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