From e4d362e199581d89b84ec5b424f39699b567d3cf Mon Sep 17 00:00:00 2001
From: Sriparno Roy <sriparnoroy01@gmail.com>
Date: Tue, 17 Sep 2024 19:55:57 +0530
Subject: [PATCH 1/3] [Term Entry] PyTorch Tensors: .mm()

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 .../pytorch/concepts/tensors/terms/mm/mm.md   | 81 +++++++++++++++++++
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diff --git a/content/pytorch/concepts/tensors/terms/mm/mm.md b/content/pytorch/concepts/tensors/terms/mm/mm.md
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+---
+Title: '.mm()'
+Description: 'Calculates the matrix product of two given tensors.'
+Subjects:
+  - 'AI'
+  - 'Data Science'
+Tags:
+  - 'AI'
+  - 'Arrays'
+  - 'Data Structures'
+  - 'Deep Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/computer-science'
+---
+
+In PyTorch, the **`.mm()`** method calculates the matrix product of two given tensors.
+
+## Syntax
+
+```pseudo
+torch.mm(ten1, ten2, *, out=None)
+```
+
+- `ten1`: The first tensor to be multiplied.
+- `ten2`: The second tensor to be multiplied.
+- `out` (Optional): The output tensor to be used. The default value is `None`.
+
+If `ten1` is a `(m x n)` tensor and `ten2` is a `(n x p)` tensor, then `out` will be a `(m x p)` tensor.
+
+## Example
+
+The following example demonstrates the usage of the `.mm()` method:
+
+```py
+import torch
+
+# Define two tensors
+ten1 = torch.tensor([[1, 2, 3],
+                     [4, 3, 8],
+                     [1, 7, 2]])
+
+ten2 = torch.tensor([[2, 4, 1],
+                     [1, 3, 6],
+                     [2, 6, 5]])
+
+# Multiply the tensors
+out = torch.mm(ten1, ten2)
+
+print(out)
+```
+
+The above code produces the following output:
+
+```shell
+tensor([[10, 28, 28],
+        [27, 73, 62],
+        [13, 37, 53]])
+```
+
+## Codebyte Example
+
+The following codebyte example shows the use of the `.mm()` method:
+
+```codebyte/python
+import torch
+
+# Define two tensors
+ten1 = torch.tensor([[6, 8, 1],
+                     [5, 2, 4],
+                     [9, 3, 7]])
+
+ten2 = torch.tensor([[9, 2, 3],
+                     [7, 8, 4],
+                     [6, 1, 5]])
+
+# Multiply the tensors
+out = torch.mm(ten1, ten2)
+
+print(out)
+```

From 5d101d9cff3ba9a7d89be440c002250fd7e524aa Mon Sep 17 00:00:00 2001
From: Sriparno Roy <sriparnoroy01@gmail.com>
Date: Wed, 25 Sep 2024 13:23:10 +0530
Subject: [PATCH 2/3] Update path

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diff --git a/content/pytorch/concepts/tensors/terms/mm/mm.md b/content/pytorch/concepts/tensor-operations/terms/mm/mm.md
similarity index 100%
rename from content/pytorch/concepts/tensors/terms/mm/mm.md
rename to content/pytorch/concepts/tensor-operations/terms/mm/mm.md

From 7ce3efb827d0bdc784d1fd540dd50368c264a7dd Mon Sep 17 00:00:00 2001
From: Sriparno Roy <sriparnoroy01@gmail.com>
Date: Wed, 9 Oct 2024 15:21:57 +0530
Subject: [PATCH 3/3] Update mm.md

---
 content/pytorch/concepts/tensor-operations/terms/mm/mm.md | 2 +-
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diff --git a/content/pytorch/concepts/tensor-operations/terms/mm/mm.md b/content/pytorch/concepts/tensor-operations/terms/mm/mm.md
index cd2b46471b3..d29aff253c8 100644
--- a/content/pytorch/concepts/tensor-operations/terms/mm/mm.md
+++ b/content/pytorch/concepts/tensor-operations/terms/mm/mm.md
@@ -26,7 +26,7 @@ torch.mm(ten1, ten2, *, out=None)
 - `ten2`: The second tensor to be multiplied.
 - `out` (Optional): The output tensor to be used. The default value is `None`.
 
-If `ten1` is a `(m x n)` tensor and `ten2` is a `(n x p)` tensor, then `out` will be a `(m x p)` tensor.
+> **Note:** If `ten1` is a `(m x n)` tensor and `ten2` is a `(n x p)` tensor, then `out` will be a `(m x p)` tensor.
 
 ## Example